Three-dimensional fluorescent microscopy via simultaneous illumination and detection at multiple planes.

PubMed

Ma, Qian; Khademhosseinieh, Bahar; Huang, Eric; Qian, Haoliang; Bakowski, Malina A; Troemel, Emily R; Liu, Zhaowei

2016-08-16

The conventional optical microscope is an inherently two-dimensional (2D) imaging tool. The objective lens, eyepiece and image sensor are all designed to capture light emitted from a 2D 'object plane'. Existing technologies, such as confocal or light sheet fluorescence microscopy have to utilize mechanical scanning, a time-multiplexing process, to capture a 3D image. In this paper, we present a 3D optical microscopy method based upon simultaneously illuminating and detecting multiple focal planes. This is implemented by adding two diffractive optical elements to modify the illumination and detection optics. We demonstrate that the image quality of this technique is comparable to conventional light sheet fluorescent microscopy with the advantage of the simultaneous imaging of multiple axial planes and reduced number of scans required to image the whole sample volume.

Broad attention to multiple individual objects may facilitate change detection with complex auditory scenes.

PubMed

Irsik, Vanessa C; Vanden Bosch der Nederlanden, Christina M; Snyder, Joel S

2016-11-01

Attention and other processing constraints limit the perception of objects in complex scenes, which has been studied extensively in the visual sense. We used a change deafness paradigm to examine how attention to particular objects helps and hurts the ability to notice changes within complex auditory scenes. In a counterbalanced design, we examined how cueing attention to particular objects affected performance in an auditory change-detection task through the use of valid or invalid cues and trials without cues (Experiment 1). We further examined how successful encoding predicted change-detection performance using an object-encoding task and we addressed whether performing the object-encoding task along with the change-detection task affected performance overall (Experiment 2). Participants had more error for invalid compared to valid and uncued trials, but this effect was reduced in Experiment 2 compared to Experiment 1. When the object-encoding task was present, listeners who completed the uncued condition first had less overall error than those who completed the cued condition first. All participants showed less change deafness when they successfully encoded change-relevant compared to irrelevant objects during valid and uncued trials. However, only participants who completed the uncued condition first also showed this effect during invalid cue trials, suggesting a broader scope of attention. These findings provide converging evidence that attention to change-relevant objects is crucial for successful detection of acoustic changes and that encouraging broad attention to multiple objects is the best way to reduce change deafness. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Radar based autonomous sensor module

NASA Astrophysics Data System (ADS)

Styles, Tim

2016-10-01

Most surveillance systems combine camera sensors with other detection sensors that trigger an alert to a human operator when an object is detected. The detection sensors typically require careful installation and configuration for each application and there is a significant burden on the operator to react to each alert by viewing camera video feeds. A demonstration system known as Sensing for Asset Protection with Integrated Electronic Networked Technology (SAPIENT) has been developed to address these issues using Autonomous Sensor Modules (ASM) and a central High Level Decision Making Module (HLDMM) that can fuse the detections from multiple sensors. This paper describes the 24 GHz radar based ASM, which provides an all-weather, low power and license exempt solution to the problem of wide area surveillance. The radar module autonomously configures itself in response to tasks provided by the HLDMM, steering the transmit beam and setting range resolution and power levels for optimum performance. The results show the detection and classification performance for pedestrians and vehicles in an area of interest, which can be modified by the HLDMM without physical adjustment. The module uses range-Doppler processing for reliable detection of moving objects and combines Radar Cross Section and micro-Doppler characteristics for object classification. Objects are classified as pedestrian or vehicle, with vehicle sub classes based on size. Detections are reported only if the object is detected in a task coverage area and it is classified as an object of interest. The system was shown in a perimeter protection scenario using multiple radar ASMs, laser scanners, thermal cameras and visible band cameras. This combination of sensors enabled the HLDMM to generate reliable alerts with improved discrimination of objects and behaviours of interest.

Focusing attention on objects of interest using multiple matched filters.

PubMed

Stough, T M; Brodley, C E

2001-01-01

In order to be of use to scientists, large image databases need to be analyzed to create a catalog of the objects of interest. One approach is to apply a multiple tiered search algorithm that uses reduction techniques of increasing computational complexity to select the desired objects from the database. The first tier of this type of algorithm, often called a focus of attention (FOA) algorithm, selects candidate regions from the image data and passes them to the next tier of the algorithm. In this paper we present a new approach to FOA that employs multiple matched filters (MMF), one for each object prototype, to detect the regions of interest. The MMFs are formed using k-means clustering on a set of image patches identified by domain experts as positive examples of objects of interest. An innovation of the approach is to radically reduce the dimensionality of the feature space, used by the k-means algorithm, by taking block averages (spoiling) the sample image patches. The process of spoiling is analyzed and its applicability to other domains is discussed. The combination of the output of the MMFs is achieved through the projection of the detections back into an empty image and then thresholding. This research was motivated by the need to detect small volcanos in the Magellan probe data from Venus. An empirical evaluation of the approach illustrates that a combination of the MMF plus the average filter results in a higher likelihood of 100% detection of the objects of interest at a lower false positive rate than a single matched filter alone.

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

Accelerated SPECT Monte Carlo Simulation Using Multiple Projection Sampling and Convolution-Based Forced Detection

NASA Astrophysics Data System (ADS)

Liu, Shaoying; King, Michael A.; Brill, Aaron B.; Stabin, Michael G.; Farncombe, Troy H.

2008-02-01

Monte Carlo (MC) is a well-utilized tool for simulating photon transport in single photon emission computed tomography (SPECT) due to its ability to accurately model physical processes of photon transport. As a consequence of this accuracy, it suffers from a relatively low detection efficiency and long computation time. One technique used to improve the speed of MC modeling is the effective and well-established variance reduction technique (VRT) known as forced detection (FD). With this method, photons are followed as they traverse the object under study but are then forced to travel in the direction of the detector surface, whereby they are detected at a single detector location. Another method, called convolution-based forced detection (CFD), is based on the fundamental idea of FD with the exception that detected photons are detected at multiple detector locations and determined with a distance-dependent blurring kernel. In order to further increase the speed of MC, a method named multiple projection convolution-based forced detection (MP-CFD) is presented. Rather than forcing photons to hit a single detector, the MP-CFD method follows the photon transport through the object but then, at each scatter site, forces the photon to interact with a number of detectors at a variety of angles surrounding the object. This way, it is possible to simulate all the projection images of a SPECT simulation in parallel, rather than as independent projections. The result of this is vastly improved simulation time as much of the computation load of simulating photon transport through the object is done only once for all projection angles. The results of the proposed MP-CFD method agrees well with the experimental data in measurements of point spread function (PSF), producing a correlation coefficient (r2) of 0.99 compared to experimental data. The speed of MP-CFD is shown to be about 60 times faster than a regular forced detection MC program with similar results.

Modified-hybrid optical neural network filter for multiple object recognition within cluttered scenes

NASA Astrophysics Data System (ADS)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.

2009-08-01

Motivated by the non-linear interpolation and generalization abilities of the hybrid optical neural network filter between the reference and non-reference images of the true-class object we designed the modifiedhybrid optical neural network filter. We applied an optical mask to the hybrid optical neural network's filter input. The mask was built with the constant weight connections of a randomly chosen image included in the training set. The resulted design of the modified-hybrid optical neural network filter is optimized for performing best in cluttered scenes of the true-class object. Due to the shift invariance properties inherited by its correlator unit the filter can accommodate multiple objects of the same class to be detected within an input cluttered image. Additionally, the architecture of the neural network unit of the general hybrid optical neural network filter allows the recognition of multiple objects of different classes within the input cluttered image by modifying the output layer of the unit. We test the modified-hybrid optical neural network filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. The filter is shown to exhibit with a single pass over the input data simultaneously out-of-plane rotation, shift invariance and good clutter tolerance. It is able to successfully detect and classify correctly the true-class objects within background clutter for which there has been no previous training.

Object tracking using multiple camera video streams

NASA Astrophysics Data System (ADS)

Mehrubeoglu, Mehrube; Rojas, Diego; McLauchlan, Lifford

2010-05-01

Two synchronized cameras are utilized to obtain independent video streams to detect moving objects from two different viewing angles. The video frames are directly correlated in time. Moving objects in image frames from the two cameras are identified and tagged for tracking. One advantage of such a system involves overcoming effects of occlusions that could result in an object in partial or full view in one camera, when the same object is fully visible in another camera. Object registration is achieved by determining the location of common features in the moving object across simultaneous frames. Perspective differences are adjusted. Combining information from images from multiple cameras increases robustness of the tracking process. Motion tracking is achieved by determining anomalies caused by the objects' movement across frames in time in each and the combined video information. The path of each object is determined heuristically. Accuracy of detection is dependent on the speed of the object as well as variations in direction of motion. Fast cameras increase accuracy but limit the speed and complexity of the algorithm. Such an imaging system has applications in traffic analysis, surveillance and security, as well as object modeling from multi-view images. The system can easily be expanded by increasing the number of cameras such that there is an overlap between the scenes from at least two cameras in proximity. An object can then be tracked long distances or across multiple cameras continuously, applicable, for example, in wireless sensor networks for surveillance or navigation.

Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras.

PubMed

Jung, Jaehoon; Yoon, Inhye; Lee, Seungwon; Paik, Joonki

2016-06-24

Since it is impossible for surveillance personnel to keep monitoring videos from a multiple camera-based surveillance system, an efficient technique is needed to help recognize important situations by retrieving the metadata of an object-of-interest. In a multiple camera-based surveillance system, an object detected in a camera has a different shape in another camera, which is a critical issue of wide-range, real-time surveillance systems. In order to address the problem, this paper presents an object retrieval method by extracting the normalized metadata of an object-of-interest from multiple, heterogeneous cameras. The proposed metadata generation algorithm consists of three steps: (i) generation of a three-dimensional (3D) human model; (ii) human object-based automatic scene calibration; and (iii) metadata generation. More specifically, an appropriately-generated 3D human model provides the foot-to-head direction information that is used as the input of the automatic calibration of each camera. The normalized object information is used to retrieve an object-of-interest in a wide-range, multiple-camera surveillance system in the form of metadata. Experimental results show that the 3D human model matches the ground truth, and automatic calibration-based normalization of metadata enables a successful retrieval and tracking of a human object in the multiple-camera video surveillance system.

Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras

PubMed Central

Jung, Jaehoon; Yoon, Inhye; Lee, Seungwon; Paik, Joonki

2016-01-01

Since it is impossible for surveillance personnel to keep monitoring videos from a multiple camera-based surveillance system, an efficient technique is needed to help recognize important situations by retrieving the metadata of an object-of-interest. In a multiple camera-based surveillance system, an object detected in a camera has a different shape in another camera, which is a critical issue of wide-range, real-time surveillance systems. In order to address the problem, this paper presents an object retrieval method by extracting the normalized metadata of an object-of-interest from multiple, heterogeneous cameras. The proposed metadata generation algorithm consists of three steps: (i) generation of a three-dimensional (3D) human model; (ii) human object-based automatic scene calibration; and (iii) metadata generation. More specifically, an appropriately-generated 3D human model provides the foot-to-head direction information that is used as the input of the automatic calibration of each camera. The normalized object information is used to retrieve an object-of-interest in a wide-range, multiple-camera surveillance system in the form of metadata. Experimental results show that the 3D human model matches the ground truth, and automatic calibration-based normalization of metadata enables a successful retrieval and tracking of a human object in the multiple-camera video surveillance system. PMID:27347961

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection.

PubMed

Ouyang, Wanli; Zeng, Xingyu; Wang, Xiaogang; Qiu, Shi; Luo, Ping; Tian, Yonglong; Li, Hongsheng; Yang, Shuo; Wang, Zhe; Li, Hongyang; Loy, Chen Change; Wang, Kun; Yan, Junjie; Tang, Xiaoou

2016-07-07

In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging. The proposed approach improves the mean averaged precision obtained by RCNN [16], which was the state-of-the-art, from 31% to 50.3% on the ILSVRC2014 detection test set. It also outperforms the winner of ILSVRC2014, GoogLeNet, by 6.1%. Detailed component-wise analysis is also provided through extensive experimental evaluation, which provides a global view for people to understand the deep learning object detection pipeline.

Discrimination of complex synthetic echoes by an echolocating bottlenose dolphin

NASA Astrophysics Data System (ADS)

Helweg, David A.; Moore, Patrick W.; Dankiewicz, Lois A.; Zafran, Justine M.; Brill, Randall L.

2003-02-01

Bottlenose dolphins (Tursiops truncatus) detect and discriminate underwater objects by interrogating the environment with their native echolocation capabilities. Study of dolphins' ability to detect complex (multihighlight) signals in noise suggest echolocation object detection using an approximate 265-μs energy integration time window sensitive to the echo region of highest energy or containing the highlight with highest energy. Backscatter from many real objects contains multiple highlights, distributed over multiple integration windows and with varying amplitude relationships. This study used synthetic echoes with complex highlight structures to test whether high-amplitude initial highlights would interfere with discrimination of low-amplitude trailing highlights. A dolphin was trained to discriminate two-highlight synthetic echoes using differences in the center frequencies of the second highlights. The energy ratio (ΔdB) and the timing relationship (ΔT) between the first and second highlights were manipulated. An iso-sensitivity function was derived using a factorial design testing ΔdB at -10, -15, -20, and -25 dB and ΔT at 10, 20, 40, and 80 μs. The results suggest that the animal processed multiple echo highlights as separable analyzable features in the discrimination task, perhaps perceived through differences in spectral rippling across the duration of the echoes.

3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging

NASA Astrophysics Data System (ADS)

Aloni, Doron; Jung, Jae-Hyun; Yitzhaky, Yitzhak

2017-10-01

Three dimensional (3D) object segmentation and tracking can be useful in various computer vision applications, such as: object surveillance for security uses, robot navigation, etc. We present a method for 3D multiple-object tracking using computational integral imaging, based on accurate 3D object segmentation. The method does not employ object detection by motion analysis in a video as conventionally performed (such as background subtraction or block matching). This means that the movement properties do not significantly affect the detection quality. The object detection is performed by analyzing static 3D image data obtained through computational integral imaging With regard to previous works that used integral imaging data in such a scenario, the proposed method performs the 3D tracking of objects without prior information about the objects in the scene, and it is found efficient under severe noise conditions.

Multiple source associated particle imaging for simultaneous capture of multiple projections

DOEpatents

Bingham, Philip R; Hausladen, Paul A; McConchi, Seth M; Mihalczo, John T; Mullens, James A

2013-11-19

Disclosed herein are representative embodiments of methods, apparatus, and systems for performing neutron radiography. For example, in one exemplary method, an object is interrogated with a plurality of neutrons. The plurality of neutrons includes a first portion of neutrons generated from a first neutron source and a second portion of neutrons generated from a second neutron source. Further, at least some of the first portion and the second portion are generated during a same time period. In the exemplary method, one or more neutrons from the first portion and one or more neutrons from the second portion are detected, and an image of the object is generated based at least in part on the detected neutrons from the first portion and the detected neutrons from the second portion.

A Real-Time Method to Estimate Speed of Object Based on Object Detection and Optical Flow Calculation

NASA Astrophysics Data System (ADS)

Liu, Kaizhan; Ye, Yunming; Li, Xutao; Li, Yan

2018-04-01

In recent years Convolutional Neural Network (CNN) has been widely used in computer vision field and makes great progress in lots of contents like object detection and classification. Even so, combining Convolutional Neural Network, which means making multiple CNN frameworks working synchronously and sharing their output information, could figure out useful message that each of them cannot provide singly. Here we introduce a method to real-time estimate speed of object by combining two CNN: YOLOv2 and FlowNet. In every frame, YOLOv2 provides object size; object location and object type while FlowNet providing the optical flow of whole image. On one hand, object size and object location help to select out the object part of optical flow image thus calculating out the average optical flow of every object. On the other hand, object type and object size help to figure out the relationship between optical flow and true speed by means of optics theory and priori knowledge. Therefore, with these two key information, speed of object can be estimated. This method manages to estimate multiple objects at real-time speed by only using a normal camera even in moving status, whose error is acceptable in most application fields like manless driving or robot vision.

Tracking Object Existence From an Autonomous Patrol Vehicle

NASA Technical Reports Server (NTRS)

Wolf, Michael; Scharenbroich, Lucas

2011-01-01

An autonomous vehicle patrols a large region, during which an algorithm receives measurements of detected potential objects within its sensor range. The goal of the algorithm is to track all objects in the region over time. This problem differs from traditional multi-target tracking scenarios because the region of interest is much larger than the sensor range and relies on the movement of the sensor through this region for coverage. The goal is to know whether anything has changed between visits to the same location. In particular, two kinds of alert conditions must be detected: (1) a previously detected object has disappeared and (2) a new object has appeared in a location already checked. For the time an object is within sensor range, the object can be assumed to remain stationary, changing position only between visits. The problem is difficult because the upstream object detection processing is likely to make many errors, resulting in heavy clutter (false positives) and missed detections (false negatives), and because only noisy, bearings-only measurements are available. This work has three main goals: (1) Associate incoming measurements with known objects or mark them as new objects or false positives, as appropriate. For this, a multiple hypothesis tracker was adapted to this scenario. (2) Localize the objects using multiple bearings-only measurements to provide estimates of global position (e.g., latitude and longitude). A nonlinear Kalman filter extension provides these 2D position estimates using the 1D measurements. (3) Calculate the probability that a suspected object truly exists (in the estimated position), and determine whether alert conditions have been triggered (for new objects or disappeared objects). The concept of a probability of existence was created, and a new Bayesian method for updating this probability at each time step was developed. A probabilistic multiple hypothesis approach is chosen because of its superiority in handling the uncertainty arising from errors in sensors and upstream processes. However, traditional target tracking methods typically assume a stationary detection volume of interest, whereas in this case, one must make adjustments for being able to see only a small portion of the region of interest and understand when an alert situation has occurred. To track object existence inside and outside the vehicle's sensor range, a probability of existence was defined for each hypothesized object, and this value was updated at every time step in a Bayesian manner based on expected characteristics of the sensor and object and whether that object has been detected in the most recent time step. Then, this value feeds into a sequential probability ratio test (SPRT) to determine the status of the object (suspected, confirmed, or deleted). Alerts are sent upon selected status transitions. Additionally, in order to track objects that move in and out of sensor range and update the probability of existence appropriately a variable probability detection has been defined and the hypothesis probability equations have been re-derived to accommodate this change. Unsupervised object tracking is a pervasive issue in automated perception systems. This work could apply to any mobile platform (ground vehicle, sea vessel, air vehicle, or orbiter) that intermittently revisits regions of interest and needs to determine whether anything interesting has changed.

Vision-based algorithms for near-host object detection and multilane sensing

NASA Astrophysics Data System (ADS)

Kenue, Surender K.

1995-01-01

Vision-based sensing can be used for lane sensing, adaptive cruise control, collision warning, and driver performance monitoring functions of intelligent vehicles. Current computer vision algorithms are not robust for handling multiple vehicles in highway scenarios. Several new algorithms are proposed for multi-lane sensing, near-host object detection, vehicle cut-in situations, and specifying regions of interest for object tracking. These algorithms were tested successfully on more than 6000 images taken from real-highway scenes under different daytime lighting conditions.

Detecting and Analyzing Multiple Moving Objects in Crowded Environments with Coherent Motion Regions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheriyadat, Anil M.

Understanding the world around us from large-scale video data requires vision systems that can perform automatic interpretation. While human eyes can unconsciously perceive independent objects in crowded scenes and other challenging operating environments, automated systems have difficulty detecting, counting, and understanding their behavior in similar scenes. Computer scientists at ORNL have a developed a technology termed as "Coherent Motion Region Detection" that invloves identifying multiple indepedent moving objects in crowded scenes by aggregating low-level motion cues extracted from moving objects. Humans and other species exploit such low-level motion cues seamlessely to perform perceptual grouping for visual understanding. The algorithm detectsmore » and tracks feature points on moving objects resulting in partial trajectories that span coherent 3D region in the space-time volume defined by the video. In the case of multi-object motion, many possible coherent motion regions can be constructed around the set of trajectories. The unique approach in the algorithm is to identify all possible coherent motion regions, then extract a subset of motion regions based on an innovative measure to automatically locate moving objects in crowded environments.The software reports snapshot of the object, count, and derived statistics ( count over time) from input video streams. The software can directly process videos streamed over the internet or directly from a hardware device (camera).« less

Multiple targets detection method in detection of UWB through-wall radar

NASA Astrophysics Data System (ADS)

Yang, Xiuwei; Yang, Chuanfa; Zhao, Xingwen; Tian, Xianzhong

2017-11-01

In this paper, the problems and difficulties encountered in the detection of multiple moving targets by UWB radar are analyzed. The experimental environment and the penetrating radar system are established. An adaptive threshold method based on local area is proposed to effectively filter out clutter interference The objective of the moving target is analyzed, and the false target is further filtered out by extracting the target feature. Based on the correlation between the targets, the target matching algorithm is proposed to improve the detection accuracy. Finally, the effectiveness of the above method is verified by practical experiment.

A Point Kinetics Model for Estimating Neutron Multiplication of Bare Uranium Metal in Tagged Neutron Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tweardy, Matthew C.; McConchie, Seth; Hayward, Jason P.

An extension of the point kinetics model is developed in this paper to describe the neutron multiplicity response of a bare uranium object under interrogation by an associated particle imaging deuterium-tritium (D-T) measurement system. This extended model is used to estimate the total neutron multiplication of the uranium. Both MCNPX-PoliMi simulations and data from active interrogation measurements of highly enriched and depleted uranium geometries are used to evaluate the potential of this method and to identify the sources of systematic error. The detection efficiency correction for measured coincidence response is identified as a large source of systematic error. If themore » detection process is not considered, results suggest that the method can estimate total multiplication to within 13% of the simulated value. Values for multiplicity constants in the point kinetics equations are sensitive to enrichment due to (n, xn) interactions by D-T neutrons and can introduce another significant source of systematic bias. This can theoretically be corrected if isotopic composition is known a priori. Finally, the spatial dependence of multiplication is also suspected of introducing further systematic bias for high multiplication uranium objects.« less

A Point Kinetics Model for Estimating Neutron Multiplication of Bare Uranium Metal in Tagged Neutron Measurements

DOE PAGES

Tweardy, Matthew C.; McConchie, Seth; Hayward, Jason P.

2017-06-13

An extension of the point kinetics model is developed in this paper to describe the neutron multiplicity response of a bare uranium object under interrogation by an associated particle imaging deuterium-tritium (D-T) measurement system. This extended model is used to estimate the total neutron multiplication of the uranium. Both MCNPX-PoliMi simulations and data from active interrogation measurements of highly enriched and depleted uranium geometries are used to evaluate the potential of this method and to identify the sources of systematic error. The detection efficiency correction for measured coincidence response is identified as a large source of systematic error. If themore » detection process is not considered, results suggest that the method can estimate total multiplication to within 13% of the simulated value. Values for multiplicity constants in the point kinetics equations are sensitive to enrichment due to (n, xn) interactions by D-T neutrons and can introduce another significant source of systematic bias. This can theoretically be corrected if isotopic composition is known a priori. Finally, the spatial dependence of multiplication is also suspected of introducing further systematic bias for high multiplication uranium objects.« less

Object detection in natural backgrounds predicted by discrimination performance and models

NASA Technical Reports Server (NTRS)

Rohaly, A. M.; Ahumada, A. J. Jr; Watson, A. B.

1997-01-01

Many models of visual performance predict image discriminability, the visibility of the difference between a pair of images. We compared the ability of three image discrimination models to predict the detectability of objects embedded in natural backgrounds. The three models were: a multiple channel Cortex transform model with within-channel masking; a single channel contrast sensitivity filter model; and a digital image difference metric. Each model used a Minkowski distance metric (generalized vector magnitude) to summate absolute differences between the background and object plus background images. For each model, this summation was implemented with three different exponents: 2, 4 and infinity. In addition, each combination of model and summation exponent was implemented with and without a simple contrast gain factor. The model outputs were compared to measures of object detectability obtained from 19 observers. Among the models without the contrast gain factor, the multiple channel model with a summation exponent of 4 performed best, predicting the pattern of observer d's with an RMS error of 2.3 dB. The contrast gain factor improved the predictions of all three models for all three exponents. With the factor, the best exponent was 4 for all three models, and their prediction errors were near 1 dB. These results demonstrate that image discrimination models can predict the relative detectability of objects in natural scenes.

Enhancing Ground Based Telescope Performance with Image Processing

DTIC Science & Technology

2013-11-13

driven by the need to detect small faint objects with relatively short integration times to avoid streaking of the satellite image across multiple...the time right before the eclipse. The orbital elements of the satellite were entered into the SST’s tracking system, so that the SST could be...short integration times , thereby avoiding streaking of the satellite image across multiple CCD pixels so that the objects are suitably modeled as point

Acoustic-based Technology to Detect Buried Pipes

DOT National Transportation Integrated Search

2011-07-29

The objective of this project is to build a pre-commercial device, improve its performance to detect multiple buried pipes, and evaluate the pre-commercial device at utility sites. In the past, Gas Technology Institute (GTI) and SoniVerse Inc. (SVI) ...

Vision System for Coarsely Estimating Motion Parameters for Unknown Fast Moving Objects in Space

PubMed Central

Chen, Min; Hashimoto, Koichi

2017-01-01

Motivated by biological interests in analyzing navigation behaviors of flying animals, we attempt to build a system measuring their motion states. To do this, in this paper, we build a vision system to detect unknown fast moving objects within a given space, calculating their motion parameters represented by positions and poses. We proposed a novel method to detect reliable interest points from images of moving objects, which can be hardly detected by general purpose interest point detectors. 3D points reconstructed using these interest points are then grouped and maintained for detected objects, according to a careful schedule, considering appearance and perspective changes. In the estimation step, a method is introduced to adapt the robust estimation procedure used for dense point set to the case for sparse set, reducing the potential risk of greatly biased estimation. Experiments are conducted against real scenes, showing the capability of the system of detecting multiple unknown moving objects and estimating their positions and poses. PMID:29206189

Neural basis for dynamic updating of object representation in visual working memory.

PubMed

Takahama, Sachiko; Miyauchi, Satoru; Saiki, Jun

2010-02-15

In real world, objects have multiple features and change dynamically. Thus, object representations must satisfy dynamic updating and feature binding. Previous studies have investigated the neural activity of dynamic updating or feature binding alone, but not both simultaneously. We investigated the neural basis of feature-bound object representation in a dynamically updating situation by conducting a multiple object permanence tracking task, which required observers to simultaneously process both the maintenance and dynamic updating of feature-bound objects. Using an event-related design, we separated activities during memory maintenance and change detection. In the search for regions showing selective activation in dynamic updating of feature-bound objects, we identified a network during memory maintenance that was comprised of the inferior precentral sulcus, superior parietal lobule, and middle frontal gyrus. In the change detection period, various prefrontal regions, including the anterior prefrontal cortex, were activated. In updating object representation of dynamically moving objects, the inferior precentral sulcus closely cooperates with a so-called "frontoparietal network", and subregions of the frontoparietal network can be decomposed into those sensitive to spatial updating and feature binding. The anterior prefrontal cortex identifies changes in object representation by comparing memory and perceptual representations rather than maintaining object representations per se, as previously suggested. Copyright 2009 Elsevier Inc. All rights reserved.

MUSIC algorithms for rebar detection

NASA Astrophysics Data System (ADS)

Solimene, Raffaele; Leone, Giovanni; Dell'Aversano, Angela

2013-12-01

The MUSIC (MUltiple SIgnal Classification) algorithm is employed to detect and localize an unknown number of scattering objects which are small in size as compared to the wavelength. The ensemble of objects to be detected consists of both strong and weak scatterers. This represents a scattering environment challenging for detection purposes as strong scatterers tend to mask the weak ones. Consequently, the detection of more weakly scattering objects is not always guaranteed and can be completely impaired when the noise corrupting data is of a relatively high level. To overcome this drawback, here a new technique is proposed, starting from the idea of applying a two-stage MUSIC algorithm. In the first stage strong scatterers are detected. Then, information concerning their number and location is employed in the second stage focusing only on the weak scatterers. The role of an adequate scattering model is emphasized to improve drastically detection performance in realistic scenarios.

Logarithmic r-θ mapping for hybrid optical neural network filter for multiple objects recognition within cluttered scenes

NASA Astrophysics Data System (ADS)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.; Birch, Phil M.

2009-04-01

θThe window unit in the design of the complex logarithmic r-θ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-θ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-θ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.

PubMed

Oh, Sang-Il; Kang, Hang-Bong

2017-01-22

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

PubMed Central

Oh, Sang-Il; Kang, Hang-Bong

2017-01-01

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226×370 image, whereas the original selective search method extracted approximately 106×n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. PMID:28117742

Multiple-object tracking while driving: the multiple-vehicle tracking task.

PubMed

Lochner, Martin J; Trick, Lana M

2014-11-01

Many contend that driving an automobile involves multiple-object tracking. At this point, no one has tested this idea, and it is unclear how multiple-object tracking would coordinate with the other activities involved in driving. To address some of the initial and most basic questions about multiple-object tracking while driving, we modified the tracking task for use in a driving simulator, creating the multiple-vehicle tracking task. In Experiment 1, we employed a dual-task methodology to determine whether there was interference between tracking and driving. Findings suggest that although it is possible to track multiple vehicles while driving, driving reduces tracking performance, and tracking compromises headway and lane position maintenance while driving. Modified change-detection paradigms were used to assess whether there were change localization advantages for tracked targets in multiple-vehicle tracking. When changes occurred during a blanking interval, drivers were more accurate (Experiment 2a) and ~250 ms faster (Experiment 2b) at locating the vehicle that changed when it was a target rather than a distractor in tracking. In a more realistic driving task where drivers had to brake in response to the sudden onset of brake lights in one of the lead vehicles, drivers were more accurate at localizing the vehicle that braked if it was a tracking target, although there was no advantage in terms of braking response time. Overall, results suggest that multiple-object tracking is possible while driving and perhaps even advantageous in some situations, but further research is required to determine whether multiple-object tracking is actually used in day-to-day driving.

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

PubMed

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

A dual-process account of auditory change detection.

PubMed

McAnally, Ken I; Martin, Russell L; Eramudugolla, Ranmalee; Stuart, Geoffrey W; Irvine, Dexter R F; Mattingley, Jason B

2010-08-01

Listeners can be "deaf" to a substantial change in a scene comprising multiple auditory objects unless their attention has been directed to the changed object. It is unclear whether auditory change detection relies on identification of the objects in pre- and post-change scenes. We compared the rates at which listeners correctly identify changed objects with those predicted by change-detection models based on signal detection theory (SDT) and high-threshold theory (HTT). Detected changes were not identified as accurately as predicted by models based on either theory, suggesting that some changes are detected by a process that does not support change identification. Undetected changes were identified as accurately as predicted by the HTT model but much less accurately than predicted by the SDT models. The process underlying change detection was investigated further by determining receiver-operating characteristics (ROCs). ROCs did not conform to those predicted by either a SDT or a HTT model but were well modeled by a dual-process that incorporated HTT and SDT components. The dual-process model also accurately predicted the rates at which detected and undetected changes were correctly identified.

Detecting abandoned objects using interacting multiple models

NASA Astrophysics Data System (ADS)

Becker, Stefan; Münch, David; Kieritz, Hilke; Hübner, Wolfgang; Arens, Michael

2015-10-01

In recent years, the wide use of video surveillance systems has caused an enormous increase in the amount of data that has to be stored, monitored, and processed. As a consequence, it is crucial to support human operators with automated surveillance applications. Towards this end an intelligent video analysis module for real-time alerting in case of abandoned objects in public spaces is proposed. The overall processing pipeline consists of two major parts. First, person motion is modeled using an Interacting Multiple Model (IMM) filter. The IMM filter estimates the state of a person according to a finite-state, discrete-time Markov chain. Second, the location of persons that stay at a fixed position defines a region of interest, in which a nonparametric background model with dynamic per-pixel state variables identifies abandoned objects. In case of a detected abandoned object, an alarm event is triggered. The effectiveness of the proposed system is evaluated on the PETS 2006 dataset and the i-Lids dataset, both reflecting prototypical surveillance scenarios.

Sensor fusion V; Proceedings of the Meeting, Boston, MA, Nov. 15-17, 1992

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1992-01-01

Topics addressed include 3D object perception, human-machine interface in multisensor systems, sensor fusion architecture, fusion of multiple and distributed sensors, interface and decision models for sensor fusion, computational networks, simple sensing for complex action, multisensor-based control, and metrology and calibration of multisensor systems. Particular attention is given to controlling 3D objects by sketching 2D views, the graphical simulation and animation environment for flexible structure robots, designing robotic systems from sensorimotor modules, cylindrical object reconstruction from a sequence of images, an accurate estimation of surface properties by integrating information using Bayesian networks, an adaptive fusion model for a distributed detection system, multiple concurrent object descriptions in support of autonomous navigation, robot control with multiple sensors and heuristic knowledge, and optical array detectors for image sensors calibration. (No individual items are abstracted in this volume)

Vision-based object detection and recognition system for intelligent vehicles

NASA Astrophysics Data System (ADS)

Ran, Bin; Liu, Henry X.; Martono, Wilfung

1999-01-01

Recently, a proactive crash mitigation system is proposed to enhance the crash avoidance and survivability of the Intelligent Vehicles. Accurate object detection and recognition system is a prerequisite for a proactive crash mitigation system, as system component deployment algorithms rely on accurate hazard detection, recognition, and tracking information. In this paper, we present a vision-based approach to detect and recognize vehicles and traffic signs, obtain their information, and track multiple objects by using a sequence of color images taken from a moving vehicle. The entire system consist of two sub-systems, the vehicle detection and recognition sub-system and traffic sign detection and recognition sub-system. Both of the sub- systems consist of four models: object detection model, object recognition model, object information model, and object tracking model. In order to detect potential objects on the road, several features of the objects are investigated, which include symmetrical shape and aspect ratio of a vehicle and color and shape information of the signs. A two-layer neural network is trained to recognize different types of vehicles and a parameterized traffic sign model is established in the process of recognizing a sign. Tracking is accomplished by combining the analysis of single image frame with the analysis of consecutive image frames. The analysis of the single image frame is performed every ten full-size images. The information model will obtain the information related to the object, such as time to collision for the object vehicle and relative distance from the traffic sings. Experimental results demonstrated a robust and accurate system in real time object detection and recognition over thousands of image frames.

Color object detection using spatial-color joint probability functions.

PubMed

Luo, Jiebo; Crandall, David

2006-06-01

Object detection in unconstrained images is an important image understanding problem with many potential applications. There has been little success in creating a single algorithm that can detect arbitrary objects in unconstrained images; instead, algorithms typically must be customized for each specific object. Consequently, it typically requires a large number of exemplars (for rigid objects) or a large amount of human intuition (for nonrigid objects) to develop a robust algorithm. We present a robust algorithm designed to detect a class of compound color objects given a single model image. A compound color object is defined as having a set of multiple, particular colors arranged spatially in a particular way, including flags, logos, cartoon characters, people in uniforms, etc. Our approach is based on a particular type of spatial-color joint probability function called the color edge co-occurrence histogram. In addition, our algorithm employs perceptual color naming to handle color variation, and prescreening to limit the search scope (i.e., size and location) for the object. Experimental results demonstrated that the proposed algorithm is insensitive to object rotation, scaling, partial occlusion, and folding, outperforming a closely related algorithm based on color co-occurrence histograms by a decisive margin.

S-CNN: Subcategory-aware convolutional networks for object detection.

PubMed

Chen, Tao; Lu, Shijian; Fan, Jiayuan

2017-09-26

The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.

Deep Space Wide Area Search Strategies

NASA Astrophysics Data System (ADS)

Capps, M.; McCafferty, J.

There is an urgent need to expand the space situational awareness (SSA) mission beyond catalog maintenance to providing near real-time indications and warnings of emerging events. While building and maintaining a catalog of space objects is essential to SSA, this does not address the threat of uncatalogued and uncorrelated deep space objects. The Air Force therefore has an interest in transformative technologies to scan the geostationary (GEO) belt for uncorrelated space objects. Traditional ground based electro-optical sensors are challenged in simultaneously detecting dim objects while covering large areas of the sky using current CCD technology. Time delayed integration (TDI) scanning has the potential to enable significantly larger coverage rates while maintaining sensitivity for detecting near-GEO objects. This paper investigates strategies of employing TDI sensing technology from a ground based electro-optical telescope, toward providing tactical indications and warnings of deep space threats. We present results of a notional wide area search TDI sensor that scans the GEO belt from three locations: Maui, New Mexico, and Diego Garcia. Deep space objects in the NASA 2030 debris catalog are propagated over multiple nights as an indicative data set to emulate notional uncatalogued near-GEO orbits which may be encountered by the TDI sensor. Multiple scan patterns are designed and simulated, to compare and contrast performance based on 1) efficiency in coverage, 2) number of objects detected, and 3) rate at which detections occur, to enable follow-up observations by other space surveillance network (SSN) sensors. A step-stare approach is also modeled using a dedicated, co-located sensor notionally similar to the Ground-Based Electro-Optical Deep Space Surveillance (GEODSS) tower. Equivalent sensitivities are assumed. This analysis quantifies the relative benefit of TDI scanning for the wide area search mission.

Multilevel depth and image fusion for human activity detection.

PubMed

Ni, Bingbing; Pei, Yong; Moulin, Pierre; Yan, Shuicheng

2013-10-01

Recognizing complex human activities usually requires the detection and modeling of individual visual features and the interactions between them. Current methods only rely on the visual features extracted from 2-D images, and therefore often lead to unreliable salient visual feature detection and inaccurate modeling of the interaction context between individual features. In this paper, we show that these problems can be addressed by combining data from a conventional camera and a depth sensor (e.g., Microsoft Kinect). We propose a novel complex activity recognition and localization framework that effectively fuses information from both grayscale and depth image channels at multiple levels of the video processing pipeline. In the individual visual feature detection level, depth-based filters are applied to the detected human/object rectangles to remove false detections. In the next level of interaction modeling, 3-D spatial and temporal contexts among human subjects or objects are extracted by integrating information from both grayscale and depth images. Depth information is also utilized to distinguish different types of indoor scenes. Finally, a latent structural model is developed to integrate the information from multiple levels of video processing for an activity detection. Extensive experiments on two activity recognition benchmarks (one with depth information) and a challenging grayscale + depth human activity database that contains complex interactions between human-human, human-object, and human-surroundings demonstrate the effectiveness of the proposed multilevel grayscale + depth fusion scheme. Higher recognition and localization accuracies are obtained relative to the previous methods.

Multiple objects tracking in fluorescence microscopy.

PubMed

Kalaidzidis, Yannis

2009-01-01

Many processes in cell biology are connected to the movement of compact entities: intracellular vesicles and even single molecules. The tracking of individual objects is important for understanding cellular dynamics. Here we describe the tracking algorithms which have been developed in the non-biological fields and successfully applied to object detection and tracking in biological applications. The characteristics features of the different algorithms are compared.

Finding Kuiper Belt Objects Below the Detection Limit

NASA Astrophysics Data System (ADS)

Whidden, Peter; Kalmbach, Bryce; Bektesevic, Dino; Connolly, Andrew; Jones, Lynne; Smotherman, Hayden; Becker, Andrew

2018-01-01

We demonstrate a novel approach for uncovering the signatures of moving objects (e.g. Kuiper Belt Objects) below the detection thresholds of single astronomical images. To do so, we will employ a matched filter moving at specific rates of proposed orbits through a time-domain dataset. This is analogous to the better-known "shift-and-stack" method; however it uses neither direct shifting nor stacking of the image pixels. Instead of resampling the raw pixels to create an image stack, we will instead integrate the object detection probabilities across multiple single-epoch images to accrue support for a proposed orbit. The filtering kernel provides a measure of the probability that an object is present along a given orbit, and enables the user to make principled decisions about when the search has been successful, and when it may be terminated. The results we present here utilize GPUs to speed up the search by two orders of magnitudes over CPU implementations.

Object Acquisition and Tracking for Space-Based Surveillance

DTIC Science & Technology

1991-11-27

on multiple image frames, and, accordingly, requires a smaller signal to noise ratio. It is sometimes referred to as track before detect , and can...smaller sensor optics. Both the traditional and track before detect approaches are applicable to systems using scanning sensors, as well as those which use staring sensors.

A Benchmark Dataset and Saliency-guided Stacked Autoencoders for Video-based Salient Object Detection.

PubMed

Li, Jia; Xia, Changqun; Chen, Xiaowu

2017-10-12

Image-based salient object detection (SOD) has been extensively studied in past decades. However, video-based SOD is much less explored due to the lack of large-scale video datasets within which salient objects are unambiguously defined and annotated. Toward this end, this paper proposes a video-based SOD dataset that consists of 200 videos. In constructing the dataset, we manually annotate all objects and regions over 7,650 uniformly sampled keyframes and collect the eye-tracking data of 23 subjects who free-view all videos. From the user data, we find that salient objects in a video can be defined as objects that consistently pop-out throughout the video, and objects with such attributes can be unambiguously annotated by combining manually annotated object/region masks with eye-tracking data of multiple subjects. To the best of our knowledge, it is currently the largest dataset for videobased salient object detection. Based on this dataset, this paper proposes an unsupervised baseline approach for video-based SOD by using saliencyguided stacked autoencoders. In the proposed approach, multiple spatiotemporal saliency cues are first extracted at the pixel, superpixel and object levels. With these saliency cues, stacked autoencoders are constructed in an unsupervised manner that automatically infers a saliency score for each pixel by progressively encoding the high-dimensional saliency cues gathered from the pixel and its spatiotemporal neighbors. In experiments, the proposed unsupervised approach is compared with 31 state-of-the-art models on the proposed dataset and outperforms 30 of them, including 19 imagebased classic (unsupervised or non-deep learning) models, six image-based deep learning models, and five video-based unsupervised models. Moreover, benchmarking results show that the proposed dataset is very challenging and has the potential to boost the development of video-based SOD.

Disability and Fatigue Can Be Objectively Measured in Multiple Sclerosis

PubMed Central

Motta, Caterina; Palermo, Eduardo; Studer, Valeria; Germanotta, Marco; Germani, Giorgio; Centonze, Diego; Cappa, Paolo

2016-01-01

Background The available clinical outcome measures of disability in multiple sclerosis are not adequately responsive or sensitive. Objective To investigate the feasibility of inertial sensor-based gait analysis in multiple sclerosis. Methods A cross-sectional study of 80 multiple sclerosis patients and 50 healthy controls was performed. Lower-limb kinematics was evaluated by using a commercially available magnetic inertial measurement unit system. Mean and standard deviation of range of motion (mROM, sROM) for each joint of lower limbs were calculated in one minute walking test. A motor performance index (E) defined as the sum of sROMs was proposed. Results We established two novel observer-independent measures of disability. Hip mROM was extremely sensitive in measuring lower limb motor impairment, being correlated with muscle strength and also altered in patients without clinically detectable disability. On the other hand, E index discriminated patients according to disability, being altered only in patients with moderate and severe disability, regardless of walking speed. It was strongly correlated with fatigue and patient-perceived health status. Conclusions Inertial sensor-based gait analysis is feasible and can detect clinical and subclinical disability in multiple sclerosis. PMID:26863109

Fly Eye radar: detection through high scattered media

NASA Astrophysics Data System (ADS)

Molchanov, Pavlo; Gorwara, Ashok

2017-05-01

Longer radio frequency waves better penetrating through high scattered media than millimeter waves, but imaging resolution limited by diffraction at longer wavelength. Same time frequency and amplitudes of diffracted waves (frequency domain measurement) provides information of object. Phase shift of diffracted waves (phase front in time domain) consists information about shape of object and can be applied for reconstruction of object shape or even image by recording of multi-frequency digital hologram. Spectrum signature or refracted waves allows identify the object content. Application of monopulse method with overlap closely spaced antenna patterns provides high accuracy measurement of amplitude, phase, and direction to signal source. Digitizing of received signals separately in each antenna relative to processor time provides phase/frequency independence. Fly eye non-scanning multi-frequency radar system provides simultaneous continuous observation of multiple targets and wide possibilities for stepped frequency, simultaneous frequency, chaotic frequency sweeping waveform (CFS), polarization modulation for reliable object detection. Proposed c-band fly eye radar demonstrated human detection through 40 cm concrete brick wall with human and wall material spectrum signatures and can be applied for through wall human detection, landmines, improvised explosive devices detection, underground or camouflaged object imaging.

Nonstationary EO/IR Clutter Suppression and Dim Object Tracking

NASA Astrophysics Data System (ADS)

Tartakovsky, A.; Brown, A.; Brown, J.

2010-09-01

We develop and evaluate the performance of advanced algorithms which provide significantly improved capabilities for automated detection and tracking of ballistic and flying dim objects in the presence of highly structured intense clutter. Applications include ballistic missile early warning, midcourse tracking, trajectory prediction, and resident space object detection and tracking. The set of algorithms include, in particular, adaptive spatiotemporal clutter estimation-suppression and nonlinear filtering-based multiple-object track-before-detect. These algorithms are suitable for integration into geostationary, highly elliptical, or low earth orbit scanning or staring sensor suites, and are based on data-driven processing that adapts to real-world clutter backgrounds, including celestial, earth limb, or terrestrial clutter. In many scenarios of interest, e.g., for highly elliptic and, especially, low earth orbits, the resulting clutter is highly nonstationary, providing a significant challenge for clutter suppression to or below sensor noise levels, which is essential for dim object detection and tracking. We demonstrate the success of the developed algorithms using semi-synthetic and real data. In particular, our algorithms are shown to be capable of detecting and tracking point objects with signal-to-clutter levels down to 1/1000 and signal-to-noise levels down to 1/4.

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

PubMed Central

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

Combinatorial clustering and Its Application to 3D Polygonal Traffic Sign Reconstruction From Multiple Images

NASA Astrophysics Data System (ADS)

Vallet, B.; Soheilian, B.; Brédif, M.

2014-08-01

The 3D reconstruction of similar 3D objects detected in 2D faces a major issue when it comes to grouping the 2D detections into clusters to be used to reconstruct the individual 3D objects. Simple clustering heuristics fail as soon as similar objects are close. This paper formulates a framework to use the geometric quality of the reconstruction as a hint to do a proper clustering. We present a methodology to solve the resulting combinatorial optimization problem with some simplifications and approximations in order to make it tractable. The proposed method is applied to the reconstruction of 3D traffic signs from their 2D detections to demonstrate its capacity to solve ambiguities.

Apparatus and method to achieve high-resolution microscopy with non-diffracting or refracting radiation

DOEpatents

Tobin, Jr., Kenneth W.; Bingham, Philip R.; Hawari, Ayman I.

2012-11-06

An imaging system employing a coded aperture mask having multiple pinholes is provided. The coded aperture mask is placed at a radiation source to pass the radiation through. The radiation impinges on, and passes through an object, which alters the radiation by absorption and/or scattering. Upon passing through the object, the radiation is detected at a detector plane to form an encoded image, which includes information on the absorption and/or scattering caused by the material and structural attributes of the object. The encoded image is decoded to provide a reconstructed image of the object. Because the coded aperture mask includes multiple pinholes, the radiation intensity is greater than a comparable system employing a single pinhole, thereby enabling a higher resolution. Further, the decoding of the encoded image can be performed to generate multiple images of the object at different distances from the detector plane. Methods and programs for operating the imaging system are also disclosed.

Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection

PubMed Central

Wei, Pan; Anderson, Derek T.

2018-01-01

A significant challenge in object detection is accurate identification of an object’s position in image space, whereas one algorithm with one set of parameters is usually not enough, and the fusion of multiple algorithms and/or parameters can lead to more robust results. Herein, a new computational intelligence fusion approach based on the dynamic analysis of agreement among object detection outputs is proposed. Furthermore, we propose an online versus just in training image augmentation strategy. Experiments comparing the results both with and without fusion are presented. We demonstrate that the augmented and fused combination results are the best, with respect to higher accuracy rates and reduction of outlier influences. The approach is demonstrated in the context of cone, pedestrian and box detection for Advanced Driver Assistance Systems (ADAS) applications. PMID:29562609

Computer vision, camouflage breaking and countershading

PubMed Central

Tankus, Ariel; Yeshurun, Yehezkel

2008-01-01

Camouflage is frequently used in the animal kingdom in order to conceal oneself from visual detection or surveillance. Many camouflage techniques are based on masking the familiar contours and texture of the subject by superposition of multiple edges on top of it. This work presents an operator, Darg, for the detection of three-dimensional smooth convex (or, equivalently, concave) objects. It can be used to detect curved objects on a relatively flat background, regardless of image edges, contours and texture. We show that a typical camouflage found in some animal species seems to be a ‘countermeasure’ taken against detection that might be based on our method. Detection by Darg is shown to be very robust, from both theoretical considerations and practical examples of real-life images. PMID:18990669

Real-time people and vehicle detection from UAV imagery

NASA Astrophysics Data System (ADS)

Gaszczak, Anna; Breckon, Toby P.; Han, Jiwan

2011-01-01

A generic and robust approach for the real-time detection of people and vehicles from an Unmanned Aerial Vehicle (UAV) is an important goal within the framework of fully autonomous UAV deployment for aerial reconnaissance and surveillance. Here we present an approach for the automatic detection of vehicles based on using multiple trained cascaded Haar classifiers with secondary confirmation in thermal imagery. Additionally we present a related approach for people detection in thermal imagery based on a similar cascaded classification technique combining additional multivariate Gaussian shape matching. The results presented show the successful detection of vehicle and people under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. Performance of the detector is optimized to reduce the overall false positive rate by aiming at the detection of each object of interest (vehicle/person) at least once in the environment (i.e. per search patter flight path) rather than every object in each image frame. Currently the detection rate for people is ~70% and cars ~80% although the overall episodic object detection rate for each flight pattern exceeds 90%.

Tracking Algorithm of Multiple Pedestrians Based on Particle Filters in Video Sequences

PubMed Central

Liu, Yun; Wang, Chuanxu; Zhang, Shujun; Cui, Xuehong

2016-01-01

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in video sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge thus to achieve multipedestrian detection; it adopts color and texture features into particle filter to get better observation results and then automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking, the algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates detection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust tracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm improves the tracking performance and has better tracking results. PMID:27847514

An approach to detecting deliberately introduced defects and micro-defects in 3D printed objects

NASA Astrophysics Data System (ADS)

Straub, Jeremy

2017-05-01

In prior work, Zeltmann, et al. demonstrated the negative impact that can be created by defects of various sizes in 3D printed objects. These defects may make the object unsuitable for its application or even present a hazard, if the object is being used for a safety-critical application. With the uses of 3D printing proliferating and consumer access to printers increasing, the desire of a nefarious individual or group to subvert the desired printing quality and safety attributes of a printer or printed object must be considered. Several different approaches to subversion may exist. Attackers may physically impair the functionality of the printer or launch a cyber-attack. Detecting introduced defects, from either attack, is critical to maintaining public trust in 3D printed objects and the technology. This paper presents an alternate approach. It applies a quality assurance technology based on visible light sensing to this challenge and assesses its capability for detecting introduced defects of multiple sizes.

VIRUS ISOLATION AND MOLECULAR DETECTION OF BLUETONGUE AND EPIZOOTIC HEMORRHAGIC DISEASE VIRUSES FROM NATURALLY INFECTED WHITE-TAILED DEER (ODOCOILEUS VIRGINIANUS).

PubMed

Kienzle, Clara; Poulson, Rebecca L; Ruder, Mark G; Stallknecht, David E

2017-10-01

Hemorrhagic disease in North America is caused by multiple serotypes of epizootic hemorrhagic disease virus (EHDV) and bluetongue virus (BTV). Diagnostic tests for detection of EHDV and BTV include virus isolation (VI), reverse transcriptase (RT)-PCR, and real-time RT-PCR (rRT-PCR). Our objective was to compare the diagnostic capabilities of three rRT-PCR protocols for detection of EHDV and BTV from naturally infected white-tailed deer (Odocoileus virginianus). We compared the effectiveness of these assays to traditional viral detection methods (e.g., VI) for historic and current clinical cases. Because of the variable nature of tissue collection and storage before diagnostic testing, an evaluation of viral persistence on multiple freeze-thaw events was also conducted. Two of the rRT-PCR assays provided for reliable detection of EHDV and BTV from 100% of clinically affected and VI-confirmed infected animals. Additionally, no significant change in viral titer was observed on multiple freeze-thaw events.

Faint Object Detection in Multi-Epoch Observations via Catalog Data Fusion

NASA Astrophysics Data System (ADS)

Budavári, Tamás; Szalay, Alexander S.; Loredo, Thomas J.

2017-03-01

Astronomy in the time-domain era faces several new challenges. One of them is the efficient use of observations obtained at multiple epochs. The work presented here addresses faint object detection and describes an incremental strategy for separating real objects from artifacts in ongoing surveys. The idea is to produce low-threshold single-epoch catalogs and to accumulate information across epochs. This is in contrast to more conventional strategies based on co-added or stacked images. We adopt a Bayesian approach, addressing object detection by calculating the marginal likelihoods for hypotheses asserting that there is no object or one object in a small image patch containing at most one cataloged source at each epoch. The object-present hypothesis interprets the sources in a patch at different epochs as arising from a genuine object; the no-object hypothesis interprets candidate sources as spurious, arising from noise peaks. We study the detection probability for constant-flux objects in a Gaussian noise setting, comparing results based on single and stacked exposures to results based on a series of single-epoch catalog summaries. Our procedure amounts to generalized cross-matching: it is the product of a factor accounting for the matching of the estimated fluxes of the candidate sources and a factor accounting for the matching of their estimated directions. We find that probabilistic fusion of multi-epoch catalogs can detect sources with similar sensitivity and selectivity compared to stacking. The probabilistic cross-matching framework underlying our approach plays an important role in maintaining detection sensitivity and points toward generalizations that could accommodate variability and complex object structure.

Salient Object Detection via Structured Matrix Decomposition.

PubMed

Peng, Houwen; Li, Bing; Ling, Haibin; Hu, Weiming; Xiong, Weihua; Maybank, Stephen J

2016-05-04

Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank matrix representing image background and a sparse matrix identifying salient objects. Two deficiencies, however, still exist. First, previous work typically assumes the elements in the sparse matrix are mutually independent, ignoring the spatial and pattern relations of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To address these problems, we propose a novel structured matrix decomposition model with two structural regularizations: (1) a tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature space. Furthermore, high-level priors are integrated to guide the matrix decomposition and boost the detection. We evaluate our model for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics.

Drogue detection for vision-based autonomous aerial refueling via low rank and sparse decomposition with multiple features

NASA Astrophysics Data System (ADS)

Gao, Shibo; Cheng, Yongmei; Song, Chunhua

2013-09-01

The technology of vision-based probe-and-drogue autonomous aerial refueling is an amazing task in modern aviation for both manned and unmanned aircraft. A key issue is to determine the relative orientation and position of the drogue and the probe accurately for relative navigation system during the approach phase, which requires locating the drogue precisely. Drogue detection is a challenging task due to disorderly motion of drogue caused by both the tanker wake vortex and atmospheric turbulence. In this paper, the problem of drogue detection is considered as a problem of moving object detection. A drogue detection algorithm based on low rank and sparse decomposition with local multiple features is proposed. The global and local information of drogue is introduced into the detection model in a unified way. The experimental results on real autonomous aerial refueling videos show that the proposed drogue detection algorithm is effective.

Comparison of different detection methods for persistent multiple hypothesis tracking in wide area motion imagery

NASA Astrophysics Data System (ADS)

Hartung, Christine; Spraul, Raphael; Schuchert, Tobias

2017-10-01

Wide area motion imagery (WAMI) acquired by an airborne multicamera sensor enables continuous monitoring of large urban areas. Each image can cover regions of several square kilometers and contain thousands of vehicles. Reliable vehicle tracking in this imagery is an important prerequisite for surveillance tasks, but remains challenging due to low frame rate and small object size. Most WAMI tracking approaches rely on moving object detections generated by frame differencing or background subtraction. These detection methods fail when objects slow down or stop. Recent approaches for persistent tracking compensate for missing motion detections by combining a detection-based tracker with a second tracker based on appearance or local context. In order to avoid the additional complexity introduced by combining two trackers, we employ an alternative single tracker framework that is based on multiple hypothesis tracking and recovers missing motion detections with a classifierbased detector. We integrate an appearance-based similarity measure, merge handling, vehicle-collision tests, and clutter handling to adapt the approach to the specific context of WAMI tracking. We apply the tracking framework on a region of interest of the publicly available WPAFB 2009 dataset for quantitative evaluation; a comparison to other persistent WAMI trackers demonstrates state of the art performance of the proposed approach. Furthermore, we analyze in detail the impact of different object detection methods and detector settings on the quality of the output tracking results. For this purpose, we choose four different motion-based detection methods that vary in detection performance and computation time to generate the input detections. As detector parameters can be adjusted to achieve different precision and recall performance, we combine each detection method with different detector settings that yield (1) high precision and low recall, (2) high recall and low precision, and (3) best f-score. Comparing the tracking performance achieved with all generated sets of input detections allows us to quantify the sensitivity of the tracker to different types of detector errors and to derive recommendations for detector and parameter choice.

High performance multichannel photonic biochip sensors for future point of care diagnostics: an overview on two EU-sponsored projects

NASA Astrophysics Data System (ADS)

Giannone, Domenico; Kazmierczak, Andrzej; Dortu, Fabian; Vivien, Laurent; Sohlström, Hans

2010-04-01

We present here research work on two optical biosensors which have been developed within two separate European projects (6th and 7th EU Framework Programmes). The biosensors are based on the idea of a disposable biochip, integrating photonics and microfluidics, optically interrogated by a multichannel interrogation platform. The objective is to develop versatile tools, suitable for performing screening tests at Point of Care or for example, at schools or in the field. The two projects explore different options in terms of optical design and different materials. While SABIO used Si3N4/SiO2 ring resonators structures, P3SENS aims at the use of photonic crystal devices based on polymers, potentially a much more economical option. We discuss both approaches to show how they enable high sensitivity and multiple channel detection. The medium term objective is to develop a new detection system that has low cost and is portable but at the same time offering high sensitivity, selectivity and multiparametric detection from a sample containing various components (e.g. blood, serum, saliva, etc.). Most biological sensing devices already present on the market suffer from limitations in multichannel operation capability (either the detection of multiple analytes indicating a given pathology or the simultaneous detection of multiple pathologies). In other words, the number of different analytes that can be detected on a single chip is very limited. This limitation is a main issue addressed by the two projects. The excessive cost per test of conventional bio sensing devices is a second issue that is addressed.

Estimated capacity of object files in visual short-term memory is not improved by retrieval cueing.

PubMed

Saiki, Jun; Miyatsuji, Hirofumi

2009-03-23

Visual short-term memory (VSTM) has been claimed to maintain three to five feature-bound object representations. Some results showing smaller capacity estimates for feature binding memory have been interpreted as the effects of interference in memory retrieval. However, change-detection tasks may not properly evaluate complex feature-bound representations such as triple conjunctions in VSTM. To understand the general type of feature-bound object representation, evaluation of triple conjunctions is critical. To test whether interference occurs in memory retrieval for complete object file representations in a VSTM task, we cued retrieval in novel paradigms that directly evaluate the memory for triple conjunctions, in comparison with a simple change-detection task. In our multiple object permanence tracking displays, observers monitored for a switch in feature combination between objects during an occlusion period, and we found that a retrieval cue provided no benefit with the triple conjunction tasks, but significant facilitation with the change-detection task, suggesting that low capacity estimates of object file memory in VSTM reflect a limit on maintenance, not retrieval.

What has driven the evolution of multiple cone classes in visual systems: object contrast enhancement or light flicker elimination?

PubMed

Sabbah, Shai; Hawryshyn, Craig W

2013-07-04

Two competing theories have been advanced to explain the evolution of multiple cone classes in vertebrate eyes. These two theories have important, but different, implications for our understanding of the design and tuning of vertebrate visual systems. The 'contrast theory' proposes that multiple cone classes evolved in shallow-water fish to maximize the visual contrast of objects against diverse backgrounds. The competing 'flicker theory' states that multiple cone classes evolved to eliminate the light flicker inherent in shallow-water environments through antagonistic neural interactions, thereby enhancing object detection. However, the selective pressures that have driven the evolution of multiple cone classes remain largely obscure. We show that two critical assumptions of the flicker theory are violated. We found that the amplitude and temporal frequency of flicker vary over the visible spectrum, precluding its cancellation by simple antagonistic interactions between the output signals of cones. Moreover, we found that the temporal frequency of flicker matches the frequency where sensitivity is maximal in a wide range of fish taxa, suggesting that the flicker may actually enhance the detection of objects. Finally, using modeling of the chromatic contrast between fish pattern and background under flickering illumination, we found that the spectral sensitivity of cones in a cichlid focal species is optimally tuned to maximize the visual contrast between fish pattern and background, instead of to produce a flicker-free visual signal. The violation of its two critical assumptions substantially undermines support for the flicker theory as originally formulated. While this alone does not support the contrast theory, comparison of the contrast and flicker theories revealed that the visual system of our focal species was tuned as predicted by the contrast theory rather than by the flicker theory (or by some combination of the two). Thus, these findings challenge key assumptions of the flicker theory, leaving the contrast theory as the most parsimonious and tenable account of the evolution of multiple cone classes.

Detection, Identification, Location, and Remote Sensing using SAW RFID Sensor Tags

NASA Technical Reports Server (NTRS)

Barton, Richard J.

2009-01-01

In this presentation, we will consider the problem of simultaneous detection, identification, location estimation, and remote sensing for multiple objects. In particular, we will describe the design and testing of a wireless system capable of simultaneously detecting the presence of multiple objects, identifying each object, and acquiring both a low-resolution estimate of location and a high-resolution estimate of temperature for each object based on wireless interrogation of passive surface acoustic wave (SAW) radiofrequency identification (RFID) sensor tags affixed to each object. The system is being studied for application on the lunar surface as well as for terrestrial remote sensing applications such as pre-launch monitoring and testing of spacecraft on the launch pad and monitoring of test facilities. The system utilizes a digitally beam-formed planar receiving antenna array to extend range and provide direction-of-arrival information coupled with an approximate maximum-likelihood signal processing algorithm to provide near-optimal estimation of both range and temperature. The system is capable of forming a large number of beams within the field of view and resolving the information from several tags within each beam. The combination of both spatial and waveform discrimination provides the capability to track and monitor telemetry from a large number of objects appearing simultaneously within the field of view of the receiving array. In the presentation, we will summarize the system design and illustrate several aspects of the operational characteristics and signal structure. We will examine the theoretical performance characteristics of the system and compare the theoretical results with results obtained from experiments in both controlled laboratory environments and in the field.

Airplane detection based on fusion framework by combining saliency model with Deep Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Dou, Hao; Sun, Xiao; Li, Bin; Deng, Qianqian; Yang, Xubo; Liu, Di; Tian, Jinwen

2018-03-01

Aircraft detection from very high resolution remote sensing images, has gained more increasing interest in recent years due to the successful civil and military applications. However, several problems still exist: 1) how to extract the high-level features of aircraft; 2) locating objects within such a large image is difficult and time consuming; 3) A common problem of multiple resolutions of satellite images still exists. In this paper, inspirited by biological visual mechanism, the fusion detection framework is proposed, which fusing the top-down visual mechanism (deep CNN model) and bottom-up visual mechanism (GBVS) to detect aircraft. Besides, we use multi-scale training method for deep CNN model to solve the problem of multiple resolutions. Experimental results demonstrate that our method can achieve a better detection result than the other methods.

Faint Object Detection in Multi-Epoch Observations via Catalog Data Fusion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Budavári, Tamás; Szalay, Alexander S.; Loredo, Thomas J.

Astronomy in the time-domain era faces several new challenges. One of them is the efficient use of observations obtained at multiple epochs. The work presented here addresses faint object detection and describes an incremental strategy for separating real objects from artifacts in ongoing surveys. The idea is to produce low-threshold single-epoch catalogs and to accumulate information across epochs. This is in contrast to more conventional strategies based on co-added or stacked images. We adopt a Bayesian approach, addressing object detection by calculating the marginal likelihoods for hypotheses asserting that there is no object or one object in a small imagemore » patch containing at most one cataloged source at each epoch. The object-present hypothesis interprets the sources in a patch at different epochs as arising from a genuine object; the no-object hypothesis interprets candidate sources as spurious, arising from noise peaks. We study the detection probability for constant-flux objects in a Gaussian noise setting, comparing results based on single and stacked exposures to results based on a series of single-epoch catalog summaries. Our procedure amounts to generalized cross-matching: it is the product of a factor accounting for the matching of the estimated fluxes of the candidate sources and a factor accounting for the matching of their estimated directions. We find that probabilistic fusion of multi-epoch catalogs can detect sources with similar sensitivity and selectivity compared to stacking. The probabilistic cross-matching framework underlying our approach plays an important role in maintaining detection sensitivity and points toward generalizations that could accommodate variability and complex object structure.« less

Dynamical mass and multiplicity constraints on co-orbital bodies around stars

NASA Astrophysics Data System (ADS)

Veras, Dimitri; Marsh, Thomas R.; Gänsicke, Boris T.

2016-09-01

Objects transiting near or within the disruption radius of both main-sequence (e.g. KOI 1843) and white dwarf (WD 1145+017) stars are now known. Upon fragmentation or disintegration, these planets or asteroids may produce co-orbital configurations of nearly equal mass objects. However, as evidenced by the co-orbital objects detected by transit photometry in the WD 1145+017 system, these bodies are largely unconstrained in size, mass, and total number (multiplicity). Motivated by potential future similar discoveries, we perform N-body simulations to demonstrate if and how debris masses and multiplicity may be bounded due to second-to-minute deviations and the resulting accumulated phase shifts in the osculating orbital period amongst multiple co-orbital equal point masses. We establish robust lower and upper mass bounds as a function of orbital period deviation, but find the constraints on multiplicity to be weak. We also quantify the fuzzy instability boundary, and show that mutual collisions occur in less than 5, 10, and 20 per cent of our simulations for masses of 1021, 1022, and 1023 kg. Our results may provide useful initial rough constraints on other stellar systems with multiple co-orbital bodies.

FERMI/LAT OBSERVATIONS OF SWIFT/BAT SEYFERT GALAXIES: ON THE CONTRIBUTION OF RADIO-QUIET ACTIVE GALACTIC NUCLEI TO THE EXTRAGALACTIC {gamma}-RAY BACKGROUND

DOE Office of Scientific and Technical Information (OSTI.GOV)

Teng, Stacy H.; Mushotzky, Richard F.; Reynolds, Christopher S.

2011-12-01

We present the analysis of 2.1 years of Fermi Large Area Telescope (LAT) data on 491 Seyfert galaxies detected by the Swift Burst Alert Telescope (BAT) survey. Only the two nearest objects, NGC 1068 and NGC 4945, which were identified in the Fermi first year catalog, are detected. Using Swift/BAT and radio 20 cm fluxes, we define a new radio-loudness parameter R{sub X,BAT} where radio-loud objects have log R{sub X,BAT} > -4.7. Based on this parameter, only radio-loud sources are detected by Fermi/LAT. An upper limit to the flux of the undetected sources is derived to be {approx}2 Multiplication-Sign 10{supmore » -11} photons cm{sup -2} s{sup -1}, approximately seven times lower than the observed flux of NGC 1068. Assuming a median redshift of 0.031, this implies an upper limit to the {gamma}-ray (1-100 GeV) luminosity of {approx}< 3 Multiplication-Sign 10{sup 41} erg s{sup -1}. In addition, we identified 120 new Fermi/LAT sources near the Swift/BAT Seyfert galaxies with significant Fermi/LAT detections. A majority of these objects do not have Swift/BAT counterparts, but their possible optical counterparts include blazars, flat-spectrum radio quasars, and quasars.« less

EEG signatures accompanying auditory figure-ground segregation

PubMed Central

Tóth, Brigitta; Kocsis, Zsuzsanna; Háden, Gábor P.; Szerafin, Ágnes; Shinn-Cunningham, Barbara; Winkler, István

2017-01-01

In everyday acoustic scenes, figure-ground segregation typically requires one to group together sound elements over both time and frequency. Electroencephalogram was recorded while listeners detected repeating tonal complexes composed of a random set of pure tones within stimuli consisting of randomly varying tonal elements. The repeating pattern was perceived as a figure over the randomly changing background. It was found that detection performance improved both as the number of pure tones making up each repeated complex (figure coherence) increased, and as the number of repeated complexes (duration) increased – i.e., detection was easier when either the spectral or temporal structure of the figure was enhanced. Figure detection was accompanied by the elicitation of the object related negativity (ORN) and the P400 event-related potentials (ERPs), which have been previously shown to be evoked by the presence of two concurrent sounds. Both ERP components had generators within and outside of auditory cortex. The amplitudes of the ORN and the P400 increased with both figure coherence and figure duration. However, only the P400 amplitude correlated with detection performance. These results suggest that 1) the ORN and P400 reflect processes involved in detecting the emergence of a new auditory object in the presence of other concurrent auditory objects; 2) the ORN corresponds to the likelihood of the presence of two or more concurrent sound objects, whereas the P400 reflects the perceptual recognition of the presence of multiple auditory objects and/or preparation for reporting the detection of a target object. PMID:27421185

Detection of Stimulus Displacements Across Saccades is Capacity-Limited and Biased in Favor of the Saccade Target

PubMed Central

Irwin, David E.; Robinson, Maria M.

2015-01-01

Retinal image displacements caused by saccadic eye movements are generally unnoticed. Recent theories have proposed that perceptual stability across saccades depends on a local evaluation process centered on the saccade target object rather than on remapping and evaluating the positions of all objects in a display. In three experiments, we examined whether objects other than the saccade target also influence perceptual stability by measuring displacement detection thresholds across saccades for saccade targets and a variable number of non-saccade objects. We found that the positions of multiple objects are maintained across saccades, but with variable precision, with the saccade target object having priority in the perception of displacement, most likely because it is the focus of attention before the saccade and resides near the fovea after the saccade. The perception of displacement of objects that are not the saccade target is affected by acuity limitations, attentional limitations, and limitations on memory capacity. Unlike previous studies that have found that a postsaccadic blank improves the detection of displacement direction across saccades, we found that postsaccadic blanking hurt the detection of displacement per se by increasing false alarms. Overall, our results are consistent with the hypothesis that visual working memory underlies the perception of stability across saccades. PMID:26640430

Standardized UXO Technology Demonstration Site Scoring Record NO. 934 Technology Type/Platform: EM61 MKII/Towed

DTIC Science & Technology

2009-07-01

nonferrous metallic objects. The applicability of the instrument for ordnance and explosives (OE) detection has been widely demonstrated at sites...was cleared of all metallic items. This clearing of the metallic anomalies from the 2 acre Active Response Demonstration Site was broken into three...with their Multiple Towed Array Detection System (MTADS). This system is known for its effectiveness and ability to detect metallic items. Once the

Detecting multiple moving objects in crowded environments with coherent motion regions

DOEpatents

Cheriyadat, Anil M.; Radke, Richard J.

2013-06-11

Coherent motion regions extend in time as well as space, enforcing consistency in detected objects over long time periods and making the algorithm robust to noisy or short point tracks. As a result of enforcing the constraint that selected coherent motion regions contain disjoint sets of tracks defined in a three-dimensional space including a time dimension. An algorithm operates directly on raw, unconditioned low-level feature point tracks, and minimizes a global measure of the coherent motion regions. At least one discrete moving object is identified in a time series of video images based on the trajectory similarity factors, which is a measure of a maximum distance between a pair of feature point tracks.

Slippery road detection and evaluation.

DOT National Transportation Integrated Search

2012-05-31

The key project objectives were: 1) the demonstration and evaluation of a low cost data acquisition system that would provide a rich data set accumulated from multiple vehicles, and 2) establish how this data, coupled with situational data from other...

The Role of Attention in the Maintenance of Feature Bindings in Visual Short-term Memory

ERIC Educational Resources Information Center

Johnson, Jeffrey S.; Hollingworth, Andrew; Luck, Steven J.

2008-01-01

This study examined the role of attention in maintaining feature bindings in visual short-term memory. In a change-detection paradigm, participants attempted to detect changes in the colors and orientations of multiple objects; the changes consisted of new feature values in a feature-memory condition and changes in how existing feature values were…

Complex Dynamic Scene Perception: Effects of Attentional Set on Perceiving Single and Multiple Event Types

ERIC Educational Resources Information Center

Sanocki, Thomas; Sulman, Noah

2013-01-01

Three experiments measured the efficiency of monitoring complex scenes composed of changing objects, or events. All events lasted about 4 s, but in a given block of trials, could be of a single type (single task) or of multiple types (multitask, with a total of four event types). Overall accuracy of detecting target events amid distractors was…

Development of a real time multiple target, multi camera tracker for civil security applications

NASA Astrophysics Data System (ADS)

Åkerlund, Hans

2009-09-01

A surveillance system has been developed that can use multiple TV-cameras to detect and track personnel and objects in real time in public areas. The document describes the development and the system setup. The system is called NIVS Networked Intelligent Video Surveillance. Persons in the images are tracked and displayed on a 3D map of the surveyed area.

One Shot Detection with Laplacian Object and Fast Matrix Cosine Similarity.

PubMed

Biswas, Sujoy Kumar; Milanfar, Peyman

2016-03-01

One shot, generic object detection involves searching for a single query object in a larger target image. Relevant approaches have benefited from features that typically model the local similarity patterns. In this paper, we combine local similarity (encoded by local descriptors) with a global context (i.e., a graph structure) of pairwise affinities among the local descriptors, embedding the query descriptors into a low dimensional but discriminatory subspace. Unlike principal components that preserve global structure of feature space, we actually seek a linear approximation to the Laplacian eigenmap that permits us a locality preserving embedding of high dimensional region descriptors. Our second contribution is an accelerated but exact computation of matrix cosine similarity as the decision rule for detection, obviating the computationally expensive sliding window search. We leverage the power of Fourier transform combined with integral image to achieve superior runtime efficiency that allows us to test multiple hypotheses (for pose estimation) within a reasonably short time. Our approach to one shot detection is training-free, and experiments on the standard data sets confirm the efficacy of our model. Besides, low computation cost of the proposed (codebook-free) object detector facilitates rather straightforward query detection in large data sets including movie videos.

Novel X-ray backscatter technique for detection of dangerous materials: application to aviation and port security

NASA Astrophysics Data System (ADS)

Kolkoori, S.; Wrobel, N.; Osterloh, K.; Zscherpel, U.; Ewert, U.

2013-09-01

Radiological inspections, in general, are the nondestructive testing (NDT) methods to detect the bulk of explosives in large objects. In contrast to personal luggage, cargo or building components constitute a complexity that may significantly hinder the detection of a threat by conventional X-ray transmission radiography. In this article, a novel X-ray backscatter technique is presented for detecting suspicious objects in a densely packed large object with only a single sided access. It consists of an X-ray backscatter camera with a special twisted slit collimator for imaging backscattering objects. The new X-ray backscatter camera is not only imaging the objects based on their densities but also by including the influences of surrounding objects. This unique feature of the X-ray backscatter camera provides new insights in identifying the internal features of the inspected object. Experimental mock-ups were designed imitating containers with threats among a complex packing as they may be encountered in reality. We investigated the dependence of the quality of the X-ray backscatter image on (a) the exposure time, (b) multiple exposures, (c) the distance between object and slit camera, and (d) the width of the slit. At the end, the significant advantages of the presented X-ray backscatter camera in the context of aviation and port security are discussed.

Tactical decisions for changeable cuttlefish camouflage: visual cues for choosing masquerade are relevant from a greater distance than visual cues used for background matching.

PubMed

Buresch, Kendra C; Ulmer, Kimberly M; Cramer, Corinne; McAnulty, Sarah; Davison, William; Mäthger, Lydia M; Hanlon, Roger T

2015-10-01

Cuttlefish use multiple camouflage tactics to evade their predators. Two common tactics are background matching (resembling the background to hinder detection) and masquerade (resembling an uninteresting or inanimate object to impede detection or recognition). We investigated how the distance and orientation of visual stimuli affected the choice of these two camouflage tactics. In the current experiments, cuttlefish were presented with three visual cues: 2D horizontal floor, 2D vertical wall, and 3D object. Each was placed at several distances: directly beneath (in a circle whose diameter was one body length (BL); at zero BL [(0BL); i.e., directly beside, but not beneath the cuttlefish]; at 1BL; and at 2BL. Cuttlefish continued to respond to 3D visual cues from a greater distance than to a horizontal or vertical stimulus. It appears that background matching is chosen when visual cues are relevant only in the immediate benthic surroundings. However, for masquerade, objects located multiple body lengths away remained relevant for choice of camouflage. © 2015 Marine Biological Laboratory.

Enhanced Algorithms for EO/IR Electronic Stabilization, Clutter Suppression, and Track-Before-Detect for Multiple Low Observable Targets

NASA Astrophysics Data System (ADS)

Tartakovsky, A.; Brown, A.; Brown, J.

The paper describes the development and evaluation of a suite of advanced algorithms which provide significantly-improved capabilities for finding, fixing, and tracking multiple ballistic and flying low observable objects in highly stressing cluttered environments. The algorithms have been developed for use in satellite-based staring and scanning optical surveillance suites for applications including theatre and intercontinental ballistic missile early warning, trajectory prediction, and multi-sensor track handoff for midcourse discrimination and intercept. The functions performed by the algorithms include electronic sensor motion compensation providing sub-pixel stabilization (to 1/100 of a pixel), as well as advanced temporal-spatial clutter estimation and suppression to below sensor noise levels, followed by statistical background modeling and Bayesian multiple-target track-before-detect filtering. The multiple-target tracking is performed in physical world coordinates to allow for multi-sensor fusion, trajectory prediction, and intercept. Output of detected object cues and data visualization are also provided. The algorithms are designed to handle a wide variety of real-world challenges. Imaged scenes may be highly complex and infinitely varied -- the scene background may contain significant celestial, earth limb, or terrestrial clutter. For example, when viewing combined earth limb and terrestrial scenes, a combination of stationary and non-stationary clutter may be present, including cloud formations, varying atmospheric transmittance and reflectance of sunlight and other celestial light sources, aurora, glint off sea surfaces, and varied natural and man-made terrain features. The targets of interest may also appear to be dim, relative to the scene background, rendering much of the existing deployed software useless for optical target detection and tracking. Additionally, it may be necessary to detect and track a large number of objects in the threat cloud, and these objects may not always be resolvable in individual data frames. In the present paper, the performance of the developed algorithms is demonstrated using real-world data containing resident space objects observed from the MSX platform, with backgrounds varying from celestial to combined celestial and earth limb, with instances of extremely bright aurora clutter. Simulation results are also presented for parameterized variations in signal-to-clutter levels (down to 1/1000) and signal-to-noise levels (down to 1/6) for simulated targets against real-world terrestrial clutter backgrounds. We also discuss algorithm processing requirements and C++ software processing capabilities from our on-going MDA- and AFRL-sponsored development of an image processing toolkit (iPTK). In the current effort, the iPTK is being developed to a Technology Readiness Level (TRL) of 6 by mid-2010, in preparation for possible integration with STSS-like, SBIRS high-like and SBSS-like surveillance suites.

Polarization-multiplexing ghost imaging

NASA Astrophysics Data System (ADS)

Dongfeng, Shi; Jiamin, Zhang; Jian, Huang; Yingjian, Wang; Kee, Yuan; Kaifa, Cao; Chenbo, Xie; Dong, Liu; Wenyue, Zhu

2018-03-01

A novel technique for polarization-multiplexing ghost imaging is proposed to simultaneously obtain multiple polarimetric information by a single detector. Here, polarization-division multiplexing speckles are employed for object illumination. The light reflected from the objects is detected by a single-pixel detector. An iterative reconstruction method is used to restore the fused image containing the different polarimetric information by using the weighted sum of the multiplexed speckles based on the correlation coefficients obtained from the detected intensities. Next, clear images of the different polarimetric information are recovered by demultiplexing the fused image. The results clearly demonstrate that the proposed method is effective.

Development of a Computer-Aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms

DTIC Science & Technology

2006-06-01

Hadjiiski, and N. Petrick, "Computerized nipple identification for multiple image analysis in computer-aided diagnosis," Medical Physics 31, 2871...candidates, 3 identification of suspicious objects, 4 feature extraction and analysis, and 5 FP reduc- tion by classification of normal tissue...detection of microcalcifi- cations on digitized mammograms.41 An illustration of a La- placian decomposition tree is shown on the left-hand side of Fig. 4

Thinking in terms of sensors: personification of self as an object in physics problem solving

NASA Astrophysics Data System (ADS)

Tabor-Morris, A. E.

2015-03-01

How can physics teachers help students develop consistent problem solving techniques for both simple and complicated physics problems, such as those that encompass objects undergoing multiple forces (mechanical or electrical) as individually portrayed in free-body diagrams and/or phenomenon involving multiple objects, such as Doppler effect reflection applications in echoes and ultrasonic cardiac monitoring for sound, or police radar for light? These problems can confuse novice physics students, and to sort out problem parts, the suggestion is made here to guide the student to personify self as the object in question, that is, to imagine oneself as the object undergoing outside influences such as forces and then qualify and quantify those for the problem at hand. This personification does NOT, as according to the three traditional definitions of the term (animism, anthropomorphism and teleology), empower the object to act, but instead just to detect its environment. By having students use their imagination to put themselves in the place of the object, they can ‘sense’ the influences the object is experiencing to analyze these individually, hopefully reducing the student’s feeling of being overwhelmed with information, and also imbuing the student with a sense of having experienced the situation. This can be especially useful in problems that involve both multiple forces AND multiple objects (for example, Atwood’s machine), since objects acted upon need to be considered separately and consecutively, with the idea that one cannot be two objects at once. This personification technique, documented to have been used by both Einstein and Feynman, is recommended here for secondary-school teen and university-level adult learners with discussions on specific physics and astronomy classroom strategies.

Design of an Evolutionary Approach for Intrusion Detection

PubMed Central

2013-01-01

A novel evolutionary approach is proposed for effective intrusion detection based on benchmark datasets. The proposed approach can generate a pool of noninferior individual solutions and ensemble solutions thereof. The generated ensembles can be used to detect the intrusions accurately. For intrusion detection problem, the proposed approach could consider conflicting objectives simultaneously like detection rate of each attack class, error rate, accuracy, diversity, and so forth. The proposed approach can generate a pool of noninferior solutions and ensembles thereof having optimized trade-offs values of multiple conflicting objectives. In this paper, a three-phase, approach is proposed to generate solutions to a simple chromosome design in the first phase. In the first phase, a Pareto front of noninferior individual solutions is approximated. In the second phase of the proposed approach, the entire solution set is further refined to determine effective ensemble solutions considering solution interaction. In this phase, another improved Pareto front of ensemble solutions over that of individual solutions is approximated. The ensemble solutions in improved Pareto front reported improved detection results based on benchmark datasets for intrusion detection. In the third phase, a combination method like majority voting method is used to fuse the predictions of individual solutions for determining prediction of ensemble solution. Benchmark datasets, namely, KDD cup 1999 and ISCX 2012 dataset, are used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover individual solutions and ensemble solutions thereof with a good support and a detection rate from benchmark datasets (in comparison with well-known ensemble methods like bagging and boosting). In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives, and a dataset can be represented in the form of labelled instances in terms of its features. PMID:24376390

EEG signatures accompanying auditory figure-ground segregation.

PubMed

Tóth, Brigitta; Kocsis, Zsuzsanna; Háden, Gábor P; Szerafin, Ágnes; Shinn-Cunningham, Barbara G; Winkler, István

2016-11-01

In everyday acoustic scenes, figure-ground segregation typically requires one to group together sound elements over both time and frequency. Electroencephalogram was recorded while listeners detected repeating tonal complexes composed of a random set of pure tones within stimuli consisting of randomly varying tonal elements. The repeating pattern was perceived as a figure over the randomly changing background. It was found that detection performance improved both as the number of pure tones making up each repeated complex (figure coherence) increased, and as the number of repeated complexes (duration) increased - i.e., detection was easier when either the spectral or temporal structure of the figure was enhanced. Figure detection was accompanied by the elicitation of the object related negativity (ORN) and the P400 event-related potentials (ERPs), which have been previously shown to be evoked by the presence of two concurrent sounds. Both ERP components had generators within and outside of auditory cortex. The amplitudes of the ORN and the P400 increased with both figure coherence and figure duration. However, only the P400 amplitude correlated with detection performance. These results suggest that 1) the ORN and P400 reflect processes involved in detecting the emergence of a new auditory object in the presence of other concurrent auditory objects; 2) the ORN corresponds to the likelihood of the presence of two or more concurrent sound objects, whereas the P400 reflects the perceptual recognition of the presence of multiple auditory objects and/or preparation for reporting the detection of a target object. Copyright © 2016. Published by Elsevier Inc.

Change-based threat detection in urban environments with a forward-looking camera

NASA Astrophysics Data System (ADS)

Morton, Kenneth, Jr.; Ratto, Christopher; Malof, Jordan; Gunter, Michael; Collins, Leslie; Torrione, Peter

2012-06-01

Roadside explosive threats continue to pose a significant risk to soldiers and civilians in conflict areas around the world. These objects are easy to manufacture and procure, but due to their ad hoc nature, they are difficult to reliably detect using standard sensing technologies. Although large roadside explosive hazards may be difficult to conceal in rural environments, urban settings provide a much more complicated background where seemingly innocuous objects (e.g., piles of trash, roadside debris) may be used to obscure threats. Since direct detection of all innocuous objects would flag too many objects to be of use, techniques must be employed to reduce the number of alarms generated and highlight only a limited subset of possibly threatening regions for the user. In this work, change detection techniques are used to reduce false alarm rates and increase detection capabilities for possible threat identification in urban environments. The proposed model leverages data from multiple video streams collected over the same regions by first applying video aligning and then using various distance metrics to detect changes based on image keypoints in the video streams. Data collected at an urban warfare simulation range at an Eastern US test site was used to evaluate the proposed approach, and significant reductions in false alarm rates compared to simpler techniques are illustrated.

Detection of nuclei in 4D Nomarski DIC microscope images of early Caenorhabditis elegans embryos using local image entropy and object tracking

PubMed Central

Hamahashi, Shugo; Onami, Shuichi; Kitano, Hiroaki

2005-01-01

Background The ability to detect nuclei in embryos is essential for studying the development of multicellular organisms. A system of automated nuclear detection has already been tested on a set of four-dimensional (4D) Nomarski differential interference contrast (DIC) microscope images of Caenorhabditis elegans embryos. However, the system needed laborious hand-tuning of its parameters every time a new image set was used. It could not detect nuclei in the process of cell division, and could detect nuclei only from the two- to eight-cell stages. Results We developed a system that automates the detection of nuclei in a set of 4D DIC microscope images of C. elegans embryos. Local image entropy is used to produce regions of the images that have the image texture of the nucleus. From these regions, those that actually detect nuclei are manually selected at the first and last time points of the image set, and an object-tracking algorithm then selects regions that detect nuclei in between the first and last time points. The use of local image entropy makes the system applicable to multiple image sets without the need to change its parameter values. The use of an object-tracking algorithm enables the system to detect nuclei in the process of cell division. The system detected nuclei with high sensitivity and specificity from the one- to 24-cell stages. Conclusion A combination of local image entropy and an object-tracking algorithm enabled highly objective and productive detection of nuclei in a set of 4D DIC microscope images of C. elegans embryos. The system will facilitate genomic and computational analyses of C. elegans embryos. PMID:15910690

Remembering Complex Objects in Visual Working Memory: Do Capacity Limits Restrict Objects or Features?

PubMed Central

Hardman, Kyle; Cowan, Nelson

2014-01-01

Visual working memory stores stimuli from our environment as representations that can be accessed by high-level control processes. This study addresses a longstanding debate in the literature about whether storage limits in visual working memory include a limit to the complexity of discrete items. We examined the issue with a number of change-detection experiments that used complex stimuli which possessed multiple features per stimulus item. We manipulated the number of relevant features of the stimulus objects in order to vary feature load. In all of our experiments, we found that increased feature load led to a reduction in change-detection accuracy. However, we found that feature load alone could not account for the results, but that a consideration of the number of relevant objects was also required. This study supports capacity limits for both feature and object storage in visual working memory. PMID:25089739

Connecting a cognitive architecture to robotic perception

NASA Astrophysics Data System (ADS)

Kurup, Unmesh; Lebiere, Christian; Stentz, Anthony; Hebert, Martial

2012-06-01

We present an integrated architecture in which perception and cognition interact and provide information to each other leading to improved performance in real-world situations. Our system integrates the Felzenswalb et. al. object-detection algorithm with the ACT-R cognitive architecture. The targeted task is to predict and classify pedestrian behavior in a checkpoint scenario, most specifically to discriminate between normal versus checkpoint-avoiding behavior. The Felzenswalb algorithm is a learning-based algorithm for detecting and localizing objects in images. ACT-R is a cognitive architecture that has been successfully used to model human cognition with a high degree of fidelity on tasks ranging from basic decision-making to the control of complex systems such as driving or air traffic control. The Felzenswalb algorithm detects pedestrians in the image and provides ACT-R a set of features based primarily on their locations. ACT-R uses its pattern-matching capabilities, specifically its partial-matching and blending mechanisms, to track objects across multiple images and classify their behavior based on the sequence of observed features. ACT-R also provides feedback to the Felzenswalb algorithm in the form of expected object locations that allow the algorithm to eliminate false-positives and improve its overall performance. This capability is an instance of the benefits pursued in developing a richer interaction between bottom-up perceptual processes and top-down goal-directed cognition. We trained the system on individual behaviors (only one person in the scene) and evaluated its performance across single and multiple behavior sets.

Case, Teacher and School Characteristics Influencing Teachers' Detection and Reporting of Child Physical Abuse and Neglect: Results from an Australian Survey

ERIC Educational Resources Information Center

Walsh, Kerryann; Bridgstock, Ruth; Farrell, Ann; Rassafiani, Mehdi; Schweitzer, Robert

2008-01-01

Objective: To identify the influence of multiple case, teacher and school characteristics on Australian primary school teachers' propensity to detect and report child physical abuse and neglect using vignettes as short hypothetical cases. Methods: A sample of 254 teachers completed a self-report questionnaire. They responded to a series of 32…

Maritime Search and Rescue via Multiple Coordinated UAS

DTIC Science & Technology

2017-06-12

performed by a set of UAS. Our investigation covers the detection of multiple mobile objects by a heterogeneous collection of UAS. Three methods (two...account for contingencies such as airspace deconfliction. Results are produced using simulation to verify the capability of the proposed method and to...compare the various par- titioning methods . Results from this simulation show that great gains in search efficiency can be made when the search space is

Systems for detecting charged particles in object inspection

DOEpatents

Morris, Christopher L.; Makela, Mark F.

2013-08-20

Techniques, apparatus and systems for detecting particles such as muons. In one implementation, a monitoring system has a cosmic ray-produced charged particle tracker with a plurality of drift cells. The drift cells, which can be for example aluminum drift tubes, can be arranged at least above and below a volume to be scanned to thereby track incoming and outgoing charged particles, such as cosmic ray-produced muons, while also detecting gamma rays. The system can selectively detect devices or materials, such as iron, lead, gold and/or tungsten, occupying the volume from multiple scattering of the charged particles passing through the volume and can also detect any radioactive sources occupying the volume from gamma rays emitted therefrom. If necessary, the drift tubes can be sealed to eliminate the need for a gas handling system. The system can be employed to inspect occupied vehicles at border crossings for nuclear threat objects.

Calibration of asynchronous smart phone cameras from moving objects

NASA Astrophysics Data System (ADS)

Hagen, Oksana; Istenič, Klemen; Bharti, Vibhav; Dhali, Maruf Ahmed; Barmaimon, Daniel; Houssineau, Jérémie; Clark, Daniel

2015-04-01

Calibrating multiple cameras is a fundamental prerequisite for many Computer Vision applications. Typically this involves using a pair of identical synchronized industrial or high-end consumer cameras. This paper considers an application on a pair of low-cost portable cameras with different parameters that are found in smart phones. This paper addresses the issues of acquisition, detection of moving objects, dynamic camera registration and tracking of arbitrary number of targets. The acquisition of data is performed using two standard smart phone cameras and later processed using detections of moving objects in the scene. The registration of cameras onto the same world reference frame is performed using a recently developed method for camera calibration using a disparity space parameterisation and the single-cluster PHD filter.

Detection of a meteorite 'stream' - Observations of a second meteorite fall from the orbit of the Innisfree chondrite

NASA Astrophysics Data System (ADS)

Halliday, I.

1987-03-01

The first observational evidence of multiple meteorite falls from the same orbit is adduced from the February 6, 1980 fall of a meteorite precisely 3 yr after the fall of the Innisfree meteorite. Due consideration of the detection probability for two related objects with the meteorite camera network in western Canada suggests that the Innisfree brecciated LL chondrite was a near-surface fragment from a parent object whose radius was of the order of several tens of meters. A meteorite mass of 1.8 kg is predicted for the new object, whose recovery in the vicinity of Ridgedale, Saskatchewan, is now sought for the sake of comparison with the Innisfree chondrite.

Uncued Low SNR Detection with Likelihood from Image Multi Bernoulli Filter

NASA Astrophysics Data System (ADS)

Murphy, T.; Holzinger, M.

2016-09-01

Both SSA and SDA necessitate uncued, partially informed detection and orbit determination efforts for small space objects which often produce only low strength electro-optical signatures. General frame to frame detection and tracking of objects includes methods such as moving target indicator, multiple hypothesis testing, direct track-before-detect methods, and random finite set based multiobject tracking. This paper will apply the multi-Bernoilli filter to low signal-to-noise ratio (SNR), uncued detection of space objects for space domain awareness applications. The primary novel innovation in this paper is a detailed analysis of the existing state-of-the-art likelihood functions and a likelihood function, based on a binary hypothesis, previously proposed by the authors. The algorithm is tested on electro-optical imagery obtained from a variety of sensors at Georgia Tech, including the GT-SORT 0.5m Raven-class telescope, and a twenty degree field of view high frame rate CMOS sensor. In particular, a data set of an extended pass of the Hitomi Astro-H satellite approximately 3 days after loss of communication and potential break up is examined.

Small Arrays for Seismic Intruder Detections: A Simulation Based Experiment

NASA Astrophysics Data System (ADS)

Pitarka, A.

2014-12-01

Seismic sensors such as geophones and fiber optic have been increasingly recognized as promising technologies for intelligence surveillance, including intruder detection and perimeter defense systems. Geophone arrays have the capability to provide cost effective intruder detection in protecting assets with large perimeters. A seismic intruder detection system uses one or multiple arrays of geophones design to record seismic signals from footsteps and ground vehicles. Using a series of real-time signal processing algorithms the system detects, classify and monitors the intruder's movement. We have carried out numerical experiments to demonstrate the capability of a seismic array to detect moving targets that generate seismic signals. The seismic source is modeled as a vertical force acting on the ground that generates continuous impulsive seismic signals with different predominant frequencies. Frequency-wave number analysis of the synthetic array data was used to demonstrate the array's capability at accurately determining intruder's movement direction. The performance of the array was also analyzed in detecting two or more objects moving at the same time. One of the drawbacks of using a single array system is its inefficiency at detecting seismic signals deflected by large underground objects. We will show simulation results of the effect of an underground concrete block at shielding the seismic signal coming from an intruder. Based on simulations we found that multiple small arrays can greatly improve the system's detection capability in the presence of underground structures. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344

An object-based approach for detecting small brain lesions: application to Virchow-Robin spaces.

PubMed

Descombes, Xavier; Kruggel, Frithjof; Wollny, Gert; Gertz, Hermann Josef

2004-02-01

This paper is concerned with the detection of multiple small brain lesions from magnetic resonance imaging (MRI) data. A model based on the marked point process framework is designed to detect Virchow-Robin spaces (VRSs). These tubular shaped spaces are due to retraction of the brain parenchyma from its supplying arteries. VRS are described by simple geometrical objects that are introduced as small tubular structures. Their radiometric properties are embedded in a data term. A prior model includes interactions describing the clustering property of VRS. A Reversible Jump Markov Chain Monte Carlo algorithm (RJMCMC) optimizes the proposed model, obtained by multiplying the prior and the data model. Example results are shown on T1-weighted MRI datasets of elderly subjects.

A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Rosen, Mark; Madabhushi, Anant

2008-03-01

Current techniques for localization of prostatic adenocarcinoma (CaP) via blinded trans-rectal ultrasound biopsy are associated with a high false negative detection rate. While high resolution endorectal in vivo Magnetic Resonance (MR) prostate imaging has been shown to have improved contrast and resolution for CaP detection over ultrasound, similarity in intensity characteristics between benign and cancerous regions on MR images contribute to a high false positive detection rate. In this paper, we present a novel unsupervised segmentation method that employs manifold learning via consensus schemes for detection of cancerous regions from high resolution 1.5 Tesla (T) endorectal in vivo prostate MRI. A significant contribution of this paper is a method to combine multiple weak, lower-dimensional representations of high dimensional feature data in a way analogous to classifier ensemble schemes, and hence create a stable and accurate reduced dimensional representation. After correcting for MR image intensity artifacts, such as bias field inhomogeneity and intensity non-standardness, our algorithm extracts over 350 3D texture features at every spatial location in the MR scene at multiple scales and orientations. Non-linear dimensionality reduction schemes such as Locally Linear Embedding (LLE) and Graph Embedding (GE) are employed to create multiple low dimensional data representations of this high dimensional texture feature space. Our novel consensus embedding method is used to average object adjacencies from within the multiple low dimensional projections so that class relationships are preserved. Unsupervised consensus clustering is then used to partition the objects in this consensus embedding space into distinct classes. Quantitative evaluation on 18 1.5 T prostate MR data against corresponding histology obtained from the multi-site ACRIN trials show a sensitivity of 92.65% and a specificity of 82.06%, which suggests that our method is successfully able to detect suspicious regions in the prostate.

Multi-point laser coherent detection system and its application on vibration measurement

NASA Astrophysics Data System (ADS)

Fu, Y.; Yang, C.; Xu, Y. J.; Liu, H.; Yan, K.; Guo, M.

2015-05-01

Laser Doppler vibrometry (LDV) is a well-known interferometric technique to measure the motions, vibrations and mode shapes of machine components and structures. The drawback of commercial LDV is that it can only offer a pointwise measurement. In order to build up a vibrometric image, a scanning device is normally adopted to scan the laser point in two spatial axes. These scanning laser Doppler vibrometers (SLDV) assume that the measurement conditions remain invariant while multiple and identical, sequential measurements are performed. This assumption makes SLDVs impractical to do measurement on transient events. In this paper, we introduce a new multiple-point laser coherent detection system based on spatial-encoding technology and fiber configuration. A simultaneous vibration measurement on multiple points is realized using a single photodetector. A prototype16-point laser coherent detection system is built and it is applied to measure the vibration of various objects, such as body of a car or a motorcycle when engine is on and under shock tests. The results show the prospect of multi-point laser coherent detection system in the area of nondestructive test and precise dynamic measurement.

High resolution skin-like sensor capable of sensing and visualizing various sensations and three dimensional shape.

PubMed

Xu, Tianbai; Wang, Wenbo; Bian, Xiaolei; Wang, Xiaoxue; Wang, Xiaozhi; Luo, J K; Dong, Shurong

2015-08-13

Human skin contains multiple receptors, and is able to sense various stimuli such as temperature, pressure, force, corrosion etc, and to feel pains and the shape of objects. The development of skin-like sensors capable of sensing these stimuli is of great importance for various applications such as robots, touch detection, temperature monitoring, strain gauges etc. Great efforts have been made to develop high performance skin-like sensors, but they are far from perfect and much inferior to human skin as most of them can only sense one stimulus with focus on pressure (strain) or temperature, and are unable to visualize sensations and shape of objects. Here we report a skin-like sensor which imitates real skin with multiple receptors, and a new concept of pain sensation. The sensor with very high resolution not only has multiple sensations for touch, pressure, temperature, but also is able to sense various pains and reproduce the three dimensional shape of an object in contact.

Efficient Spatiotemporal Clutter Rejection and Nonlinear Filtering-based Dim Resolved and Unresolved Object Tracking Algorithms

NASA Astrophysics Data System (ADS)

Tartakovsky, A.; Tong, M.; Brown, A. P.; Agh, C.

2013-09-01

We develop efficient spatiotemporal image processing algorithms for rejection of non-stationary clutter and tracking of multiple dim objects using non-linear track-before-detect methods. For clutter suppression, we include an innovative image alignment (registration) algorithm. The images are assumed to contain elements of the same scene, but taken at different angles, from different locations, and at different times, with substantial clutter non-stationarity. These challenges are typical for space-based and surface-based IR/EO moving sensors, e.g., highly elliptical orbit or low earth orbit scenarios. The algorithm assumes that the images are related via a planar homography, also known as the projective transformation. The parameters are estimated in an iterative manner, at each step adjusting the parameter vector so as to achieve improved alignment of the images. Operating in the parameter space rather than in the coordinate space is a new idea, which makes the algorithm more robust with respect to noise as well as to large inter-frame disturbances, while operating at real-time rates. For dim object tracking, we include new advancements to a particle non-linear filtering-based track-before-detect (TrbD) algorithm. The new TrbD algorithm includes both real-time full image search for resolved objects not yet in track and joint super-resolution and tracking of individual objects in closely spaced object (CSO) clusters. The real-time full image search provides near-optimal detection and tracking of multiple extremely dim, maneuvering objects/clusters. The super-resolution and tracking CSO TrbD algorithm provides efficient near-optimal estimation of the number of unresolved objects in a CSO cluster, as well as the locations, velocities, accelerations, and intensities of the individual objects. We demonstrate that the algorithm is able to accurately estimate the number of CSO objects and their locations when the initial uncertainty on the number of objects is large. We demonstrate performance of the TrbD algorithm both for satellite-based and surface-based EO/IR surveillance scenarios.

Change detection from remotely sensed images: From pixel-based to object-based approaches

NASA Astrophysics Data System (ADS)

Hussain, Masroor; Chen, Dongmei; Cheng, Angela; Wei, Hui; Stanley, David

2013-06-01

The appetite for up-to-date information about earth's surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.

Value of FDG PET in the assessment of patients with multiple myeloma.

PubMed

Bredella, Miriam A; Steinbach, Lynne; Caputo, Gary; Segall, George; Hawkins, Randall

2005-04-01

Our objective was to evaluate if whole-body PET with FDG is able to detect bone marrow involvement in patients with multiple myeloma and to assess its appearance and distribution pattern. Seventeen whole-body FDG PET scans were performed in 13 patients with multiple myeloma. Four patients were referred for evaluation of extent of disease pretherapy and nine patients were referred for assessment of therapy response (chemotherapy, radiation therapy, bone marrow transplant). FDG PET images were evaluated for distribution and uptake pattern. Standardized uptake values were obtained to quantify FDG uptake. Results of other imaging examinations (MRI, CT, radiography), laboratory data, biopsies, and the clinical course were used for verification of detected lesions. FDG PET was able to detect medullary involvement of multiple myeloma. There were two false-negative results. In one patient, the radiographic skeletal survey showed subcentimeter lytic lesions within the ribs that were not detected on FDG PET and in the other patient, a lytic lesion detected on radiographs showed only mildly increased FDG uptake that was not identified prospectively. There was one false-positive FDG PET result in a patient who had undergone radiation therapy 3 weeks before PET. FDG PET was helpful in differentiating between posttherapeutic changes and residual/recurrent tumor and in assessing response to therapy. FDG PET resulted in upstaging of disease in four patients, which influenced subsequent management and prognosis. Sensitivity of FDG PET in detecting myelomatous involvement was 85% and specificity was 92%. FDG PET is able to detect bone marrow involvement in patients with multiple myeloma. FDG PET is useful in assessing extent of disease at time of initial diagnosis, contributing to staging that is more accurate. FDG PET is also useful for evaluating therapy response.

Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation

DTIC Science & Technology

2007-05-30

with large region of attraction about the true minimum. The physical optics models provide features for high confidence identification of stationary...the detection test are used to estimate 3D object scattering; multiple images can be noncoherently combined to reconstruct a more complete object...Proc. SPIE Algorithms for Synthetic Aper- ture Radar Imagery XIII, The International Society for Optical Engineering, April 2006. [40] K. Varshney, M. C

Remembered but Unused: The Accessory Items in Working Memory that Do Not Guide Attention

ERIC Educational Resources Information Center

Peters, Judith C.; Goebel, Rainer; Roelfsema, Pieter R.

2009-01-01

If we search for an item, a representation of this item in our working memory guides attention to matching items in the visual scene. We can hold multiple items in working memory. Do all these items guide attention in parallel? We asked participants to detect a target object in a stream of objects while they maintained a second item in memory for…

Detecting target changes in multiple object tracking with peripheral vision: More pronounced eccentricity effects for changes in form than in motion.

PubMed

Vater, Christian; Kredel, Ralf; Hossner, Ernst-Joachim

2017-05-01

In the current study, dual-task performance is examined with multiple-object tracking as a primary task and target-change detection as a secondary task. The to-be-detected target changes in conditions of either change type (form vs. motion; Experiment 1) or change salience (stop vs. slowdown; Experiment 2), with changes occurring at either near (5°-10°) or far (15°-20°) eccentricities (Experiments 1 and 2). The aim of the study was to test whether changes can be detected solely with peripheral vision. By controlling for saccades and computing gaze distances, we could show that participants used peripheral vision to monitor the targets and, additionally, to perceive changes at both near and far eccentricities. Noticeably, gaze behavior was not affected by the actual target change. Detection rates as well as response times generally varied as a function of change condition and eccentricity, with faster detections for motion changes and near changes. However, in contrast to the effects found for motion changes, sharp declines in detection rates and increased response times were observed for form changes as a function of the eccentricities. This result can be ascribed to properties of the visual system, namely to the limited spatial acuity in the periphery and the comparably receptive motion sensitivity of peripheral vision. These findings show that peripheral vision is functional for simultaneous target monitoring and target-change detection as saccadic information suppression can be avoided and covert attention can be optimally distributed to all targets. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Cortical mechanisms for the segregation and representation of acoustic textures.

PubMed

Overath, Tobias; Kumar, Sukhbinder; Stewart, Lauren; von Kriegstein, Katharina; Cusack, Rhodri; Rees, Adrian; Griffiths, Timothy D

2010-02-10

Auditory object analysis requires two fundamental perceptual processes: the definition of the boundaries between objects, and the abstraction and maintenance of an object's characteristic features. Although it is intuitive to assume that the detection of the discontinuities at an object's boundaries precedes the subsequent precise representation of the object, the specific underlying cortical mechanisms for segregating and representing auditory objects within the auditory scene are unknown. We investigated the cortical bases of these two processes for one type of auditory object, an "acoustic texture," composed of multiple frequency-modulated ramps. In these stimuli, we independently manipulated the statistical rules governing (1) the frequency-time space within individual textures (comprising ramps with a given spectrotemporal coherence) and (2) the boundaries between textures (adjacent textures with different spectrotemporal coherences). Using functional magnetic resonance imaging, we show mechanisms defining boundaries between textures with different coherences in primary and association auditory cortices, whereas texture coherence is represented only in association cortex. Furthermore, participants' superior detection of boundaries across which texture coherence increased (as opposed to decreased) was reflected in a greater neural response in auditory association cortex at these boundaries. The results suggest a hierarchical mechanism for processing acoustic textures that is relevant to auditory object analysis: boundaries between objects are first detected as a change in statistical rules over frequency-time space, before a representation that corresponds to the characteristics of the perceived object is formed.

Laser radiography forming bremsstrahlung radiation to image an object

DOEpatents

Perry, Michael D.; Sefcik, Joseph A.

2004-01-13

A method of imaging an object by generating laser pulses with a short-pulse, high-power laser. When the laser pulse strikes a conductive target, bremsstrahlung radiation is generated such that hard ballistic high-energy electrons are formed to penetrate an object. A detector on the opposite side of the object detects these electrons. Since laser pulses are used to form the hard x-rays, multiple pulses can be used to image an object in motion, such as an exploding or compressing object, by using time gated detectors. Furthermore, the laser pulses can be directed down different tubes using mirrors and filters so that each laser pulse will image a different portion of the object.

Behavioral and neurochemical consequences of multiple MDMA administrations in the rat: role of individual differences in anxiety-related behavior.

PubMed

Ludwig, V; Mihov, Y; Schwarting, R K W

2008-05-16

Using the elevated plus-maze (EPM), Wistar rats can be distinguished into high (HA) or low anxiety (LA) subjects. These differences seem to reflect traits, since HA and LA rats vary also in other anxiety-dependent tasks, neurochemical mechanisms, and psychopharmacological reactivity, including lasting consequences after single treatment with 3,4-methylenedioxymethamphetamine (MDMA). Here, we tested whether multiple MDMA treatments also have subject-dependent effects. Based on routine EPM screening, male Wistar rats were divided into HA and LA sub-groups, which received five (i.e. multiple) daily injections of MDMA (5 mg/kg) or saline, followed by a test battery, including a challenge test with MDMA, a retest in the EPM, a novel-object test, and a final neurochemical analysis. Acutely, MDMA led to comparable hyperactivity in HA and LA rats. After multiple MDMA, behavioral sensitization was observed, especially in LA rats. Open arm time during the EPM retest (min 0-5) correlated with that of the initial one only in those rats, which had received a single injection of MDMA. Rats with multiple MDMA, especially LA-rats, showed more open-arm time and locomotion during the subsequent 5-10 min of the retest. In a novel-object test, rats with multiple MDMA, again especially LA subjects, showed more exploratory bouts towards the novel object. Neurochemically, multiple MDMA led to moderately lower serotonin in the ventral striatum, and higher dopamine levels in the frontal cortex as compared to single MDMA; these effects were also moderated by subject-dependent factors. Our data show that low-dosed multiple MDMA can lead to behavioral sensitization and outlasting consequences, which affect behavior in the EPM and a novel object task. Detecting such sequels partly requires consideration of individual differences.

Automated multiple target detection and tracking in UAV videos

NASA Astrophysics Data System (ADS)

Mao, Hongwei; Yang, Chenhui; Abousleman, Glen P.; Si, Jennie

2010-04-01

In this paper, a novel system is presented to detect and track multiple targets in Unmanned Air Vehicles (UAV) video sequences. Since the output of the system is based on target motion, we first segment foreground moving areas from the background in each video frame using background subtraction. To stabilize the video, a multi-point-descriptor-based image registration method is performed where a projective model is employed to describe the global transformation between frames. For each detected foreground blob, an object model is used to describe its appearance and motion information. Rather than immediately classifying the detected objects as targets, we track them for a certain period of time and only those with qualified motion patterns are labeled as targets. In the subsequent tracking process, a Kalman filter is assigned to each tracked target to dynamically estimate its position in each frame. Blobs detected at a later time are used as observations to update the state of the tracked targets to which they are associated. The proposed overlap-rate-based data association method considers the splitting and merging of the observations, and therefore is able to maintain tracks more consistently. Experimental results demonstrate that the system performs well on real-world UAV video sequences. Moreover, careful consideration given to each component in the system has made the proposed system feasible for real-time applications.

A dynamical approach in exploring the unknown mass in the Solar system using pulsar timing arrays

NASA Astrophysics Data System (ADS)

Guo, Y. J.; Lee, K. J.; Caballero, R. N.

2018-04-01

The error in the Solar system ephemeris will lead to dipolar correlations in the residuals of pulsar timing array for widely separated pulsars. In this paper, we utilize such correlated signals, and construct a Bayesian data-analysis framework to detect the unknown mass in the Solar system and to measure the orbital parameters. The algorithm is designed to calculate the waveform of the induced pulsar-timing residuals due to the unmodelled objects following the Keplerian orbits in the Solar system. The algorithm incorporates a Bayesian-analysis suit used to simultaneously analyse the pulsar-timing data of multiple pulsars to search for coherent waveforms, evaluate the detection significance of unknown objects, and to measure their parameters. When the object is not detectable, our algorithm can be used to place upper limits on the mass. The algorithm is verified using simulated data sets, and cross-checked with analytical calculations. We also investigate the capability of future pulsar-timing-array experiments in detecting the unknown objects. We expect that the future pulsar-timing data can limit the unknown massive objects in the Solar system to be lighter than 10-11-10-12 M⊙, or measure the mass of Jovian system to a fractional precision of 10-8-10-9.

Simultaneous segmentation of the bone and cartilage surfaces of a knee joint in 3D

NASA Astrophysics Data System (ADS)

Yin, Y.; Zhang, X.; Anderson, D. D.; Brown, T. D.; Hofwegen, C. Van; Sonka, M.

2009-02-01

We present a novel framework for the simultaneous segmentation of multiple interacting surfaces belonging to multiple mutually interacting objects. The method is a non-trivial extension of our previously reported optimal multi-surface segmentation. Considering an example application of knee-cartilage segmentation, the framework consists of the following main steps: 1) Shape model construction: Building a mean shape for each bone of the joint (femur, tibia, patella) from interactively segmented volumetric datasets. Using the resulting mean-shape model - identification of cartilage, non-cartilage, and transition areas on the mean-shape bone model surfaces. 2) Presegmentation: Employment of iterative optimal surface detection method to achieve approximate segmentation of individual bone surfaces. 3) Cross-object surface mapping: Detection of inter-bone equidistant separating sheets to help identify corresponding vertex pairs for all interacting surfaces. 4) Multi-object, multi-surface graph construction and final segmentation: Construction of a single multi-bone, multi-surface graph so that two surfaces (bone and cartilage) with zero and non-zero intervening distances can be detected for each bone of the joint, according to whether or not cartilage can be locally absent or present on the bone. To define inter-object relationships, corresponding vertex pairs identified using the separating sheets were interlinked in the graph. The graph optimization algorithm acted on the entire multiobject, multi-surface graph to yield a globally optimal solution. The segmentation framework was tested on 16 MR-DESS knee-joint datasets from the Osteoarthritis Initiative database. The average signed surface positioning error for the 6 detected surfaces ranged from 0.00 to 0.12 mm. When independently initialized, the signed reproducibility error of bone and cartilage segmentation ranged from 0.00 to 0.26 mm. The results showed that this framework provides robust, accurate, and reproducible segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multi-object segmentation problems.

Salient object detection based on multi-scale contrast.

PubMed

Wang, Hai; Dai, Lei; Cai, Yingfeng; Sun, Xiaoqiang; Chen, Long

2018-05-01

Due to the development of deep learning networks, a salient object detection based on deep learning networks, which are used to extract the features, has made a great breakthrough compared to the traditional methods. At present, the salient object detection mainly relies on very deep convolutional network, which is used to extract the features. In deep learning networks, an dramatic increase of network depth may cause more training errors instead. In this paper, we use the residual network to increase network depth and to mitigate the errors caused by depth increase simultaneously. Inspired by image simplification, we use color and texture features to obtain simplified image with multiple scales by means of region assimilation on the basis of super-pixels in order to reduce the complexity of images and to improve the accuracy of salient target detection. We refine the feature on pixel level by the multi-scale feature correction method to avoid the feature error when the image is simplified at the above-mentioned region level. The final full connection layer not only integrates features of multi-scale and multi-level but also works as classifier of salient targets. The experimental results show that proposed model achieves better results than other salient object detection models based on original deep learning networks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Directed area search using socio-biological vision algorithms and cognitive Bayesian reasoning

NASA Astrophysics Data System (ADS)

Medasani, S.; Owechko, Y.; Allen, D.; Lu, T. C.; Khosla, D.

2010-04-01

Volitional search systems that assist the analyst by searching for specific targets or objects such as vehicles, factories, airports, etc in wide area overhead imagery need to overcome multiple problems present in current manual and automatic approaches. These problems include finding targets hidden in terabytes of information, relatively few pixels on targets, long intervals between interesting regions, time consuming analysis requiring many analysts, no a priori representative examples or templates of interest, detecting multiple classes of objects, and the need for very high detection rates and very low false alarm rates. This paper describes a conceptual analyst-centric framework that utilizes existing technology modules to search and locate occurrences of targets of interest (e.g., buildings, mobile targets of military significance, factories, nuclear plants, etc.), from video imagery of large areas. Our framework takes simple queries from the analyst and finds the queried targets with relatively minimum interaction from the analyst. It uses a hybrid approach that combines biologically inspired bottom up attention, socio-biologically inspired object recognition for volitionally recognizing targets, and hierarchical Bayesian networks for modeling and representing the domain knowledge. This approach has the benefits of high accuracy, low false alarm rate and can handle both low-level visual information and high-level domain knowledge in a single framework. Such a system would be of immense help for search and rescue efforts, intelligence gathering, change detection systems, and other surveillance systems.

Population trends of binary near-Earth asteroids based on radar and lightcurves observations

NASA Astrophysics Data System (ADS)

Brozovic, Marina; Benner, Lance A. M.; Naidu, Shantanu P.; Taylor, Patrick A.; Busch, Michael W.; Margot, Jean-Luc; Nolan, Michael C.; Howell, Ellen S.; Springmann, Alessondra; Giorgini, Jon D.; Shepard, Michael K.; Magri, Christopher; Richardson, James E.; Rivera-Valentin, Edgard G.; Rodriguez-Ford, Linda A.; Zambrano Marin, Luisa Fernanda

2016-10-01

The Arecibo and Goldstone planetary radars are invaluable instruments for the discovery and characterization of binary and triple asteroids in the near-Earth asteroid (NEA) population. To date, 41 out of 56 known binaries and triples (~73% of the objects) have been discovered by radar and 49 of these multiple systems have been detected by radar. Their absolute magnitudes range from 12.4 for (1866) Sisyphus to 22.6 for 2015 TD144 and have a mean and rms dispersion of 18.1+-2.0. There is a pronounced decrease in the abundance of binaries for absolute magnitudes H>20. One of the smallest binaries, 1994 CJ1, with an absolute magnitude H=21.4, is also the most accessible binary for a spacecraft rendezvous. Among 365 NEAs with H<22 (corresponding to diameters larger than ~ 140 m) detected by radar since 1999, ~13% have at least one companion. Two triple systems are known, (15391) 2001 SN263 and (136617) 1994 CC, but this is probably an underestimate due to low signal to noise ratios (SNRs) for many of the binary radar detections. Taxonomic classes have been reported for 41 out of 56 currently known multiple systems and some trends are starting to emerge: at least 50% of multiple asteroid systems are S, Sq, Q, or Sk, and at least 20% are optically dark (C, B, P, or U). Thirteen V-class NEAs have been observed by radar and six of them are binaries. Curiously, a comparable number of E-class objects have been detected by radar, but none is known to be a binary.

Exploring the feasibility of traditional image querying tasks for industrial radiographs

NASA Astrophysics Data System (ADS)

Bray, Iliana E.; Tsai, Stephany J.; Jimenez, Edward S.

2015-08-01

Although there have been great strides in object recognition with optical images (photographs), there has been comparatively little research into object recognition for X-ray radiographs. Our exploratory work contributes to this area by creating an object recognition system designed to recognize components from a related database of radiographs. Object recognition for radiographs must be approached differently than for optical images, because radiographs have much less color-based information to distinguish objects, and they exhibit transmission overlap that alters perceived object shapes. The dataset used in this work contained more than 55,000 intermixed radiographs and photographs, all in a compressed JPEG form and with multiple ways of describing pixel information. For this work, a robust and efficient system is needed to combat problems presented by properties of the X-ray imaging modality, the large size of the given database, and the quality of the images contained in said database. We have explored various pre-processing techniques to clean the cluttered and low-quality images in the database, and we have developed our object recognition system by combining multiple object detection and feature extraction methods. We present the preliminary results of the still-evolving hybrid object recognition system.

Effect of processing on recovery and variability associated with immunochemical analytical methods for multiple allergens in a single matrix: dark chocolate.

PubMed

Khuda, Sefat; Slate, Andrew; Pereira, Marion; Al-Taher, Fadwa; Jackson, Lauren; Diaz-Amigo, Carmen; Bigley, Elmer C; Whitaker, Thomas; Williams, Kristina

2012-05-02

Immunodetection of allergens in dark chocolate is complicated by interference from the chocolate components. The objectives of this study were to establish reference materials for detecting multiple allergens in dark chocolate and to determine the accuracy and precision of allergen detection by enzyme-linked immunosorbent assay (ELISA) before and after chocolate processing. Defatted peanut flour, whole egg powder, and spray-dried milk were added to melted chocolate at seven incurred levels and tempered for 4 h. Allergen concentrations were measured using commercial ELISA kits. Tempering decreased the detection of casein and β-lactoglobulin (BLG), but had no significant effect on the detection of peanut and egg. Total coefficients of variation were higher in tempered than untempered chocolate for casein and BLG, but total and analytical CVs were comparable for peanut and egg. These findings indicate that processing has a greater effect on recovery and variability of casein and BLG than peanut and egg detection in a dark chocolate matrix.

Deep learning

NASA Astrophysics Data System (ADS)

Lecun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-01

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Deep learning.

PubMed

LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-28

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Biased Competition in Visual Processing Hierarchies: A Learning Approach Using Multiple Cues.

PubMed

Gepperth, Alexander R T; Rebhan, Sven; Hasler, Stephan; Fritsch, Jannik

2011-03-01

In this contribution, we present a large-scale hierarchical system for object detection fusing bottom-up (signal-driven) processing results with top-down (model or task-driven) attentional modulation. Specifically, we focus on the question of how the autonomous learning of invariant models can be embedded into a performing system and how such models can be used to define object-specific attentional modulation signals. Our system implements bi-directional data flow in a processing hierarchy. The bottom-up data flow proceeds from a preprocessing level to the hypothesis level where object hypotheses created by exhaustive object detection algorithms are represented in a roughly retinotopic way. A competitive selection mechanism is used to determine the most confident hypotheses, which are used on the system level to train multimodal models that link object identity to invariant hypothesis properties. The top-down data flow originates at the system level, where the trained multimodal models are used to obtain space- and feature-based attentional modulation signals, providing biases for the competitive selection process at the hypothesis level. This results in object-specific hypothesis facilitation/suppression in certain image regions which we show to be applicable to different object detection mechanisms. In order to demonstrate the benefits of this approach, we apply the system to the detection of cars in a variety of challenging traffic videos. Evaluating our approach on a publicly available dataset containing approximately 3,500 annotated video images from more than 1 h of driving, we can show strong increases in performance and generalization when compared to object detection in isolation. Furthermore, we compare our results to a late hypothesis rejection approach, showing that early coupling of top-down and bottom-up information is a favorable approach especially when processing resources are constrained.

[Design and experiment of micro biochemical detector based on micro spectrometer].

PubMed

Yu, Qing-hua; Wen, Zhi-yu; Chen, Gang; Dai, Wei-wei; Liu, Nian-ci; Wu, Xin

2012-03-01

According to the requirements of rapid detection of important life parameters for the sick and wounded, a new micro bio-chemical detection configuration was proposed utilizing continuous spectroscopy analysis, which was founded on MOEMS and embedded technology. The configuration was developed as so much research work was carried out on the detecting objects and methods. Important parameters such as stray light, absorbance linearity, absorbance ratability, stability and temperature accuracy of the instrument were tested, which are all in good agreement with the design requirements. Clinic tests show that it can detect multiple life parameters quickly (Na+, GLU, Hb eg.).

Reflection symmetry detection using locally affine invariant edge correspondence.

PubMed

Wang, Zhaozhong; Tang, Zesheng; Zhang, Xiao

2015-04-01

Reflection symmetry detection receives increasing attentions in recent years. The state-of-the-art algorithms mainly use the matching of intensity-based features (such as the SIFT) within a single image to find symmetry axes. This paper proposes a novel approach by establishing the correspondence of locally affine invariant edge-based features, which are superior to the intensity based in the aspects that it is insensitive to illumination variations, and applicable to textureless objects. The locally affine invariance is achieved by simple linear algebra for efficient and robust computations, making the algorithm suitable for detections under object distortions like perspective projection. Commonly used edge detectors and a voting process are, respectively, used before and after the edge description and matching steps to form a complete reflection detection pipeline. Experiments are performed using synthetic and real-world images with both multiple and single reflection symmetry axis. The test results are compared with existing algorithms to validate the proposed method.

IAEA Sampling Plan

DOE Office of Scientific and Technical Information (OSTI.GOV)

Geist, William H.

2017-09-15

The objectives for this presentation are to describe the method that the IAEA uses to determine a sampling plan for nuclear material measurements; describe the terms detection probability and significant quantity; list the three nuclear materials measurement types; describe the sampling method applied to an item facility; and describe multiple method sampling.

Collision detection and modeling of rigid and deformable objects in laparoscopic simulator

NASA Astrophysics Data System (ADS)

Dy, Mary-Clare; Tagawa, Kazuyoshi; Tanaka, Hiromi T.; Komori, Masaru

2015-03-01

Laparoscopic simulators are viable alternatives for surgical training and rehearsal. Haptic devices can also be incorporated with virtual reality simulators to provide additional cues to the users. However, to provide realistic feedback, the haptic device must be updated by 1kHz. On the other hand, realistic visual cues, that is, the collision detection and deformation between interacting objects must be rendered at least 30 fps. Our current laparoscopic simulator detects the collision between a point on the tool tip, and on the organ surfaces, in which haptic devices are attached on actual tool tips for realistic tool manipulation. The triangular-mesh organ model is rendered using a mass spring deformation model, or finite element method-based models. In this paper, we investigated multi-point-based collision detection on the rigid tool rods. Based on the preliminary results, we propose a method to improve the collision detection scheme, and speed up the organ deformation reaction. We discuss our proposal for an efficient method to compute simultaneous multiple collision between rigid (laparoscopic tools) and deformable (organs) objects, and perform the subsequent collision response, with haptic feedback, in real-time.

Multiple-modality program for standoff detection of roadside hazards

NASA Astrophysics Data System (ADS)

Williams, Kathryn; Middleton, Seth; Close, Ryan; Luke, Robert H.; Suri, Rajiv

2016-05-01

The U.S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD) is executing a program to assess the performance of a variety of sensor modalities for standoff detection of roadside explosive hazards. The program objective is to identify an optimal sensor or combination of fused sensors to incorporate with autonomous detection algorithms into a system of systems for use in future route clearance operations. This paper provides an overview of the program, including a description of the sensors under consideration, sensor test events, and ongoing data analysis.

Perception of the average size of multiple objects in chimpanzees (Pan troglodytes).

PubMed

Imura, Tomoko; Kawakami, Fumito; Shirai, Nobu; Tomonaga, Masaki

2017-08-30

Humans can extract statistical information, such as the average size of a group of objects or the general emotion of faces in a crowd without paying attention to any individual object or face. To determine whether summary perception is unique to humans, we investigated the evolutional origins of this ability by assessing whether chimpanzees, which are closely related to humans, can also determine the average size of multiple visual objects. Five chimpanzees and 18 humans were able to choose the array in which the average size was larger, when presented with a pair of arrays, each containing 12 circles of different or the same sizes. Furthermore, both species were more accurate in judging the average size of arrays consisting of 12 circles of different or the same sizes than they were in judging the average size of arrays consisting of a single circle. Our findings could not be explained by the use of a strategy in which the chimpanzee detected the largest or smallest circle among those in the array. Our study provides the first evidence that chimpanzees can perceive the average size of multiple visual objects. This indicates that the ability to compute the statistical properties of a complex visual scene is not unique to humans, but is shared between both species. © 2017 The Authors.

Perception of the average size of multiple objects in chimpanzees (Pan troglodytes)

PubMed Central

Kawakami, Fumito; Shirai, Nobu; Tomonaga, Masaki

2017-01-01

Humans can extract statistical information, such as the average size of a group of objects or the general emotion of faces in a crowd without paying attention to any individual object or face. To determine whether summary perception is unique to humans, we investigated the evolutional origins of this ability by assessing whether chimpanzees, which are closely related to humans, can also determine the average size of multiple visual objects. Five chimpanzees and 18 humans were able to choose the array in which the average size was larger, when presented with a pair of arrays, each containing 12 circles of different or the same sizes. Furthermore, both species were more accurate in judging the average size of arrays consisting of 12 circles of different or the same sizes than they were in judging the average size of arrays consisting of a single circle. Our findings could not be explained by the use of a strategy in which the chimpanzee detected the largest or smallest circle among those in the array. Our study provides the first evidence that chimpanzees can perceive the average size of multiple visual objects. This indicates that the ability to compute the statistical properties of a complex visual scene is not unique to humans, but is shared between both species. PMID:28835550

Remembering complex objects in visual working memory: do capacity limits restrict objects or features?

PubMed

Hardman, Kyle O; Cowan, Nelson

2015-03-01

Visual working memory stores stimuli from our environment as representations that can be accessed by high-level control processes. This study addresses a longstanding debate in the literature about whether storage limits in visual working memory include a limit to the complexity of discrete items. We examined the issue with a number of change-detection experiments that used complex stimuli that possessed multiple features per stimulus item. We manipulated the number of relevant features of the stimulus objects in order to vary feature load. In all of our experiments, we found that increased feature load led to a reduction in change-detection accuracy. However, we found that feature load alone could not account for the results but that a consideration of the number of relevant objects was also required. This study supports capacity limits for both feature and object storage in visual working memory. PsycINFO Database Record (c) 2015 APA, all rights reserved.

High resolution multiple excitation spot optical microscopy

NASA Astrophysics Data System (ADS)

Dilipkumar, Shilpa; Mondal, Partha Pratim

2011-06-01

We propose fundamental improvements in three-dimensional (3D) resolution of multiple excitation spot optical microscopy. The excitation point spread function (PSF) is generated by two interfering counter-propagating depth-of-focus beams along the optical axis. Detection PSF is obtained by coherently interfering the emitted fluorescent light (collected by both the objectives) at the detector. System PSF shows upto 14-fold reduction in focal volume as compared to confocal, and almost 2-fold improvement in lateral resolution. Proposed PSF has the ability to simultaneously excite multiple 3D-spots of sub-femtoliter volume. Potential applications are in fluorescence microscopy and nanobioimaging.

Unveiling the AGN activity in multiple SMBH systems observed with XMM-Newton

NASA Astrophysics Data System (ADS)

De Rosa, A.

2017-10-01

In this talk we will present results from the MAGNA (Multiple AGN Activity) project focused on the detection and study of multiple supermassive black hole systems. We investigate the physical properties (accretion rate and local environment) of multiple AGN candidates in interacting systems with respect to isolated sources with the goal to understand the mechanisms that trigger AGN activity in different stages of galaxy mergers. We present the study performed with SDSS and XMM data sets of 4 AGN pairs at separations of 20-70 kpc. XMM data allowed us to detect and characterize the AGN in all systems, by measuring the accretion and absorption properties of the sources. In each system at least one object is highly obscured, possibly Compton-thick, in agreement with the hypothesis that galaxy encounters are effective in driving gas inflow. One system however manifests the opposite behaviour showing a pair composed from an unobscured type 1 AGN and a Compton Thick AGN. The talk will reflect on broader implications of these findings.

Image processing system and method for recognizing and removing shadows from the image of a monitored scene

DOEpatents

Osbourn, Gordon C.

1996-01-01

The shadow contrast sensitivity of the human vision system is simulated by configuring information obtained from an image sensor so that the information may be evaluated with multiple pixel widths in order to produce a machine vision system able to distinguish between shadow edges and abrupt object edges. A second difference of the image intensity for each line of the image is developed and this second difference is used to screen out high frequency noise contributions from the final edge detection signals. These edge detection signals are constructed from first differences of the image intensity where the screening conditions are satisfied. The positional coincidence of oppositely signed maxima in the first difference signal taken from the right and the second difference signal taken from the left is used to detect the presence of an object edge. Alternatively, the effective number of responding operators (ENRO) may be utilized to determine the presence of object edges.

Nonlocally sensing the magnetic states of nanoscale antiferromagnets with an atomic spin sensor

PubMed Central

Yan, Shichao; Malavolti, Luigi; Burgess, Jacob A. J.; Droghetti, Andrea; Rubio, Angel; Loth, Sebastian

2017-01-01

The ability to sense the magnetic state of individual magnetic nano-objects is a key capability for powerful applications ranging from readout of ultradense magnetic memory to the measurement of spins in complex structures with nanometer precision. Magnetic nano-objects require extremely sensitive sensors and detection methods. We create an atomic spin sensor consisting of three Fe atoms and show that it can detect nanoscale antiferromagnets through minute, surface-mediated magnetic interaction. Coupling, even to an object with no net spin and having vanishing dipolar stray field, modifies the transition matrix element between two spin states of the Fe atom–based spin sensor that changes the sensor’s spin relaxation time. The sensor can detect nanoscale antiferromagnets at up to a 3-nm distance and achieves an energy resolution of 10 μeV, surpassing the thermal limit of conventional scanning probe spectroscopy. This scheme permits simultaneous sensing of multiple antiferromagnets with a single-spin sensor integrated onto the surface. PMID:28560346

Disability and Fatigue Can Be Objectively Measured in Multiple Sclerosis.

PubMed

Motta, Caterina; Palermo, Eduardo; Studer, Valeria; Germanotta, Marco; Germani, Giorgio; Centonze, Diego; Cappa, Paolo; Rossi, Silvia; Rossi, Stefano

2016-01-01

The available clinical outcome measures of disability in multiple sclerosis are not adequately responsive or sensitive. To investigate the feasibility of inertial sensor-based gait analysis in multiple sclerosis. A cross-sectional study of 80 multiple sclerosis patients and 50 healthy controls was performed. Lower-limb kinematics was evaluated by using a commercially available magnetic inertial measurement unit system. Mean and standard deviation of range of motion (mROM, sROM) for each joint of lower limbs were calculated in one minute walking test. A motor performance index (E) defined as the sum of sROMs was proposed. We established two novel observer-independent measures of disability. Hip mROM was extremely sensitive in measuring lower limb motor impairment, being correlated with muscle strength and also altered in patients without clinically detectable disability. On the other hand, E index discriminated patients according to disability, being altered only in patients with moderate and severe disability, regardless of walking speed. It was strongly correlated with fatigue and patient-perceived health status. Inertial sensor-based gait analysis is feasible and can detect clinical and subclinical disability in multiple sclerosis.

Experimental Investigation on the Detection of Multiple Surface Cracks Using Vibrothermography with a Low-Power Piezoceramic Actuator.

PubMed

Xu, Changhang; Xie, Jing; Zhang, Wuyang; Kong, Qingzhao; Chen, Guoming; Song, Gangbing

2017-11-23

Vibrothermography often employs a high-power actuator to generate heat on a specimen to reveal damage, however, the high-power actuator brings inconvenience to the application and possibly introduces additional damage to the inspected objects. This study uses a low-power piezoceramic transducer as the actuator of vibrothermography and explores its ability to detect multiple surface cracks in a metal part. Experiments were conducted on a thin aluminum beam with three cracks in different orientations. Detailed analyses of both thermograms and temperature data are presented to validate the proposed vibrothermography method. To further investigate the performance of the proposed vibrothermography method, we experimentally studied the effects of several critical factors, including the amplitude of excitation signal, specimen constraints, relative position between the transducer and cracks (the transducer is mounted on the same or the opposite side with the cracks). The results demonstrate that all cracks can be detected conveniently and simultaneously by using the proposed low-power vibrothermography. We also found that the magnitude of excitation signal and the specimen constraints have a great influence on detection results. Combined with effective data processing methods, such as Fourier transformation employed in this study, the proposed method provides a promising potential to detect multiple cracks on a metal surface in a safe and effective manner.

Trace detection of explosives using an in-line high-volume sampler, preconcentrator, and Fido explosives detector

NASA Astrophysics Data System (ADS)

Ingram, Russ; Sikes, John

2010-04-01

This paper shall demonstrate the results of a prototype system to detect explosive objects and obscured contaminated targets. By combining a high volume sampling nozzle with an inline 2-stage preconcentrator and a Fido, greater standoff is achieved than with the Fido alone. The direct application of this system is on the Autonomous Mine Detection System (AMDS) but could be deployed on a large variety of robotic platforms. It is being developed under the auspices of the U.S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate, Countermine Division. This device is one of several detection tools and technologies to be used on the AMDS. These systems will have multiple, and at times, overlapping objectives. One objective is trace detection on the surface of an unknown potential target. By increasing the standoff capabilities of the detector, the fine manipulation of the robot deploying the detector is less critical. Current detectors used on robotic systems must either be directly in the vapor plume or make direct contact with the target. By increasing the standoff, detection is more easily and quickly achieved. The end result detector must overcome cross-contamination, sample throughput, and environmental issues. The paper will provide preliminary results of the prototype system to include data, and where feasible, video of testing results.

Kruger 60

NASA Astrophysics Data System (ADS)

Knapp, Wilfried; Nanson, John

2018-01-01

As announced in our report “Measurements of WDS Objects found in images taken for detecting CPM pairs in the LSPM catalog” we present here a report on Kruger 60. This multiple is listed in WDS with such a large number of components that we thought it deserves a separate report.

Speckle Interferometry at the Blanco and SOAR Telescopes in 2008 and 2009

NASA Technical Reports Server (NTRS)

Tokovinin, Andrei; Mason, Brian D.; Hartkopf, William I.

2010-01-01

The results of speckle interferometric measurements of binary and multiple stars conducted in 2008 and 2009 at the Blanco and Southern Astrophysical Research (SOAR) 4 m telescopes in Chile are presented. A tot al of 1898 measurements of 1189 resolved pairs or sub-systems and 394 observations of 285 un-resolved targets are listed. We resolved for the first time 48 new pairs, 21 of which are new sub-systems in close visual multiple stars. Typical internal measurement precision is 0.3 mas in both coordinates, typical companion detection capability is delta m approximately 4.2 at 0.15 degree separation. These data were obtained with a new electron-multiplication CCD camera; data processing is described in detail, including estimation of magnitude difference, observational errors, detection limits, and analysis of artifacts. We comment on some newly discovered pairs and objects of special interest.

SPECKLE INTERFEROMETRY AT THE BLANCO AND SOAR TELESCOPES IN 2008 AND 2009

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tokovinin, Andrei; Mason, Brian D.; Hartkopf, William I.

2010-02-15

The results of speckle interferometric measurements of binary and multiple stars conducted in 2008 and 2009 at the Blanco and SOAR 4 m telescopes in Chile are presented. A total of 1898 measurements of 1189 resolved pairs or sub-systems and 394 observations of 285 un-resolved targets are listed. We resolved for the first time 48 new pairs, 21 of which are new sub-systems in close visual multiple stars. Typical internal measurement precision is 0.3 mas in both coordinates, typical companion detection capability is {delta}m {approx} 4.2 at 0.''15 separation. These data were obtained with a new electron-multiplication CCD camera; datamore » processing is described in detail, including estimation of magnitude difference, observational errors, detection limits, and analysis of artifacts. We comment on some newly discovered pairs and objects of special interest.« less

Semantic memory retrieval circuit: role of pre-SMA, caudate, and thalamus.

PubMed

Hart, John; Maguire, Mandy J; Motes, Michael; Mudar, Raksha Anand; Chiang, Hsueh-Sheng; Womack, Kyle B; Kraut, Michael A

2013-07-01

We propose that pre-supplementary motor area (pre-SMA)-thalamic interactions govern processes fundamental to semantic retrieval of an integrated object memory. At the onset of semantic retrieval, pre-SMA initiates electrical interactions between multiple cortical regions associated with semantic memory subsystems encodings as indexed by an increase in theta-band EEG power. This starts between 100-150 ms after stimulus presentation and is sustained throughout the task. We posit that this activity represents initiation of the object memory search, which continues in searching for an object memory. When the correct memory is retrieved, there is a high beta-band EEG power increase, which reflects communication between pre-SMA and thalamus, designates the end of the search process and resultant in object retrieval from multiple semantic memory subsystems. This high beta signal is also detected in cortical regions. This circuit is modulated by the caudate nuclei to facilitate correct and suppress incorrect target memories. Copyright © 2012 Elsevier Inc. All rights reserved.

Linear high-boost fusion of Stokes vector imagery for effective discrimination and recognition of real targets in the presence of multiple identical decoys

NASA Astrophysics Data System (ADS)

El-Saba, Aed; Sakla, Wesam A.

2010-04-01

Recently, the use of imaging polarimetry has received considerable attention for use in automatic target recognition (ATR) applications. In military remote sensing applications, there is a great demand for sensors that are capable of discriminating between real targets and decoys. Accurate discrimination of decoys from real targets is a challenging task and often requires the fusion of various sensor modalities that operate simultaneously. In this paper, we use a simple linear fusion technique known as the high-boost fusion method for effective discrimination of real targets in the presence of multiple decoys. The HBF assigns more weight to the polarization-based imagery in forming the final fused image that is used for detection. We have captured both intensity and polarization-based imagery from an experimental laboratory arrangement containing a mixture of sand/dirt, rocks, vegetation, and other objects for the purpose of simulating scenery that would be acquired in a remote sensing military application. A target object and three decoys that are identical in physical appearance (shape, surface structure and color) and different in material composition have also been placed in the scene. We use the wavelet-filter joint transform correlation (WFJTC) technique to perform detection between input scenery and the target object. Our results show that use of the HBF method increases the correlation performance metrics associated with the WFJTC-based detection process when compared to using either the traditional intensity or polarization-based images.

Gravitational lensing by a smoothly variable surface mass density

NASA Technical Reports Server (NTRS)

Paczynski, Bohdan; Wambsganss, Joachim

1989-01-01

The statistical properties of gravitational lensing due to smooth but nonuniform distributions of matter are considered. It is found that a majority of triple images had a parity characteristic for 'shear-induced' lensing. Almost all cases of triple or multiple imaging were associated with large surface density enhancements, and lensing objects were present between the images. Thus, the observed gravitational lens candidates for which no lensing object has been detected between the images are unlikely to be a result of asymmetric distribution of mass external to the image circle. In a model with smoothly variable surface mass density, moderately and highly amplified images tended to be single rather than multiple. An opposite trend was found in models which had singularities in the surface mass distribution.

A system for learning statistical motion patterns.

PubMed

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

Approach to explosive hazard detection using sensor fusion and multiple kernel learning with downward-looking GPR and EMI sensor data

NASA Astrophysics Data System (ADS)

Pinar, Anthony; Masarik, Matthew; Havens, Timothy C.; Burns, Joseph; Thelen, Brian; Becker, John

2015-05-01

This paper explores the effectiveness of an anomaly detection algorithm for downward-looking ground penetrating radar (GPR) and electromagnetic inductance (EMI) data. Threat detection with GPR is challenged by high responses to non-target/clutter objects, leading to a large number of false alarms (FAs), and since the responses of target and clutter signatures are so similar, classifier design is not trivial. We suggest a method based on a Run Packing (RP) algorithm to fuse GPR and EMI data into a composite confidence map to improve detection as measured by the area-under-ROC (NAUC) metric. We examine the value of a multiple kernel learning (MKL) support vector machine (SVM) classifier using image features such as histogram of oriented gradients (HOG), local binary patterns (LBP), and local statistics. Experimental results on government furnished data show that use of our proposed fusion and classification methods improves the NAUC when compared with the results from individual sensors and a single kernel SVM classifier.

Advances in quantum cascade lasers for security and crime-fighting

NASA Astrophysics Data System (ADS)

Normand, Erwan L.; Stokes, Robert J.; Hay, Kenneth; Foulger, Brian; Lewis, Colin

2010-10-01

Advances in the application of Quantum Cascade Lasers (QCL) to trace gas detection will be presented. The solution is real time (~1 μsec per scan), is insensitive to turbulence and vibration, and performs multiple measurements in one sweep. The QCL provides a large dynamic range, which is a linear response from ppt to % level. The concentration can be derived with excellent immunity from cross interference. Point sensing sensors developed by Cascade for home made and commercial explosives operate by monitoring key constituents in real time and matching this to a spatial event (i.e. sniffer device placed close to an object or person walking through portal (overt or covert). Programmable signature detection capability allows for detection of multiple chemical compounds along the most likely array of explosive chemical formulation. The advantages of configuration as "point sensing" or "stand off" will be discussed. In addition to explosives this method is highly applicable to the detection of mobile drugs labs through volatile chemical release.

Distributed proximity sensor system having embedded light emitters and detectors

NASA Technical Reports Server (NTRS)

Lee, Sukhan (Inventor)

1990-01-01

A distributed proximity sensor system is provided with multiple photosensitive devices and light emitters embedded on the surface of a robot hand or other moving member in a geometric pattern. By distributing sensors and emitters capable of detecting distances and angles to points on the surface of an object from known points in the geometric pattern, information is obtained for achieving noncontacting shape and distance perception, i.e., for automatic determination of the object's shape, direction and distance, as well as the orientation of the object relative to the robot hand or other moving member.

Investigating prior probabilities in a multiple hypothesis test for use in space domain awareness

NASA Astrophysics Data System (ADS)

Hardy, Tyler J.; Cain, Stephen C.

2016-05-01

The goal of this research effort is to improve Space Domain Awareness (SDA) capabilities of current telescope systems through improved detection algorithms. Ground-based optical SDA telescopes are often spatially under-sampled, or aliased. This fact negatively impacts the detection performance of traditionally proposed binary and correlation-based detection algorithms. A Multiple Hypothesis Test (MHT) algorithm has been previously developed to mitigate the effects of spatial aliasing. This is done by testing potential Resident Space Objects (RSOs) against several sub-pixel shifted Point Spread Functions (PSFs). A MHT has been shown to increase detection performance for the same false alarm rate. In this paper, the assumption of a priori probability used in a MHT algorithm is investigated. First, an analysis of the pixel decision space is completed to determine alternate hypothesis prior probabilities. These probabilities are then implemented into a MHT algorithm, and the algorithm is then tested against previous MHT algorithms using simulated RSO data. Results are reported with Receiver Operating Characteristic (ROC) curves and probability of detection, Pd, analysis.

Developmental changes in distinguishing concurrent auditory objects.

PubMed

Alain, Claude; Theunissen, Eef L; Chevalier, Hélène; Batty, Magali; Taylor, Margot J

2003-04-01

Children have considerable difficulties in identifying speech in noise. In the present study, we examined age-related differences in central auditory functions that are crucial for parsing co-occurring auditory events using behavioral and event-related brain potential measures. Seventeen pre-adolescent children and 17 adults were presented with complex sounds containing multiple harmonics, one of which could be 'mistuned' so that it was no longer an integer multiple of the fundamental. Both children and adults were more likely to report hearing the mistuned harmonic as a separate sound with an increase in mistuning. However, children were less sensitive in detecting mistuning across all levels as revealed by lower d' scores than adults. The perception of two concurrent auditory events was accompanied by a negative wave that peaked at about 160 ms after sound onset. In both age groups, the negative wave, referred to as the 'object-related negativity' (ORN), increased in amplitude with mistuning. The ORN was larger in children than in adults despite a lower d' score. Together, the behavioral and electrophysiological results suggest that concurrent sound segregation is probably adult-like in pre-adolescent children, but that children are inefficient in processing the information following the detection of mistuning. These findings also suggest that processes involved in distinguishing concurrent auditory objects continue to mature during adolescence.

Detecting Target Objects by Natural Language Instructions Using an RGB-D Camera

PubMed Central

Bao, Jiatong; Jia, Yunyi; Cheng, Yu; Tang, Hongru; Xi, Ning

2016-01-01

Controlling robots by natural language (NL) is increasingly attracting attention for its versatility, convenience and no need of extensive training for users. Grounding is a crucial challenge of this problem to enable robots to understand NL instructions from humans. This paper mainly explores the object grounding problem and concretely studies how to detect target objects by the NL instructions using an RGB-D camera in robotic manipulation applications. In particular, a simple yet robust vision algorithm is applied to segment objects of interest. With the metric information of all segmented objects, the object attributes and relations between objects are further extracted. The NL instructions that incorporate multiple cues for object specifications are parsed into domain-specific annotations. The annotations from NL and extracted information from the RGB-D camera are matched in a computational state estimation framework to search all possible object grounding states. The final grounding is accomplished by selecting the states which have the maximum probabilities. An RGB-D scene dataset associated with different groups of NL instructions based on different cognition levels of the robot are collected. Quantitative evaluations on the dataset illustrate the advantages of the proposed method. The experiments of NL controlled object manipulation and NL-based task programming using a mobile manipulator show its effectiveness and practicability in robotic applications. PMID:27983604

Detection of Salmonella serotypes by overnight incubation of entire broiler carcass

USDA-ARS?s Scientific Manuscript database

Salmonella is a human bacterial pathogen that has been associated with poultry and poultry products. There are multiple ways to sample broiler chicken carcasses for the prevalence of Salmonella. A common method in the USA is a whole carcass rinse and culture of an aliquot of the rinse. The object...

Evidence of biotic resistance to invasions in forests of the Eastern USA

Treesearch

Basil V. Iannone III; Kevin M. Potter; Kelly-Ann Dixon Hamil; Whitney Huang; Hao Zhang; Qinfeng Guo; Christopher M. Oswalt; Christopher W. Woodall; Songlin Fei

2016-01-01

Context Detecting biotic resistance to biological invasions across large geographic areas may require acknowledging multiple metrics of niche usage and potential spatial heterogeneity in associations between invasive and native species diversity and dominance.Objectives Determine (1) if native communities are ...

Sensors Umbra Package v 1.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oppel, Fred J.; Hart, Brian E.; Whitford, Gregg Douglas

2016-08-25

This package contains modules that model sensors in Umbra. There is a mix of modalities for both accumulating and tracking energy sensors: seismic, magnetic, and radiation. Some modules fuss information from multiple sensor types. Sensor devices (e.g., seismic sensors), detect objects such as people and vehicles that have sensor properties attached (e.g., seismic properties).

Detection and quantification of MS lesions using fuzzy topological principles

NASA Astrophysics Data System (ADS)

Udupa, Jayaram K.; Wei, Luogang; Samarasekera, Supun; Miki, Yukio; van Buchem, M. A.; Grossman, Robert I.

1996-04-01

Quantification of the severity of the multiple sclerosis (MS) disease through estimation of lesion volume via MR imaging is vital for understanding and monitoring the disease and its treatment. This paper presents a novel methodology and a system that can be routinely used for segmenting and estimating the volume of MS lesions via dual-echo spin-echo MR imagery. An operator indicates a few points in the images by pointing to the white matter, the gray matter, and the CSF. Each of these objects is then detected as a fuzzy connected set. The holes in the union of these objects correspond to potential lesion sites which are utilized to detect each potential lesion as a fuzzy connected object. These 3D objects are presented to the operator who indicates acceptance/rejection through the click of a mouse button. The volume of accepted lesions is then computed and output. Based on several evaluation studies and over 300 3D data sets that were processed, we conclude that the methodology is highly reliable and consistent, with a coefficient of variation (due to subjective operator actions) of less than 1.0% for volume.

Safe trajectory estimation at a pedestrian crossing to assist visually impaired people.

PubMed

Alghamdi, Saleh; van Schyndel, Ron; Khalil, Ibrahim

2012-01-01

The aim of this paper is to present a service for blind and people with low vision to assist them to cross the street independently. The presented approach provides the user with significant information such as detection of pedestrian crossing signal from any point of view, when the pedestrian crossing signal light is green, the detection of dynamic and fixed obstacles, predictions of the movement of fellow pedestrians and information on objects which may intersect his path. Our approach is based on capturing multiple frames using a depth camera which is attached to a user's headgear. Currently a testbed system is built on a helmet and is connected to a laptop in the user's backpack. In this paper, we discussed efficiency of using Speeded-Up Robust Features (SURF) algorithm for object recognition for purposes of blind people assistance. The system predicts the movement of objects of interest to provide the user with information on the safest path to navigate and information on the surrounding area. Evaluation of this approach on real sequence video frames provides 90% of human detection and more than 80% for recognition of other related objects.

Interactive object modelling based on piecewise planar surface patches.

PubMed

Prankl, Johann; Zillich, Michael; Vincze, Markus

2013-06-01

Detecting elements such as planes in 3D is essential to describe objects for applications such as robotics and augmented reality. While plane estimation is well studied, table-top scenes exhibit a large number of planes and methods often lock onto a dominant plane or do not estimate 3D object structure but only homographies of individual planes. In this paper we introduce MDL to the problem of incrementally detecting multiple planar patches in a scene using tracked interest points in image sequences. Planar patches are reconstructed and stored in a keyframe-based graph structure. In case different motions occur, separate object hypotheses are modelled from currently visible patches and patches seen in previous frames. We evaluate our approach on a standard data set published by the Visual Geometry Group at the University of Oxford [24] and on our own data set containing table-top scenes. Results indicate that our approach significantly improves over the state-of-the-art algorithms.

Evaluating structural pattern recognition for handwritten math via primitive label graphs

NASA Astrophysics Data System (ADS)

Zanibbi, Richard; Mouchère, Harold; Viard-Gaudin, Christian

2013-01-01

Currently, structural pattern recognizer evaluations compare graphs of detected structure to target structures (i.e. ground truth) using recognition rates, recall and precision for object segmentation, classification and relationships. In document recognition, these target objects (e.g. symbols) are frequently comprised of multiple primitives (e.g. connected components, or strokes for online handwritten data), but current metrics do not characterize errors at the primitive level, from which object-level structure is obtained. Primitive label graphs are directed graphs defined over primitives and primitive pairs. We define new metrics obtained by Hamming distances over label graphs, which allow classification, segmentation and parsing errors to be characterized separately, or using a single measure. Recall and precision for detected objects may also be computed directly from label graphs. We illustrate the new metrics by comparing a new primitive-level evaluation to the symbol-level evaluation performed for the CROHME 2012 handwritten math recognition competition. A Python-based set of utilities for evaluating, visualizing and translating label graphs is publicly available.

Interactive object modelling based on piecewise planar surface patches☆

PubMed Central

Prankl, Johann; Zillich, Michael; Vincze, Markus

2013-01-01

Detecting elements such as planes in 3D is essential to describe objects for applications such as robotics and augmented reality. While plane estimation is well studied, table-top scenes exhibit a large number of planes and methods often lock onto a dominant plane or do not estimate 3D object structure but only homographies of individual planes. In this paper we introduce MDL to the problem of incrementally detecting multiple planar patches in a scene using tracked interest points in image sequences. Planar patches are reconstructed and stored in a keyframe-based graph structure. In case different motions occur, separate object hypotheses are modelled from currently visible patches and patches seen in previous frames. We evaluate our approach on a standard data set published by the Visual Geometry Group at the University of Oxford [24] and on our own data set containing table-top scenes. Results indicate that our approach significantly improves over the state-of-the-art algorithms. PMID:24511219

Detection, Identification, Location, and Remote Sensing Using SAW RFID Sensor Tags

NASA Technical Reports Server (NTRS)

Barton, Richard J.; Kennedy, Timothy F.; Williams, Robert M.; Fink, Patrick W.; Ngo, Phong H.

2009-01-01

The Electromagnetic Systems Branch (EV4) of the Avionic Systems Division at NASA Johnson Space Center in Houston, TX is studying the utility of surface acoustic wave (SAW) radiofrequency identification (RFID) tags for multiple wireless applications including detection, identification, tracking, and remote sensing of objects on the lunar surface, monitoring of environmental test facilities, structural shape and health monitoring, and nondestructive test and evaluation of assets. For all of these applications, it is anticipated that the system utilized to interrogate the SAW RFID tags may need to operate at fairly long range and in the presence of considerable multipath and multiple-access interference. Towards that end, EV4 is developing a prototype SAW RFID wireless interrogation system for use in such environments called the Passive Adaptive RFID Sensor Equipment (PARSED) system. The system utilizes a digitally beam-formed planar receiving antenna array to extend range and provide direction-of-arrival information coupled with an approximate maximum-likelihood signal processing algorithm to provide near-optimal estimation of both range and temperature. The system is capable of forming a large number of beams within the field of view and resolving the information from several tags within each beam. The combination of both spatial and waveform discrimination provides the capability to track and monitor telemetry from a large number of objects appearing simultaneously within the field of view of the receiving array. In this paper, we will consider the application of the PARSEQ system to the problem of simultaneous detection, identification, localization, and temperature estimation for multiple objects. We will summarize the overall design of the PARSEQ system and present a detailed description of the design and performance of the signal detection and estimation algorithms incorporated in the system. The system is currently configured only to measure temperature (jointly with range and tag ID), but future versions will be revised to measure parameters other than temperature as SAW tags capable of interfacing with external sensors become available. It is anticipated that the estimation of arbitrary parameters measured using SAW-based sensors will be based on techniques very similar to the joint range and temperature estimation techniques described in this paper.

Spatial and temporal coherence in perceptual binding

PubMed Central

Blake, Randolph; Yang, Yuede

1997-01-01

Component visual features of objects are registered by distributed patterns of activity among neurons comprising multiple pathways and visual areas. How these distributed patterns of activity give rise to unified representations of objects remains unresolved, although one recent, controversial view posits temporal coherence of neural activity as a binding agent. Motivated by the possible role of temporal coherence in feature binding, we devised a novel psychophysical task that requires the detection of temporal coherence among features comprising complex visual images. Results show that human observers can more easily detect synchronized patterns of temporal contrast modulation within hybrid visual images composed of two components when those components are drawn from the same original picture. Evidently, time-varying changes within spatially coherent features produce more salient neural signals. PMID:9192701

Detecting submerged objects: the application of side scan sonar to forensic contexts.

PubMed

Schultz, John J; Healy, Carrie A; Parker, Kenneth; Lowers, Bim

2013-09-10

Forensic personnel must deal with numerous challenges when searching for submerged objects. While traditional water search methods have generally involved using dive teams, remotely operated vehicles (ROVs), and water scent dogs for cases involving submerged objects and bodies, law enforcement is increasingly integrating multiple methods that include geophysical technologies. There are numerous advantages for integrating geophysical technologies, such as side scan sonar and ground penetrating radar (GPR), with more traditional search methods. Overall, these methods decrease the time involved searching, in addition to increasing area searched. However, as with other search methods, there are advantages and disadvantages when using each method. For example, in instances with excessive aquatic vegetation or irregular bottom terrain, it may not be possible to discern a submersed body with side scan sonar. As a result, forensic personnel will have the highest rate of success during searches for submerged objects when integrating multiple search methods, including deploying multiple geophysical technologies. The goal of this paper is to discuss the methodology of various search methods that are employed for submerged objects and how these various methods can be integrated as part of a comprehensive protocol for water searches depending upon the type of underwater terrain. In addition, two successful case studies involving the search and recovery of a submerged human body using side scan sonar are presented to illustrate the successful application of integrating a geophysical technology with divers when searching for a submerged object. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

THE RADIO JET ASSOCIATED WITH THE MULTIPLE V380 ORI SYSTEM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rodríguez, Luis F.; Yam, J. Omar; Carrasco-González, Carlos

The giant Herbig–Haro object 222 extends over ∼6′ in the plane of the sky, with a bow shock morphology. The identification of its exciting source has remained uncertain over the years. A non-thermal radio source located at the core of the shock structure was proposed to be the exciting source. However, Very Large Array studies showed that the radio source has a clear morphology of radio galaxy and a lack of flux variations or proper motions, favoring an extragalactic origin. Recently, an optical–IR study proposed that this giant HH object is driven by the multiple stellar system V380 Ori, locatedmore » about 23′ to the SE of HH 222. The exciting sources of HH systems are usually detected as weak free–free emitters at centimeter wavelengths. Here, we report the detection of an elongated radio source associated with the Herbig Be star or with its close infrared companion in the multiple V380 Ori system. This radio source has the characteristics of a thermal radio jet and is aligned with the direction of the giant outflow defined by HH 222 and its suggested counterpart to the SE, HH 1041. We propose that this radio jet traces the origin of the large scale HH outflow. Assuming that the jet arises from the Herbig Be star, the radio luminosity is a few times smaller than the value expected from the radio–bolometric correlation for radio jets, confirming that this is a more evolved object than those used to establish the correlation.« less

Estimating weak ratiometric signals in imaging data. II. Meta-analysis with multiple, dual-channel datasets.

PubMed

Sornborger, Andrew; Broder, Josef; Majumder, Anirban; Srinivasamoorthy, Ganesh; Porter, Erika; Reagin, Sean S; Keith, Charles; Lauderdale, James D

2008-09-01

Ratiometric fluorescent indicators are used for making quantitative measurements of a variety of physiological variables. Their utility is often limited by noise. This is the second in a series of papers describing statistical methods for denoising ratiometric data with the aim of obtaining improved quantitative estimates of variables of interest. Here, we outline a statistical optimization method that is designed for the analysis of ratiometric imaging data in which multiple measurements have been taken of systems responding to the same stimulation protocol. This method takes advantage of correlated information across multiple datasets for objectively detecting and estimating ratiometric signals. We demonstrate our method by showing results of its application on multiple, ratiometric calcium imaging experiments.

Toward a Computer Vision-based Wayfinding Aid for Blind Persons to Access Unfamiliar Indoor Environments.

PubMed

Tian, Yingli; Yang, Xiaodong; Yi, Chucai; Arditi, Aries

2013-04-01

Independent travel is a well known challenge for blind and visually impaired persons. In this paper, we propose a proof-of-concept computer vision-based wayfinding aid for blind people to independently access unfamiliar indoor environments. In order to find different rooms (e.g. an office, a lab, or a bathroom) and other building amenities (e.g. an exit or an elevator), we incorporate object detection with text recognition. First we develop a robust and efficient algorithm to detect doors, elevators, and cabinets based on their general geometric shape, by combining edges and corners. The algorithm is general enough to handle large intra-class variations of objects with different appearances among different indoor environments, as well as small inter-class differences between different objects such as doors and door-like cabinets. Next, in order to distinguish intra-class objects (e.g. an office door from a bathroom door), we extract and recognize text information associated with the detected objects. For text recognition, we first extract text regions from signs with multiple colors and possibly complex backgrounds, and then apply character localization and topological analysis to filter out background interference. The extracted text is recognized using off-the-shelf optical character recognition (OCR) software products. The object type, orientation, location, and text information are presented to the blind traveler as speech.

Toward a Computer Vision-based Wayfinding Aid for Blind Persons to Access Unfamiliar Indoor Environments

PubMed Central

Tian, YingLi; Yang, Xiaodong; Yi, Chucai; Arditi, Aries

2012-01-01

Independent travel is a well known challenge for blind and visually impaired persons. In this paper, we propose a proof-of-concept computer vision-based wayfinding aid for blind people to independently access unfamiliar indoor environments. In order to find different rooms (e.g. an office, a lab, or a bathroom) and other building amenities (e.g. an exit or an elevator), we incorporate object detection with text recognition. First we develop a robust and efficient algorithm to detect doors, elevators, and cabinets based on their general geometric shape, by combining edges and corners. The algorithm is general enough to handle large intra-class variations of objects with different appearances among different indoor environments, as well as small inter-class differences between different objects such as doors and door-like cabinets. Next, in order to distinguish intra-class objects (e.g. an office door from a bathroom door), we extract and recognize text information associated with the detected objects. For text recognition, we first extract text regions from signs with multiple colors and possibly complex backgrounds, and then apply character localization and topological analysis to filter out background interference. The extracted text is recognized using off-the-shelf optical character recognition (OCR) software products. The object type, orientation, location, and text information are presented to the blind traveler as speech. PMID:23630409

Object detection system using SPAD proximity detectors

NASA Astrophysics Data System (ADS)

Stark, Laurence; Raynor, Jeffrey M.; Henderson, Robert K.

2011-10-01

This paper presents an object detection system based upon the use of multiple single photon avalanche diode (SPAD) proximity sensors operating upon the time-of-flight (ToF) principle, whereby the co-ordinates of a target object in a coordinate system relative to the assembly are calculated. The system is similar to a touch screen system in form and operation except that the lack of requirement of a physical sensing surface provides a novel advantage over most existing touch screen technologies. The sensors are controlled by FPGA-based firmware and each proximity sensor in the system measures the range from the sensor to the target object. A software algorithm is implemented to calculate the x-y coordinates of the target object based on the distance measurements from at least two separate sensors and the known relative positions of these sensors. Existing proximity sensors were capable of determining the distance to an object with centimetric accuracy and were modified to obtain a wide field of view in the x-y axes with low beam angle in z in order to provide a detection area as large as possible. Design and implementation of the firmware, electronic hardware, mechanics and optics are covered in the paper. Possible future work would include characterisation with alternative designs of proximity sensors, as this is the component which determines the highest achievable accur1acy of the system.

Time-resolved non-sequential ray-tracing modelling of non-line-of-sight picosecond pulse LIDAR

NASA Astrophysics Data System (ADS)

Sroka, Adam; Chan, Susan; Warburton, Ryan; Gariepy, Genevieve; Henderson, Robert; Leach, Jonathan; Faccio, Daniele; Lee, Stephen T.

2016-05-01

The ability to detect motion and to track a moving object that is hidden around a corner or behind a wall provides a crucial advantage when physically going around the obstacle is impossible or dangerous. One recently demonstrated approach to achieving this goal makes use of non-line-of-sight picosecond pulse laser ranging. This approach has recently become interesting due to the availability of single-photon avalanche diode (SPAD) receivers with picosecond time resolution. We present a time-resolved non-sequential ray-tracing model and its application to indirect line-of-sight detection of moving targets. The model makes use of the Zemax optical design programme's capabilities in stray light analysis where it traces large numbers of rays through multiple random scattering events in a 3D non-sequential environment. Our model then reconstructs the generated multi-segment ray paths and adds temporal analysis. Validation of this model against experimental results is shown. We then exercise the model to explore the limits placed on system design by available laser sources and detectors. In particular we detail the requirements on the laser's pulse energy, duration and repetition rate, and on the receiver's temporal response and sensitivity. These are discussed in terms of the resulting implications for achievable range, resolution and measurement time while retaining eye-safety with this technique. Finally, the model is used to examine potential extensions to the experimental system that may allow for increased localisation of the position of the detected moving object, such as the inclusion of multiple detectors and/or multiple emitters.

MUSIC algorithm DoA estimation for cooperative node location in mobile ad hoc networks

NASA Astrophysics Data System (ADS)

Warty, Chirag; Yu, Richard Wai; ElMahgoub, Khaled; Spinsante, Susanna

In recent years the technological development has encouraged several applications based on distributed communications network without any fixed infrastructure. The problem of providing a collaborative early warning system for multiple mobile nodes against a fast moving object. The solution is provided subject to system level constraints: motion of nodes, antenna sensitivity and Doppler effect at 2.4 GHz and 5.8 GHz. This approach consists of three stages. The first phase consists of detecting the incoming object using a highly directive two element antenna at 5.0 GHz band. The second phase consists of broadcasting the warning message using a low directivity broad antenna beam using 2× 2 antenna array which then in third phase will be detected by receiving nodes by using direction of arrival (DOA) estimation technique. The DOA estimation technique is used to estimate the range and bearing of the incoming nodes. The position of fast arriving object can be estimated using the MUSIC algorithm for warning beam DOA estimation. This paper is mainly intended to demonstrate the feasibility of early detection and warning system using a collaborative node to node communication links. The simulation is performed to show the behavior of detecting and broadcasting antennas as well as performance of the detection algorithm. The idea can be further expanded to implement commercial grade detection and warning system

Experimental Investigation on the Detection of Multiple Surface Cracks Using Vibrothermography with a Low-Power Piezoceramic Actuator

PubMed Central

Xu, Changhang; Xie, Jing; Zhang, Wuyang; Kong, Qingzhao; Chen, Guoming; Song, Gangbing

2017-01-01

Vibrothermography often employs a high-power actuator to generate heat on a specimen to reveal damage, however, the high-power actuator brings inconvenience to the application and possibly introduces additional damage to the inspected objects. This study uses a low-power piezoceramic transducer as the actuator of vibrothermography and explores its ability to detect multiple surface cracks in a metal part. Experiments were conducted on a thin aluminum beam with three cracks in different orientations. Detailed analyses of both thermograms and temperature data are presented to validate the proposed vibrothermography method. To further investigate the performance of the proposed vibrothermography method, we experimentally studied the effects of several critical factors, including the amplitude of excitation signal, specimen constraints, relative position between the transducer and cracks (the transducer is mounted on the same or the opposite side with the cracks). The results demonstrate that all cracks can be detected conveniently and simultaneously by using the proposed low-power vibrothermography. We also found that the magnitude of excitation signal and the specimen constraints have a great influence on detection results. Combined with effective data processing methods, such as Fourier transformation employed in this study, the proposed method provides a promising potential to detect multiple cracks on a metal surface in a safe and effective manner. PMID:29168759

Detecting objects in radiographs for homeland security

NASA Astrophysics Data System (ADS)

Prasad, Lakshman; Snyder, Hans

2005-05-01

We present a general scheme for segmenting a radiographic image into polygons that correspond to visual features. This decomposition provides a vectorized representation that is a high-level description of the image. The polygons correspond to objects or object parts present in the image. This characterization of radiographs allows the direct application of several shape recognition algorithms to identify objects. In this paper we describe the use of constrained Delaunay triangulations as a uniform foundational tool to achieve multiple visual tasks, namely image segmentation, shape decomposition, and parts-based shape matching. Shape decomposition yields parts that serve as tokens representing local shape characteristics. Parts-based shape matching enables the recognition of objects in the presence of occlusions, which commonly occur in radiographs. The polygonal representation of image features affords the efficient design and application of sophisticated geometric filtering methods to detect large-scale structural properties of objects in images. Finally, the representation of radiographs via polygons results in significant reduction of image file sizes and permits the scalable graphical representation of images, along with annotations of detected objects, in the SVG (scalable vector graphics) format that is proposed by the world wide web consortium (W3C). This is a textual representation that can be compressed and encrypted for efficient and secure transmission of information over wireless channels and on the Internet. In particular, our methods described here provide an algorithmic framework for developing image analysis tools for screening cargo at ports of entry for homeland security.

Independent motion detection with a rival penalized adaptive particle filter

NASA Astrophysics Data System (ADS)

Becker, Stefan; Hübner, Wolfgang; Arens, Michael

2014-10-01

Aggregation of pixel based motion detection into regions of interest, which include views of single moving objects in a scene is an essential pre-processing step in many vision systems. Motion events of this type provide significant information about the object type or build the basis for action recognition. Further, motion is an essential saliency measure, which is able to effectively support high level image analysis. When applied to static cameras, background subtraction methods achieve good results. On the other hand, motion aggregation on freely moving cameras is still a widely unsolved problem. The image flow, measured on a freely moving camera is the result from two major motion types. First the ego-motion of the camera and second object motion, that is independent from the camera motion. When capturing a scene with a camera these two motion types are adverse blended together. In this paper, we propose an approach to detect multiple moving objects from a mobile monocular camera system in an outdoor environment. The overall processing pipeline consists of a fast ego-motion compensation algorithm in the preprocessing stage. Real-time performance is achieved by using a sparse optical flow algorithm as an initial processing stage and a densely applied probabilistic filter in the post-processing stage. Thereby, we follow the idea proposed by Jung and Sukhatme. Normalized intensity differences originating from a sequence of ego-motion compensated difference images represent the probability of moving objects. Noise and registration artefacts are filtered out, using a Bayesian formulation. The resulting a posteriori distribution is located on image regions, showing strong amplitudes in the difference image which are in accordance with the motion prediction. In order to effectively estimate the a posteriori distribution, a particle filter is used. In addition to the fast ego-motion compensation, the main contribution of this paper is the design of the probabilistic filter for real-time detection and tracking of independently moving objects. The proposed approach introduces a competition scheme between particles in order to ensure an improved multi-modality. Further, the filter design helps to generate a particle distribution which is homogenous even in the presence of multiple targets showing non-rigid motion patterns. The effectiveness of the method is shown on exemplary outdoor sequences.

Adult Age Differences in Categorization and Multiple-Cue Judgment

ERIC Educational Resources Information Center

Mata, Rui; von Helversen, Bettina; Karlsson, Linnea; Cupper, Lutz

2012-01-01

We often need to infer unknown properties of objects from observable ones, just like detectives must infer guilt from observable clues and behavior. But how do inferential processes change with age? We examined young and older adults' reliance on rule-based and similarity-based processes in an inference task that can be considered either a…

On the Optimality of Answer-Copying Indices: Theory and Practice

ERIC Educational Resources Information Center

Romero, Mauricio; Riascos, Álvaro; Jara, Diego

2015-01-01

Multiple-choice exams are frequently used as an efficient and objective method to assess learning, but they are more vulnerable to answer copying than tests based on open questions. Several statistical tests (known as indices in the literature) have been proposed to detect cheating; however, to the best of our knowledge, they all lack mathematical…

Image Discrimination Predictions of a Single Channel Model with Contrast Gain Control

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.; Null, Cynthia H.

1995-01-01

Image discrimination models predict the number of just-noticeable-differences between two images. We report the predictions of a single channel model with contrast masking for a range of standard discrimination experiments. Despite its computational simplicity, this model has performed as well as a multiple channel model in an object detection task.

A combined system for 3D printing cybersecurity

NASA Astrophysics Data System (ADS)

Straub, Jeremy

2017-06-01

Previous work has discussed the impact of cybersecurity breaches on 3D printed objects. Multiple attack types that could weaken objects, make them unsuitable for certain applications and even create safety hazards have been presented. This paper considers a visible light sensing-based verification system's efficacy as a means of thwarting cybersecurity threats to 3D printing. This system detects discrepancies between expected and actual printed objects (based on an independent pristine CAD model). Whether reliance on an independent CAD model is appropriate is also considered. The future of 3D printing is projected and the importance of cybersecurity in this future is discussed.

Pre-impact fall detection system using dynamic threshold and 3D bounding box

NASA Astrophysics Data System (ADS)

Otanasap, Nuth; Boonbrahm, Poonpong

2017-02-01

Fall prevention and detection system have to subjugate many challenges in order to develop an efficient those system. Some of the difficult problems are obtrusion, occlusion and overlay in vision based system. Other associated issues are privacy, cost, noise, computation complexity and definition of threshold values. Estimating human motion using vision based usually involves with partial overlay, caused either by direction of view point between objects or body parts and camera, and these issues have to be taken into consideration. This paper proposes the use of dynamic threshold based and bounding box posture analysis method with multiple Kinect cameras setting for human posture analysis and fall detection. The proposed work only uses two Kinect cameras for acquiring distributed values and differentiating activities between normal and falls. If the peak value of head velocity is greater than the dynamic threshold value, bounding box posture analysis will be used to confirm fall occurrence. Furthermore, information captured by multiple Kinect placed in right angle will address the skeleton overlay problem due to single Kinect. This work contributes on the fusion of multiple Kinect based skeletons, based on dynamic threshold and bounding box posture analysis which is the only research work reported so far.

Micro-crack detection in CFRP laminates using coda wave NDE

NASA Astrophysics Data System (ADS)

Dayal, Vinay; Barnard, Dan; Livings, Richard

2018-04-01

Coda Waves or diffuse field has been touted to be an NDE method that does not require the damage to be in the path of the ultrasound. The object is insonified with ultrasound and instead of catching the first or second arrival, the waves are allowed to bounce multiple times. This aspect is very important in structural health monitoring (SHM) where the potential damage development location is unknown. Researchers have used Coda waves in the interrogation of seismic damage and metallic materials. In this work we have applied the technique to composite material, and present the results herein. The coda wave and acoustic emission signals are recorded simultaneously and corroborated. Development of small incipient damage in the form of micro-crack and their detection is the objective of this work.

LED induced autofluorescence (LIAF) imager with eight multi-filters for oral cancer diagnosis

NASA Astrophysics Data System (ADS)

Huang, Ting-Wei; Cheng, Nai-Lun; Tsai, Ming-Hsui; Chiou, Jin-Chern; Mang, Ou-Yang

2016-03-01

Oral cancer is one of the serious and growing problem in many developing and developed countries. The simple oral visual screening by clinician can reduce 37,000 oral cancer deaths annually worldwide. However, the conventional oral examination with the visual inspection and the palpation of oral lesions is not an objective and reliable approach for oral cancer diagnosis, and it may cause the delayed hospital treatment for the patients of oral cancer or leads to the oral cancer out of control in the late stage. Therefore, a device for oral cancer detection are developed for early diagnosis and treatment. A portable LED Induced autofluorescence (LIAF) imager is developed by our group. It contained the multiple wavelength of LED excitation light and the rotary filter ring of eight channels to capture ex-vivo oral tissue autofluorescence images. The advantages of LIAF imager compared to other devices for oral cancer diagnosis are that LIAF imager has a probe of L shape for fixing the object distance, protecting the effect of ambient light, and observing the blind spot in the deep port between the gumsgingiva and the lining of the mouth. Besides, the multiple excitation of LED light source can induce multiple autofluorescence, and LIAF imager with the rotary filter ring of eight channels can detect the spectral images of multiple narrow bands. The prototype of a portable LIAF imager is applied in the clinical trials for some cases in Taiwan, and the images of the clinical trial with the specific excitation show the significant differences between normal tissue and oral tissue under these cases.

The Effects of Age and Set Size on the Fast Extraction of Egocentric Distance

PubMed Central

Gajewski, Daniel A.; Wallin, Courtney P.; Philbeck, John W.

2016-01-01

Angular direction is a source of information about the distance to floor-level objects that can be extracted from brief glimpses (near one's threshold for detection). Age and set size are two factors known to impact the viewing time needed to directionally localize an object, and these were posited to similarly govern the extraction of distance. The question here was whether viewing durations sufficient to support object detection (controlled for age and set size) would also be sufficient to support well-constrained judgments of distance. Regardless of viewing duration, distance judgments were more accurate (less biased towards underestimation) when multiple potential targets were presented, suggesting that the relative angular declinations between the objects are an additional source of useful information. Distance judgments were more precise with additional viewing time, but the benefit did not depend on set size and accuracy did not improve with longer viewing durations. The overall pattern suggests that distance can be efficiently derived from direction for floor-level objects. Controlling for age-related differences in the viewing time needed to support detection was sufficient to support distal localization but only when brief and longer glimpse trials were interspersed. Information extracted from longer glimpse trials presumably supported performance on subsequent trials when viewing time was more limited. This outcome suggests a particularly important role for prior visual experience in distance judgments for older observers. PMID:27398065

Robust statistical reconstruction for charged particle tomography

DOEpatents

Schultz, Larry Joe; Klimenko, Alexei Vasilievich; Fraser, Andrew Mcleod; Morris, Christopher; Orum, John Christopher; Borozdin, Konstantin N; Sossong, Michael James; Hengartner, Nicolas W

2013-10-08

Systems and methods for charged particle detection including statistical reconstruction of object volume scattering density profiles from charged particle tomographic data to determine the probability distribution of charged particle scattering using a statistical multiple scattering model and determine a substantially maximum likelihood estimate of object volume scattering density using expectation maximization (ML/EM) algorithm to reconstruct the object volume scattering density. The presence of and/or type of object occupying the volume of interest can be identified from the reconstructed volume scattering density profile. The charged particle tomographic data can be cosmic ray muon tomographic data from a muon tracker for scanning packages, containers, vehicles or cargo. The method can be implemented using a computer program which is executable on a computer.

Experimental Demonstration of Adaptive Infrared Multispectral Imaging using Plasmonic Filter Array.

PubMed

Jang, Woo-Yong; Ku, Zahyun; Jeon, Jiyeon; Kim, Jun Oh; Lee, Sang Jun; Park, James; Noyola, Michael J; Urbas, Augustine

2016-10-10

In our previous theoretical study, we performed target detection using a plasmonic sensor array incorporating the data-processing technique termed "algorithmic spectrometry". We achieved the reconstruction of a target spectrum by extracting intensity at multiple wavelengths with high resolution from the image data obtained from the plasmonic array. The ultimate goal is to develop a full-scale focal plane array with a plasmonic opto-coupler in order to move towards the next generation of versatile infrared cameras. To this end, and as an intermediate step, this paper reports the experimental demonstration of adaptive multispectral imagery using fabricated plasmonic spectral filter arrays and proposed target detection scenarios. Each plasmonic filter was designed using periodic circular holes perforated through a gold layer, and an enhanced target detection strategy was proposed to refine the original spectrometry concept for spatial and spectral computation of the data measured from the plasmonic array. Both the spectrum of blackbody radiation and a metal ring object at multiple wavelengths were successfully reconstructed using the weighted superposition of plasmonic output images as specified in the proposed detection strategy. In addition, plasmonic filter arrays were theoretically tested on a target at extremely high temperature as a challenging scenario for the detection scheme.

Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique

NASA Astrophysics Data System (ADS)

Tieng, Quang M.; Anbazhagan, Ashwin; Chen, Min; Reutens, David C.

2017-12-01

Objective. Epilepsy is a common neurological disorder characterized by recurrent, unprovoked seizures. The search for new treatments for seizures and epilepsy relies upon studies in animal models of epilepsy. To capture data on seizures, many applications require prolonged electroencephalography (EEG) with recordings that generate voluminous data. The desire for efficient evaluation of these recordings motivates the development of automated seizure detection algorithms. Approach. A new seizure detection method is proposed, based on multiple features and a simple thresholding technique. The features are derived from chaos theory, information theory and the power spectrum of EEG recordings and optimally exploit both linear and nonlinear characteristics of EEG data. Main result. The proposed method was tested with real EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and specificity. Significance. The proposed approach introduces two new features: negative logarithm of adaptive correlation integral and power spectral coherence ratio. The combination of these new features with two previously described features, entropy and phase coherence, improved seizure detection accuracy significantly. Negative logarithm of adaptive correlation integral can also be used to compute the duration of automatically detected seizures.

Multi-view and 3D deformable part models.

PubMed

Pepik, Bojan; Stark, Michael; Gehler, Peter; Schiele, Bernt

2015-11-01

As objects are inherently 3D, they have been modeled in 3D in the early days of computer vision. Due to the ambiguities arising from mapping 2D features to 3D models, 3D object representations have been neglected and 2D feature-based models are the predominant paradigm in object detection nowadays. While such models have achieved outstanding bounding box detection performance, they come with limited expressiveness, as they are clearly limited in their capability of reasoning about 3D shape or viewpoints. In this work, we bring the worlds of 3D and 2D object representations closer, by building an object detector which leverages the expressive power of 3D object representations while at the same time can be robustly matched to image evidence. To that end, we gradually extend the successful deformable part model [1] to include viewpoint information and part-level 3D geometry information, resulting in several different models with different level of expressiveness. We end up with a 3D object model, consisting of multiple object parts represented in 3D and a continuous appearance model. We experimentally verify that our models, while providing richer object hypotheses than the 2D object models, provide consistently better joint object localization and viewpoint estimation than the state-of-the-art multi-view and 3D object detectors on various benchmarks (KITTI [2] , 3D object classes [3] , Pascal3D+ [4] , Pascal VOC 2007 [5] , EPFL multi-view cars[6] ).

A novel snapshot polarimetric imager

NASA Astrophysics Data System (ADS)

Wong, Gerald; McMaster, Ciaran; Struthers, Robert; Gorman, Alistair; Sinclair, Peter; Lamb, Robert; Harvey, Andrew R.

2012-10-01

Polarimetric imaging (PI) is of increasing importance in determining additional scene information beyond that of conventional images. For very long-range surveillance, image quality is degraded due to turbulence. Furthermore, the high magnification required to create images with sufficient spatial resolution suitable for object recognition and identification require long focal length optical systems. These are incompatible with the size and weight restrictions for aircraft. Techniques which allow detection and recognition of an object at the single pixel level are therefore likely to provide advance warning of approaching threats or long-range object cueing. PI is a technique that has the potential to detect object signatures at the pixel level. Early attempts to develop PI used rotating polarisers (and spectral filters) which recorded sequential polarized images from which the complete Stokes matrix could be derived. This approach has built-in latency between frames and requires accurate registration of consecutive frames to analyze real-time video of moving objects. Alternatively, multiple optical systems and cameras have been demonstrated to remove latency, but this approach increases cost and bulk of the imaging system. In our investigation we present a simplified imaging system that divides an image into two orthogonal polarimetric components which are then simultaneously projected onto a single detector array. Thus polarimetric data is recorded without latency on a single snapshot. We further show that, for pixel-level objects, the data derived from only two orthogonal states (H and V) is sufficient to increase the probability of detection whilst reducing false alarms compared to conventional unpolarised imaging.

Microscopy using source and detector arrays

NASA Astrophysics Data System (ADS)

Sheppard, Colin J. R.; Castello, Marco; Vicidomini, Giuseppe; Duocastella, Martí; Diaspro, Alberto

2016-03-01

There are basically two types of microscope, which we call conventional and scanning. The former type is a full-field imaging system. In the latter type, the object is illuminated with a probe beam, and a signal detected. We can generalize the probe to a patterned illumination. Similarly we can generalize the detection to a patterned detection. Combining these we get a range of different modalities: confocal microscopy, structured illumination (with full-field imaging), spinning disk (with multiple illumination points), and so on. The combination allows the spatial frequency bandwidth of the system to be doubled. In general we can record a four dimensional (4D) image of a 2D object (or a 6D image from a 3D object, using an acoustic tuneable lens). The optimum way to directly reconstruct the resulting image is by image scanning microscopy (ISM). But the 4D image is highly redundant, so deconvolution-based approaches are also relevant. ISM can be performed in fluorescence, bright field or interference microscopy. Several different implementations have been described, with associated advantages and disadvantages. In two-photon microscopy, the illumination and detection point spread functions are very different. This is also the case when using pupil filters or when there is a large Stokes shift.

Threshold selection for classification of MR brain images by clustering method

NASA Astrophysics Data System (ADS)

Moldovanu, Simona; Obreja, Cristian; Moraru, Luminita

2015-12-01

Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

Comparison of detectability in step-and-shoot mode and continuous mode digital tomosynthesis systems

NASA Astrophysics Data System (ADS)

Lee, Changwoo; Han, Minah; Baek, Jongduk

2017-03-01

Digital tomosynthesis system has been widely used in chest, dental, and breast imaging. Since the digital tomosynthesis system provides volumetric images from multiple projection data, structural noise inherent in X-ray radiograph can be reduced, and thus signal detection performance is improved. Currently, tomosynthesis system uses two data acquisition modes: step-and-shoot mode and continuous mode. Several studies have been conducted to compare the system performance of two acquisition modes with respect to spatial resolution and contrast. In this work, we focus on signal detectability in step-and-shoot mode and continuous mode. For evaluation, uniform background is considered, and eight spherical objects with diameters of 0.5, 0.8, 1, 2, 3, 5, 8, 10 mm are used as signals. Projection data with and without spherical objects are acquired in step-and-shoot mode and continuous mode, respectively, and quantum noise are added. Then, noisy projection data are reconstructed by FDK algorithm. To compare the detection performance of two acquisition modes, we calculate task signal-to-noise ratio (SNR) of channelized Hotelling observer with Laguerre-Gauss channels for each spherical object. While the task-SNR values of two acquisition modes are similar for spherical objects larger than 1 mm diameter, step-and-shoot mode yields higher detectability for small signal sizes. The main reason of this behavior is that small signal is more affected by X-ray tube motion blur than large signal. Our results indicate that it is beneficial to use step-and-shoot data acquisition mode to improve the detectability of small signals (i.e., less than 1 mm diameter) in digital tomosynthesis systems.

Deep Learning for Extreme Weather Detection

NASA Astrophysics Data System (ADS)

Prabhat, M.; Racah, E.; Biard, J.; Liu, Y.; Mudigonda, M.; Kashinath, K.; Beckham, C.; Maharaj, T.; Kahou, S.; Pal, C.; O'Brien, T. A.; Wehner, M. F.; Kunkel, K.; Collins, W. D.

2017-12-01

We will present our latest results from the application of Deep Learning methods for detecting, localizing and segmenting extreme weather patterns in climate data. We have successfully applied supervised convolutional architectures for the binary classification tasks of detecting tropical cyclones and atmospheric rivers in centered, cropped patches. We have subsequently extended our architecture to a semi-supervised formulation, which is capable of learning a unified representation of multiple weather patterns, predicting bounding boxes and object categories, and has the capability to detect novel patterns (w/ few, or no labels). We will briefly present our efforts in scaling the semi-supervised architecture to 9600 nodes of the Cori supercomputer, obtaining 15PF performance. Time permitting, we will highlight our efforts in pixel-level segmentation of weather patterns.

A data set for evaluating the performance of multi-class multi-object video tracking

NASA Astrophysics Data System (ADS)

Chakraborty, Avishek; Stamatescu, Victor; Wong, Sebastien C.; Wigley, Grant; Kearney, David

2017-05-01

One of the challenges in evaluating multi-object video detection, tracking and classification systems is having publically available data sets with which to compare different systems. However, the measures of performance for tracking and classification are different. Data sets that are suitable for evaluating tracking systems may not be appropriate for classification. Tracking video data sets typically only have ground truth track IDs, while classification video data sets only have ground truth class-label IDs. The former identifies the same object over multiple frames, while the latter identifies the type of object in individual frames. This paper describes an advancement of the ground truth meta-data for the DARPA Neovision2 Tower data set to allow both the evaluation of tracking and classification. The ground truth data sets presented in this paper contain unique object IDs across 5 different classes of object (Car, Bus, Truck, Person, Cyclist) for 24 videos of 871 image frames each. In addition to the object IDs and class labels, the ground truth data also contains the original bounding box coordinates together with new bounding boxes in instances where un-annotated objects were present. The unique IDs are maintained during occlusions between multiple objects or when objects re-enter the field of view. This will provide: a solid foundation for evaluating the performance of multi-object tracking of different types of objects, a straightforward comparison of tracking system performance using the standard Multi Object Tracking (MOT) framework, and classification performance using the Neovision2 metrics. These data have been hosted publically.

A New 3D Object Pose Detection Method Using LIDAR Shape Set

PubMed Central

Kim, Jung-Un

2018-01-01

In object detection systems for autonomous driving, LIDAR sensors provide very useful information. However, problems occur because the object representation is greatly distorted by changes in distance. To solve this problem, we propose a LIDAR shape set that reconstructs the shape surrounding the object more clearly by using the LIDAR point information projected on the object. The LIDAR shape set restores object shape edges from a bird’s eye view by filtering LIDAR points projected on a 2D pixel-based front view. In this study, we use this shape set for two purposes. The first is to supplement the shape set with a LIDAR Feature map, and the second is to divide the entire shape set according to the gradient of the depth and density to create a 2D and 3D bounding box proposal for each object. We present a multimodal fusion framework that classifies objects and restores the 3D pose of each object using enhanced feature maps and shape-based proposals. The network structure consists of a VGG -based object classifier that receives multiple inputs and a LIDAR-based Region Proposal Networks (RPN) that identifies object poses. It works in a very intuitive and efficient manner and can be extended to other classes other than vehicles. Our research has outperformed object classification accuracy (Average Precision, AP) and 3D pose restoration accuracy (3D bounding box recall rate) based on the latest studies conducted with KITTI data sets. PMID:29547551

A New 3D Object Pose Detection Method Using LIDAR Shape Set.

PubMed

Kim, Jung-Un; Kang, Hang-Bong

2018-03-16

In object detection systems for autonomous driving, LIDAR sensors provide very useful information. However, problems occur because the object representation is greatly distorted by changes in distance. To solve this problem, we propose a LIDAR shape set that reconstructs the shape surrounding the object more clearly by using the LIDAR point information projected on the object. The LIDAR shape set restores object shape edges from a bird's eye view by filtering LIDAR points projected on a 2D pixel-based front view. In this study, we use this shape set for two purposes. The first is to supplement the shape set with a LIDAR Feature map, and the second is to divide the entire shape set according to the gradient of the depth and density to create a 2D and 3D bounding box proposal for each object. We present a multimodal fusion framework that classifies objects and restores the 3D pose of each object using enhanced feature maps and shape-based proposals. The network structure consists of a VGG -based object classifier that receives multiple inputs and a LIDAR-based Region Proposal Networks (RPN) that identifies object poses. It works in a very intuitive and efficient manner and can be extended to other classes other than vehicles. Our research has outperformed object classification accuracy (Average Precision, AP) and 3D pose restoration accuracy (3D bounding box recall rate) based on the latest studies conducted with KITTI data sets.

Asteroid detection using a single multi-wavelength CCD scan

NASA Astrophysics Data System (ADS)

Melton, Jonathan

2016-09-01

Asteroid detection is a topic of great interest due to the possibility of diverting possibly dangerous asteroids or mining potentially lucrative ones. Currently, asteroid detection is generally performed by taking multiple images of the same patch of sky separated by 10-15 minutes, then subtracting the images to find movement. However, this is time consuming because of the need to revisit the same area multiple times per night. This paper describes an algorithm that can detect asteroids using a single CCD camera scan, thus cutting down on the time and cost of an asteroid survey. The algorithm is based on the fact that some telescopes scan the sky at multiple wavelengths with a small time separation between the wavelength components. As a result, an object moving with sufficient speed will appear in different places in different wavelength components of the same image. Using image processing techniques we detect the centroids of points of light in the first component and compare these positions to the centroids in the other components using a nearest neighbor algorithm. The algorithm was used on a test set of 49 images obtained from the Sloan telescope in New Mexico and found 100% of known asteroids with only 3 false positives. This algorithm has the advantage of decreasing the amount of time required to perform an asteroid scan, thus allowing more sky to be scanned in the same amount of time or freeing a telescope for other pursuits.

Multiple pass reimaging optical system

NASA Technical Reports Server (NTRS)

Gunter, W. D., Jr.; Brown, R. M. (Inventor)

1973-01-01

An optical imaging system for enabling nonabsorbed light imaged onto a photodetective surface to be collected and reimaged one or more times onto that surface in register with the original image. The system includes an objective lens, one or more imaging lenses, one or more retroreflectors and perhaps a prism for providing optical matching of the imaging lens focal planes to the photo detective surface.

Use of unwounded ash trees for the detection of emerald ash borer adults: EAB landing behavior

Treesearch

Jordan M. Marshall; Melissa J. Porter; Andrew J. Storer

2011-01-01

Incorporation of multiple trapping techniques and sites within a survey program is essential to adequately identify the range of emerald ash borer (EAB) (Agrilus planipennis Fairmaire) infestation. Within natural forests, EAB lands on stick band traps wrapped around girdled ash trees at a rate similar to that on unwounded ash trees. The objective of...

Target Classification of Canonical Scatterers Using Classical Estimation and Dictionary Based Techniques

DTIC Science & Technology

2012-03-22

shapes tested , when the objective parameter set was confined to a dictionary’s de - fined parameter space. These physical characteristics included...8 2.3 Hypothesis Testing and Detection Theory . . . . . . . . . . . . . . . 8 2.4 3-D SAR Scattering Models...basis pursuit de -noising (BPDN) algorithm is chosen to perform extraction due to inherent efficiency and error tolerance. Multiple shape dictionaries

Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate.

PubMed

Song, Qi; Wu, Xiaodong; Liu, Yunlong; Smith, Mark; Buatti, John; Sonka, Milan

2009-01-01

We present a novel method for globally optimal surface segmentation of multiple mutually interacting objects, incorporating both edge and shape knowledge in a 3-D graph-theoretic approach. Hard surface interacting constraints are enforced in the interacting regions, preserving the geometric relationship of those partially interacting surfaces. The soft smoothness a priori shape compliance is introduced into the energy functional to provide shape guidance. The globally optimal surfaces can be simultaneously achieved by solving a maximum flow problem based on an arc-weighted graph representation. Representing the segmentation problem in an arc-weighted graph, one can incorporate a wider spectrum of constraints into the formulation, thus increasing segmentation accuracy and robustness in volumetric image data. To the best of our knowledge, our method is the first attempt to introduce the arc-weighted graph representation into the graph-searching approach for simultaneous segmentation of multiple partially interacting objects, which admits a globally optimal solution in a low-order polynomial time. Our new approach was applied to the simultaneous surface detection of bladder and prostate. The result was quite encouraging in spite of the low saliency of the bladder and prostate in CT images.

Multiple-camera/motion stereoscopy for range estimation in helicopter flight

NASA Technical Reports Server (NTRS)

Smith, Phillip N.; Sridhar, Banavar; Suorsa, Raymond E.

1993-01-01

Aiding the pilot to improve safety and reduce pilot workload by detecting obstacles and planning obstacle-free flight paths during low-altitude helicopter flight is desirable. Computer vision techniques provide an attractive method of obstacle detection and range estimation for objects within a large field of view ahead of the helicopter. Previous research has had considerable success by using an image sequence from a single moving camera to solving this problem. The major limitations of single camera approaches are that no range information can be obtained near the instantaneous direction of motion or in the absence of motion. These limitations can be overcome through the use of multiple cameras. This paper presents a hybrid motion/stereo algorithm which allows range refinement through recursive range estimation while avoiding loss of range information in the direction of travel. A feature-based approach is used to track objects between image frames. An extended Kalman filter combines knowledge of the camera motion and measurements of a feature's image location to recursively estimate the feature's range and to predict its location in future images. Performance of the algorithm will be illustrated using an image sequence, motion information, and independent range measurements from a low-altitude helicopter flight experiment.

MuSCoWERT: multi-scale consistence of weighted edge Radon transform for horizon detection in maritime images.

PubMed

Prasad, Dilip K; Rajan, Deepu; Rachmawati, Lily; Rajabally, Eshan; Quek, Chai

2016-12-01

This paper addresses the problem of horizon detection, a fundamental process in numerous object detection algorithms, in a maritime environment. The maritime environment is characterized by the absence of fixed features, the presence of numerous linear features in dynamically changing objects and background and constantly varying illumination, rendering the typically simple problem of detecting the horizon a challenging one. We present a novel method called multi-scale consistence of weighted edge Radon transform, abbreviated as MuSCoWERT. It detects the long linear features consistent over multiple scales using multi-scale median filtering of the image followed by Radon transform on a weighted edge map and computing the histogram of the detected linear features. We show that MuSCoWERT has excellent performance, better than seven other contemporary methods, for 84 challenging maritime videos, containing over 33,000 frames, and captured using visible range and near-infrared range sensors mounted onboard, onshore, or on floating buoys. It has a median error of about 2 pixels (less than 0.2%) from the center of the actual horizon and a median angular error of less than 0.4 deg. We are also sharing a new challenging horizon detection dataset of 65 videos of visible, infrared cameras for onshore and onboard ship camera placement.

Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics.

PubMed

Benedek, C; Descombes, X; Zerubia, J

2012-01-01

In this paper, we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. We present methodological contributions in three key issues: 1) We implement a novel object-change modeling approach based on Multitemporal Marked Point Processes, which simultaneously exploits low-level change information between the time layers and object-level building description to recognize and separate changed and unaltered buildings. 2) To answer the challenges of data heterogeneity in aerial and satellite image repositories, we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature-based modules. 3) To simultaneously ensure the convergence, optimality, and computation complexity constraints raised by the increased data quantity, we adopt the quick Multiple Birth and Death optimization technique for change detection purposes, and propose a novel nonuniform stochastic object birth process which generates relevant objects with higher probability based on low-level image features.

Multiple injuries in suicide simulating homicide: report of three cases.

PubMed

Austin, Amy E; Guddat, Saskia S; Tsokos, Michael; Gilbert, John D; Byard, Roger W

2013-08-01

Multiple inflicted injuries in traumatic deaths usually indicate homicide. Three cases are reported where homicide was initially suspected due to findings at the death scene and the apparent nature of the injuries however, after investigation, involvement of any other individuals in the deaths could be excluded. Case 1: A 52-year-old male was found with multiple stab wounds. At autopsy, 36 stab wounds were identified, the majority of which were superficial. Only two stab wounds had penetrated deeply. Case 2: A 19-year-old female was found with three gunshot entry wounds to the right temple and a .22 calibre automatic rifle resting across her lap. Case 3: A 47-year-old female was found with numerous haematomas and three deep head wounds in keeping with trauma from impact with a blunt object. A high level of clozapine was detected on toxicological analysis of blood and a history of schizophrenia was reported. Although multiple self-inflicted wounds are most often caused by sharp objects such as knives, on occasion multiple gunshot wounds and rarely, blunt trauma may also be encountered. Careful integration of scene and autopsy findings may be required to avoid misinterpretation of the circumstances and manner of death. Copyright © 2013 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Hierarchical image feature extraction by an irregular pyramid of polygonal partitions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Skurikhin, Alexei N

2008-01-01

We present an algorithmic framework for hierarchical image segmentation and feature extraction. We build a successive fine-to-coarse hierarchy of irregular polygonal partitions of the original image. This multiscale hierarchy forms the basis for object-oriented image analysis. The framework incorporates the Gestalt principles of visual perception, such as proximity and closure, and exploits spectral and textural similarities of polygonal partitions, while iteratively grouping them until dissimilarity criteria are exceeded. Seed polygons are built upon a triangular mesh composed of irregular sized triangles, whose spatial arrangement is adapted to the image content. This is achieved by building the triangular mesh on themore » top of detected spectral discontinuities (such as edges), which form a network of constraints for the Delaunay triangulation. The image is then represented as a spatial network in the form of a graph with vertices corresponding to the polygonal partitions and edges reflecting their relations. The iterative agglomeration of partitions into object-oriented segments is formulated as Minimum Spanning Tree (MST) construction. An important characteristic of the approach is that the agglomeration of polygonal partitions is constrained by the detected edges; thus the shapes of agglomerated partitions are more likely to correspond to the outlines of real-world objects. The constructed partitions and their spatial relations are characterized using spectral, textural and structural features based on proximity graphs. The framework allows searching for object-oriented features of interest across multiple levels of details of the built hierarchy and can be generalized to the multi-criteria MST to account for multiple criteria important for an application.« less

Single-Photon-Sensitive HgCdTe Avalanche Photodiode Detector

NASA Technical Reports Server (NTRS)

Huntington, Andrew

2013-01-01

The purpose of this program was to develop single-photon-sensitive short-wavelength infrared (SWIR) and mid-wavelength infrared (MWIR) avalanche photodiode (APD) receivers based on linear-mode HgCdTe APDs, for application by NASA in light detection and ranging (lidar) sensors. Linear-mode photon-counting APDs are desired for lidar because they have a shorter pixel dead time than Geiger APDs, and can detect sequential pulse returns from multiple objects that are closely spaced in range. Linear-mode APDs can also measure photon number, which Geiger APDs cannot, adding an extra dimension to lidar scene data for multi-photon returns. High-gain APDs with low multiplication noise are required for efficient linear-mode detection of single photons because of APD gain statistics -- a low-excess-noise APD will generate detectible current pulses from single photon input at a much higher rate of occurrence than will a noisy APD operated at the same average gain. MWIR and LWIR electron-avalanche HgCdTe APDs have been shown to operate in linear mode at high average avalanche gain (M > 1000) without excess multiplication noise (F = 1), and are therefore very good candidates for linear-mode photon counting. However, detectors fashioned from these narrow-bandgap alloys require aggressive cooling to control thermal dark current. Wider-bandgap SWIR HgCdTe APDs were investigated in this program as a strategy to reduce detector cooling requirements.

Radiation detector having a multiplicity of individual detecting elements

DOEpatents

Whetten, Nathan R.; Kelley, John E.

1985-01-01

A radiation detector has a plurality of detector collection element arrays immersed in a radiation-to-electron conversion medium. Each array contains a multiplicity of coplanar detector elements radially disposed with respect to one of a plurality of positions which at least one radiation source can assume. Each detector collector array is utilized only when a source is operative at the associated source position, negating the necessity for a multi-element detector to be moved with respect to an object to be examined. A novel housing provides the required containment of a high-pressure gas conversion medium.

Change Detection Algorithms for Surveillance in Visual IoT: A Comparative Study

NASA Astrophysics Data System (ADS)

Akram, Beenish Ayesha; Zafar, Amna; Akbar, Ali Hammad; Wajid, Bilal; Chaudhry, Shafique Ahmad

2018-01-01

The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule's Coefficient) and JC (Jaccard's Coefficient), execution time and memory consumption. Experimental results showed that Kapur's algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes.

Convergent and invariant object representations for sight, sound, and touch.

PubMed

Man, Kingson; Damasio, Antonio; Meyer, Kaspar; Kaplan, Jonas T

2015-09-01

We continuously perceive objects in the world through multiple sensory channels. In this study, we investigated the convergence of information from different sensory streams within the cerebral cortex. We presented volunteers with three common objects via three different modalities-sight, sound, and touch-and used multivariate pattern analysis of functional magnetic resonance imaging data to map the cortical regions containing information about the identity of the objects. We could reliably predict which of the three stimuli a subject had seen, heard, or touched from the pattern of neural activity in the corresponding early sensory cortices. Intramodal classification was also successful in large portions of the cerebral cortex beyond the primary areas, with multiple regions showing convergence of information from two or all three modalities. Using crossmodal classification, we also searched for brain regions that would represent objects in a similar fashion across different modalities of presentation. We trained a classifier to distinguish objects presented in one modality and then tested it on the same objects presented in a different modality. We detected audiovisual invariance in the right temporo-occipital junction, audiotactile invariance in the left postcentral gyrus and parietal operculum, and visuotactile invariance in the right postcentral and supramarginal gyri. Our maps of multisensory convergence and crossmodal generalization reveal the underlying organization of the association cortices, and may be related to the neural basis for mental concepts. © 2015 Wiley Periodicals, Inc.

An opposite view data replacement approach for reducing artifacts due to metallic dental objects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yazdi, Mehran; Lari, Meghdad Asadi; Bernier, Gaston

Purpose: To present a conceptually new method for metal artifact reduction (MAR) that can be used on patients with multiple objects within the scan plane that are also of small sized along the longitudinal (scanning) direction, such as dental fillings. Methods: The proposed algorithm, named opposite view replacement, achieves MAR by first detecting the projection data affected by metal objects and then replacing the affected projections by the corresponding opposite view projections, which are not affected by metal objects. The authors also applied a fading process to avoid producing any discontinuities in the boundary of the affected projection areas inmore » the sinogram. A skull phantom with and without a variety of dental metal inserts was made to extract the performance metric of the algorithm. A head and neck case, typical of IMRT planning, was also tested. Results: The reconstructed CT images based on this new replacement scheme show a significant improvement in image quality for patients with metallic dental objects compared to the MAR algorithms based on the interpolation scheme. For the phantom, the authors showed that the artifact reduction algorithm can efficiently recover the CT numbers in the area next to the metallic objects. Conclusions: The authors presented a new and efficient method for artifact reduction due to multiple small metallic objects. The obtained results from phantoms and clinical cases fully validate the proposed approach.« less

The Benefits of Using Time-Frequency Analysis with Synthetic Aperture Focusing Technique

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albright, Austin P; Clayton, Dwight A

2015-01-01

Improvements in detection and resolution are always desired and needed. There are various instruments available for the inspection of concrete structures that can be used with confidence for detecting different defects. However, more often than not that confidence is heavily dependent on the experience of the operator rather than the clear, objective discernibility of the output of the instrument. The challenge of objective discernment is amplified when the concrete structures contain multiple layers of reinforcement, are of significant thickness, or both, such as concrete structures in nuclear power plants. We seek to improve and extend the usefulness of results producedmore » using the synthetic aperture focusing technique (SAFT) on data collected from thick, complex concrete structures. A secondary goal is to improve existing SAFT results, with regards to repeatedly and objectively identifying defects and/or internal structure of concrete structures. Towards these goals, we are applying the time-frequency technique of wavelet packet decomposition and reconstruction using a mother wavelet that possesses the exact reconstruction property. However, instead of analyzing the coefficients of each decomposition node, we select and reconstruct specific nodes based on the frequency band it contains to produce a frequency band specific time-series representation. SAFT is then applied to these frequency specific reconstructions allowing SAFT to be used to visualize the reflectivity of a frequency band and that band s interaction with the contents of the concrete structure. We apply our technique to data sets collected using a commercial, ultrasonic linear array (MIRA) from two 1.5m x 2m x 25cm concrete test specimens. One specimen contains multiple layers of rebar. The other contains honeycomb, crack, and rebar bonding defect analogs. This approach opens up a multitude of possibilities for improved detection, readability, and overall improved objectivity. We will focus on improved defect/reinforcement isolation in thick and multilayered reinforcement environments. Additionally, the ability to empirically explore the possibility of a frequency-band-defect-type relationship or sensitivity becomes available.« less

The benefits of using time-frequency analysis with synthetic aperture focusing technique

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albright, Austin, E-mail: albrightap@ornl.gov, E-mail: claytonda@ornl.gov; Clayton, Dwight, E-mail: albrightap@ornl.gov, E-mail: claytonda@ornl.gov

2015-03-31

Improvements in detection and resolution are always desired and needed. There are various instruments available for the inspection of concrete structures that can be used with confidence for detecting different defects. However, more often than not that confidence is heavily dependent on the experience of the operator rather than the clear, objective discernibility of the output of the instrument. The challenge of objective discernment is amplified when the concrete structures contain multiple layers of reinforcement, are of significant thickness, or both, such as concrete structures in nuclear power plants. We seek to improve and extend the usefulness of results producedmore » using the synthetic aperture focusing technique (SAFT) on data collected from thick, complex concrete structures. A secondary goal is to improve existing SAFT results, with regards to repeatedly and objectively identifying defects and/or internal structure of concrete structures. Towards these goals, we are applying the time-frequency technique of wavelet packet decomposition and reconstruction using a mother wavelet that possesses the exact reconstruction property. However, instead of analyzing the coefficients of each decomposition node, we select and reconstruct specific nodes based on the frequency band it contains to produce a frequency band specific time-series representation. SAFT is then applied to these frequency specific reconstructions allowing SAFT to be used to visualize the reflectivity of a frequency band and that band's interaction with the contents of the concrete structure. We apply our technique to data sets collected using a commercial, ultrasonic linear array (MIRA) from two 1.5m × 2m × 25cm concrete test specimens. One specimen contains multiple layers of rebar. The other contains honeycomb, crack, and rebar bonding defect analogs. This approach opens up a multitude of possibilities for improved detection, readability, and overall improved objectivity. We will focus on improved defect/reinforcement isolation in thick and multilayered reinforcement environments. Additionally, the ability to empirically explore the possibility of a frequency-band-defect-type relationship or sensitivity becomes available.« less

The benefits of using time-frequency analysis with synthetic aperture focusing technique

NASA Astrophysics Data System (ADS)

Albright, Austin; Clayton, Dwight

2015-03-01

Improvements in detection and resolution are always desired and needed. There are various instruments available for the inspection of concrete structures that can be used with confidence for detecting different defects. However, more often than not that confidence is heavily dependent on the experience of the operator rather than the clear, objective discernibility of the output of the instrument. The challenge of objective discernment is amplified when the concrete structures contain multiple layers of reinforcement, are of significant thickness, or both, such as concrete structures in nuclear power plants. We seek to improve and extend the usefulness of results produced using the synthetic aperture focusing technique (SAFT) on data collected from thick, complex concrete structures. A secondary goal is to improve existing SAFT results, with regards to repeatedly and objectively identifying defects and/or internal structure of concrete structures. Towards these goals, we are applying the time-frequency technique of wavelet packet decomposition and reconstruction using a mother wavelet that possesses the exact reconstruction property. However, instead of analyzing the coefficients of each decomposition node, we select and reconstruct specific nodes based on the frequency band it contains to produce a frequency band specific time-series representation. SAFT is then applied to these frequency specific reconstructions allowing SAFT to be used to visualize the reflectivity of a frequency band and that band's interaction with the contents of the concrete structure. We apply our technique to data sets collected using a commercial, ultrasonic linear array (MIRA) from two 1.5m × 2m × 25cm concrete test specimens. One specimen contains multiple layers of rebar. The other contains honeycomb, crack, and rebar bonding defect analogs. This approach opens up a multitude of possibilities for improved detection, readability, and overall improved objectivity. We will focus on improved defect/reinforcement isolation in thick and multilayered reinforcement environments. Additionally, the ability to empirically explore the possibility of a frequency-band-defect-type relationship or sensitivity becomes available.

Choose and choose again: appearance-reality errors, pragmatics and logical ability.

PubMed

Deák, Gedeon O; Enright, Brian

2006-05-01

In the Appearance/Reality (AR) task some 3- and 4-year-old children make perseverative errors: they choose the same word for the appearance and the function of a deceptive object. Are these errors specific to the AR task, or signs of a general question-answering problem? Preschoolers completed five tasks: AR; simple successive forced-choice question pairs (QP); flexible naming of objects (FN); working memory (WM) span; and indeterminacy detection (ID). AR errors correlated with QP errors. Insensitivity to indeterminacy predicted perseveration in both tasks. Neither WM span nor flexible naming predicted other measures. Age predicted sensitivity to indeterminacy. These findings suggest that AR tests measure a pragmatic understanding; specifically, different questions about a topic usually call for different answers. This understanding is related to the ability to detect indeterminacy of each question in a series. AR errors are unrelated to the ability to represent an object as belonging to multiple categories, to working memory span, or to inhibiting previously activated words.

Doppler radar detection of vortex hazard indicators

NASA Technical Reports Server (NTRS)

Nespor, Jerald D.; Hudson, B.; Stegall, R. L.; Freedman, Jerome E.

1994-01-01

Wake vortex experiments were conducted at White Sands Missile Range, NM using the AN/MPS-39 Multiple Object Tracking Radar (MOTR). The purpose of these experiments was twofold. The first objective was to verify that radar returns from wake vortex are observed for some time after the passage of an aircraft. The second objective was to verify that other vortex hazard indicators such as ambient wind speed and direction could also be detected. The present study addresses the Doppler characteristics of wake vortex and clear air returns based upon measurements employing MOTR, a very sensitive C-Band phased array radar. In this regard, the experiment was conducted so that the spectral characteristics could be determined on a dwell to-dwell basis. Results are presented from measurements of the backscattered power (equivalent structure constant), radial velocity and spectral width when the aircraft flies transverse and axial to the radar beam. The statistics of the backscattered power and spectral width for each case are given. In addition, the scan strategy, experimental test procedure and radar parameters are presented.

Gaze movements and spatial working memory in collision avoidance: a traffic intersection task

PubMed Central

Hardiess, Gregor; Hansmann-Roth, Sabrina; Mallot, Hanspeter A.

2013-01-01

Street crossing under traffic is an everyday activity including collision detection as well as avoidance of objects in the path of motion. Such tasks demand extraction and representation of spatio-temporal information about relevant obstacles in an optimized format. Relevant task information is extracted visually by the use of gaze movements and represented in spatial working memory. In a virtual reality traffic intersection task, subjects are confronted with a two-lane intersection where cars are appearing with different frequencies, corresponding to high and low traffic densities. Under free observation and exploration of the scenery (using unrestricted eye and head movements) the overall task for the subjects was to predict the potential-of-collision (POC) of the cars or to adjust an adequate driving speed in order to cross the intersection without collision (i.e., to find the free space for crossing). In a series of experiments, gaze movement parameters, task performance, and the representation of car positions within working memory at distinct time points were assessed in normal subjects as well as in neurological patients suffering from homonymous hemianopia. In the following, we review the findings of these experiments together with other studies and provide a new perspective of the role of gaze behavior and spatial memory in collision detection and avoidance, focusing on the following questions: (1) which sensory variables can be identified supporting adequate collision detection? (2) How do gaze movements and working memory contribute to collision avoidance when multiple moving objects are present and (3) how do they correlate with task performance? (4) How do patients with homonymous visual field defects (HVFDs) use gaze movements and working memory to compensate for visual field loss? In conclusion, we extend the theory of collision detection and avoidance in the case of multiple moving objects and provide a new perspective on the combined operation of external (bottom-up) and internal (top-down) cues in a traffic intersection task. PMID:23760667

A phantom design and assessment of lesion detectability in PET imaging

NASA Astrophysics Data System (ADS)

Wollenweber, Scott D.; Kinahan, Paul E.; Alessio, Adam M.

2017-03-01

The early detection of abnormal regions with increased tracer uptake in positron emission tomography (PET) is a key driver of imaging system design and optimization as well as choice of imaging protocols. Detectability, however, remains difficult to assess due to the need for realistic objects mimicking the clinical scene, multiple lesion-present and lesion-absent images and multiple observers. Fillable phantoms, with tradeoffs between complexity and utility, provide a means to quantitatively test and compare imaging systems under truth-known conditions. These phantoms, however, often focus on quantification rather than detectability. This work presents extensions to a novel phantom design and analysis techniques to evaluate detectability in the context of realistic, non-piecewise constant backgrounds. The design consists of a phantom filled with small solid plastic balls and a radionuclide solution to mimic heterogeneous background uptake. A set of 3D-printed regular dodecahedral `features' were included at user-defined locations within the phantom to create `holes' within the matrix of chaotically-packed balls. These features fill at approximately 3:1 contrast to the lumpy background. A series of signal-known-present (SP) and signal-known-absent (SA) sub-images were generated and used as input for observer studies. This design was imaged in a head-like 20 cm diameter, 20 cm long cylinder and in a body-like 36 cm wide by 21 cm tall by 40 cm long tank. A series of model observer detectability indices were compared across scan conditions (count levels, number of scan replicates), PET image reconstruction methods (with/without TOF and PSF) and between PET/CT scanner system designs using the same phantom imaged on multiple systems. The detectability index was further compared to the noise-equivalent count (NEC) level to characterize the relationship between NEC and observer SNR.

Real-time edge tracking using a tactile sensor

NASA Technical Reports Server (NTRS)

Berger, Alan D.; Volpe, Richard; Khosla, Pradeep K.

1989-01-01

Object recognition through the use of input from multiple sensors is an important aspect of an autonomous manipulation system. In tactile object recognition, it is necessary to determine the location and orientation of object edges and surfaces. A controller is proposed that utilizes a tactile sensor in the feedback loop of a manipulator to track along edges. In the control system, the data from the tactile sensor is first processed to find edges. The parameters of these edges are then used to generate a control signal to a hybrid controller. Theory is presented for tactile edge detection and an edge tracking controller. In addition, experimental verification of the edge tracking controller is presented.

Assessment of Gamma-Ray-Spectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures

DOE PAGES

Alamaniotis, Miltiadis; Tsoukalas, Lefteri H.

2018-01-01

The analysis of measured data plays a significant role in enhancing nuclear nonproliferation mainly by inferring the presence of patterns associated with special nuclear materials. Among various types of measurements, gamma-ray spectra is the widest utilized type of data in nonproliferation applications. In this paper, a method that employs the fireworks algorithm (FWA) for analyzing gamma-ray spectra aiming at detecting gamma signatures is presented. In particular, FWA is utilized to fit a set of known signatures to a measured spectrum by optimizing an objective function, where non-zero coefficients express the detected signatures. FWA is tested on a set of experimentallymore » obtained measurements optimizing various objective functions—MSE, RMSE, Theil-2, MAE, MAPE, MAP—with results exhibiting its potential in providing highly accurate and precise signature detection. Finally and furthermore, FWA is benchmarked against genetic algorithms and multiple linear regression, showing its superiority over those algorithms regarding precision with respect to MAE, MAPE, and MAP measures.« less

Object-based detection of vehicles using combined optical and elevation data

NASA Astrophysics Data System (ADS)

Schilling, Hendrik; Bulatov, Dimitri; Middelmann, Wolfgang

2018-02-01

The detection of vehicles is an important and challenging topic that is relevant for many applications. In this work, we present a workflow that utilizes optical and elevation data to detect vehicles in remotely sensed urban data. This workflow consists of three consecutive stages: candidate identification, classification, and single vehicle extraction. Unlike in most previous approaches, fusion of both data sources is strongly pursued at all stages. While the first stage utilizes the fact that most man-made objects are rectangular in shape, the second and third stages employ machine learning techniques combined with specific features. The stages are designed to handle multiple sensor input, which results in a significant improvement. A detailed evaluation shows the benefits of our workflow, which includes hand-tailored features; even in comparison with classification approaches based on Convolutional Neural Networks, which are state of the art in computer vision, we could obtain a comparable or superior performance (F1 score of 0.96-0.94).

The MACHO Project Sample of Galactic Bulge High-Amplitude δ Scuti Stars: Pulsation Behavior and Stellar Properties

NASA Astrophysics Data System (ADS)

Alcock, C.; Allsman, R. A.; Alves, D. R.; Axelrod, T. S.; Becker, A. C.; Bennett, D. P.; Cook, K. H.; Freeman, K. C.; Geha, M.; Griest, K.; Lehner, M. J.; Marshall, S. L.; McNamara, B. J.; Minniti, D.; Nelson, C.; Peterson, B. A.; Popowski, P.; Pratt, M. R.; Quinn, P. J.; Rodgers, A. W.; Sutherland, W.; Templeton, M. R.; Vandehei, T.; Welch, D. L.

2000-06-01

We have detected 90 objects with periods and light-curve structures similar to those of field δ Scuti stars using the Massive Compact Halo Object (MACHO) Project database of Galactic bulge photometry. If we assume similar extinction values for all candidates and absolute magnitudes similar to those of other field high-amplitude δ Scuti stars (HADS), the majority of these objects lie in or near the Galactic bulge. At least two of these objects are likely foreground δ Scuti stars, one of which may be an evolved nonradial pulsator, similar to other evolved, disk-population δ Scuti stars. We have analyzed the light curves of these objects and find that they are similar to the light curves of field δ Scuti stars and the δ Scuti stars found by the Optical Gravitational Lens Experiment (OGLE). However, the amplitude distribution of these sources lies between those of low- and high-amplitude δ Scuti stars, which suggests that they may be an intermediate population. We have found nine double-mode HADS with frequency ratios ranging from 0.75 to 0.79, four probable double- and multiple-mode objects, and another four objects with marginal detections of secondary modes. The low frequencies (5-14 cycles day-1) and the observed period ratios of ~0.77 suggest that the majority of these objects are evolved stars pulsating in fundamental or first overtone radial modes.

Automatic detection and classification of damage zone(s) for incorporating in digital image correlation technique

NASA Astrophysics Data System (ADS)

Bhattacharjee, Sudipta; Deb, Debasis

2016-07-01

Digital image correlation (DIC) is a technique developed for monitoring surface deformation/displacement of an object under loading conditions. This method is further refined to make it capable of handling discontinuities on the surface of the sample. A damage zone is referred to a surface area fractured and opened in due course of loading. In this study, an algorithm is presented to automatically detect multiple damage zones in deformed image. The algorithm identifies the pixels located inside these zones and eliminate them from FEM-DIC processes. The proposed algorithm is successfully implemented on several damaged samples to estimate displacement fields of an object under loading conditions. This study shows that displacement fields represent the damage conditions reasonably well as compared to regular FEM-DIC technique without considering the damage zones.

Conditional Random Field (CRF)-Boosting: Constructing a Robust Online Hybrid Boosting Multiple Object Tracker Facilitated by CRF Learning

PubMed Central

Yang, Ehwa; Gwak, Jeonghwan; Jeon, Moongu

2017-01-01

Due to the reasonably acceptable performance of state-of-the-art object detectors, tracking-by-detection is a standard strategy for visual multi-object tracking (MOT). In particular, online MOT is more demanding due to its diverse applications in time-critical situations. A main issue of realizing online MOT is how to associate noisy object detection results on a new frame with previously being tracked objects. In this work, we propose a multi-object tracker method called CRF-boosting which utilizes a hybrid data association method based on online hybrid boosting facilitated by a conditional random field (CRF) for establishing online MOT. For data association, learned CRF is used to generate reliable low-level tracklets and then these are used as the input of the hybrid boosting. To do so, while existing data association methods based on boosting algorithms have the necessity of training data having ground truth information to improve robustness, CRF-boosting ensures sufficient robustness without such information due to the synergetic cascaded learning procedure. Further, a hierarchical feature association framework is adopted to further improve MOT accuracy. From experimental results on public datasets, we could conclude that the benefit of proposed hybrid approach compared to the other competitive MOT systems is noticeable. PMID:28304366

Image analysis of multiple moving wood pieces in real time

NASA Astrophysics Data System (ADS)

Wang, Weixing

2006-02-01

This paper presents algorithms for image processing and image analysis of wood piece materials. The algorithms were designed for auto-detection of wood piece materials on a moving conveyor belt or a truck. When wood objects on moving, the hard task is to trace the contours of the objects in n optimal way. To make the algorithms work efficiently in the plant, a flexible online system was designed and developed, which mainly consists of image acquisition, image processing, object delineation and analysis. A number of newly-developed algorithms can delineate wood objects with high accuracy and high speed, and in the wood piece analysis part, each wood piece can be characterized by a number of visual parameters which can also be used for constructing experimental models directly in the system.

An Approach to Extract Moving Objects from Mls Data Using a Volumetric Background Representation

NASA Astrophysics Data System (ADS)

Gehrung, J.; Hebel, M.; Arens, M.; Stilla, U.

2017-05-01

Data recorded by mobile LiDAR systems (MLS) can be used for the generation and refinement of city models or for the automatic detection of long-term changes in the public road space. Since for this task only static structures are of interest, all mobile objects need to be removed. This work presents a straightforward but powerful approach to remove the subclass of moving objects. A probabilistic volumetric representation is utilized to separate MLS measurements recorded by a Velodyne HDL-64E into mobile objects and static background. The method was subjected to a quantitative and a qualitative examination using multiple datasets recorded by a mobile mapping platform. The results show that depending on the chosen octree resolution 87-95% of the measurements are labeled correctly.

Attentional enhancement during multiple-object tracking.

PubMed

Drew, Trafton; McCollough, Andrew W; Horowitz, Todd S; Vogel, Edward K

2009-04-01

What is the role of attention in multiple-object tracking? Does attention enhance target representations, suppress distractor representations, or both? It is difficult to ask this question in a purely behavioral paradigm without altering the very attentional allocation one is trying to measure. In the present study, we used event-related potentials to examine the early visual evoked responses to task-irrelevant probes without requiring an additional detection task. Subjects tracked two targets among four moving distractors and four stationary distractors. Brief probes were flashed on targets, moving distractors, stationary distractors, or empty space. We obtained a significant enhancement of the visually evoked P1 and N1 components (approximately 100-150 msec) for probes on targets, relative to distractors. Furthermore, good trackers showed larger differences between target and distractor probes than did poor trackers. These results provide evidence of early attentional enhancement of tracked target items and also provide a novel approach to measuring attentional allocation during tracking.

Using the time shift in single pushbroom datatakes to detect ships and their heading

NASA Astrophysics Data System (ADS)

Willburger, Katharina A. M.; Schwenk, Kurt

2017-10-01

The detection of ships from remote sensing data has become an essential task for maritime security. The variety of application scenarios includes piracy, illegal fishery, ocean dumping and ships carrying refugees. While techniques using data from SAR sensors for ship detection are widely common, there is only few literature discussing algorithms based on imagery of optical camera systems. A ship detection algorithm for optical pushbroom data has been developed. It takes advantage of the special detector assembly of most of those scanners, which allows apart from the detection of a ship also the calculation of its heading out of a single acquisition. The proposed algorithm for the detection of moving ships was developed with RapidEye imagery. It algorithm consists mainly of three steps: the creation of a land-watermask, the object extraction and the deeper examination of each single object. The latter step is built up by several spectral and geometric filters, making heavy use of the inter-channel displacement typical for pushbroom sensors with multiple CCD lines, finally yielding a set of ships and their direction of movement. The working principle of time-shifted pushbroom sensors and the developed algorithm is explained in detail. Furthermore, we present our first results and give an outlook to future improvements.

ASAS-SN Discovery of a Possible Galactic Nova ASASSN-18ix

NASA Astrophysics Data System (ADS)

Stanek, K. Z.; Kochanek, C. S.; Shields, J. V.; Thompson, T. A.; Chomiuk, L.; Strader, J.; Shappee, B. J.; Holoien, T. W.-S.; Prieto, J. L.; Dong, Subo; Stritzinger, M.

2018-04-01

During the ongoing All Sky Automated Survey for SuperNovae (ASAS-SN, Shappee et al. 2014), using data from multiple ASAS-SN telescopes, we detect a new bright transient source, possibly a classical nova, but it might also be a young, large amplitude outburst of a cataclysmic variable Object RA (J2000) DEC (J2000) Gal l (deg) Gal b (deg) Disc.

New Directions in the Digital Signal Processing of Image Data.

DTIC Science & Technology

1987-05-01

and identify by block number) FIELD GROUP SUB-GROUP Object detection and idLntification 12 01 restoration of photon noise limited imagery 15 04 image...from incomplete information, restoration of blurred images in additive and multiplicative noise , motion analysis with fast hierarchical algorithms...different resolutions. As is well known, the solution to the matched filter problem under additive white noise conditions is the correlation receiver

Automatic mine detection based on multiple features

NASA Astrophysics Data System (ADS)

Yu, Ssu-Hsin; Gandhe, Avinash; Witten, Thomas R.; Mehra, Raman K.

2000-08-01

Recent research sponsored by the Army, Navy and DARPA has significantly advanced the sensor technologies for mine detection. Several innovative sensor systems have been developed and prototypes were built to investigate their performance in practice. Most of the research has been focused on hardware design. However, in order for the systems to be in wide use instead of in limited use by a small group of well-trained experts, an automatic process for mine detection is needed to make the final decision process on mine vs. no mine easier and more straightforward. In this paper, we describe an automatic mine detection process consisting of three stage, (1) signal enhancement, (2) pixel-level mine detection, and (3) object-level mine detection. The final output of the system is a confidence measure that quantifies the presence of a mine. The resulting system was applied to real data collected using radar and acoustic technologies.

Particle detection systems and methods

DOEpatents

Morris, Christopher L.; Makela, Mark F.

2010-05-11

Techniques, apparatus and systems for detecting particles such as muons and neutrons. In one implementation, a particle detection system employs a plurality of drift cells, which can be for example sealed gas-filled drift tubes, arranged on sides of a volume to be scanned to track incoming and outgoing charged particles, such as cosmic ray-produced muons. The drift cells can include a neutron sensitive medium to enable concurrent counting of neutrons. The system can selectively detect devices or materials, such as iron, lead, gold, uranium, plutonium, and/or tungsten, occupying the volume from multiple scattering of the charged particles passing through the volume and can concurrently detect any unshielded neutron sources occupying the volume from neutrons emitted therefrom. If necessary, the drift cells can be used to also detect gamma rays. The system can be employed to inspect occupied vehicles at border crossings for nuclear threat objects.

Inheritance on processes, exemplified on distributed termination detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomsen, K.S.

1987-02-01

A multiple inheritance mechanism on processes is designed and presented within the framework of a small object oriented language. Processes are described in classes, and the different action parts of a process inherited from different classes are executed in a coroutine-like style called alternation. The inheritance mechanism is a useful tool for factorizing the description of common aspects of processes. This is demonstrated within the domain of distributed programming by using the inheritance mechanism to factorize the description of distributed termination detection algorithms from the description of the distributed main computations for which termination is to be detected. A clearmore » separation of concerns is obtained, and arbitrary combinations of terminations detection algorithms and main computations can be formed. The same termination detection classes can also be used for more general purposes within distributed programming, such as detecting termination of each phase in a multi-phase main computation.« less

The fundamentals of average local variance--Part I: Detecting regular patterns.

PubMed

Bøcher, Peder Klith; McCloy, Keith R

2006-02-01

The method of average local variance (ALV) computes the mean of the standard deviation values derived for a 3 x 3 moving window on a successively coarsened image to produce a function of ALV versus spatial resolution. In developing ALV, the authors used approximately a doubling of the pixel size at each coarsening of the image. They hypothesized that ALV is low when the pixel size is smaller than the size of scene objects because the pixels on the object will have similar response values. When the pixel and objects are of similar size, they will tend to vary in response and the ALV values will increase. As the size of pixels increase further, more objects will be contained in a single pixel and ALV will decrease. The authors showed that various cover types produced single peak ALV functions that inexplicitly peaked when the pixel size was 1/2 to 3/4 of the object size. This paper reports on work done to explore the characteristics of the various forms of the ALV function and to understand the location of the peaks that occur in this function. The work was conducted using synthetically generated image data. The investigation showed that the hypothesis originally proposed in is not adequate. A new hypothesis is proposed that the ALV function has peak locations that are related to the geometric size of pattern structures in the scene. These structures are not always the same as scene objects. Only in cases where the size of and separation between scene objects are equal does the ALV function detect the size of the objects. In situations where the distance between scene objects are larger than their size, the ALV function has a peak at the object separation, not at the object size. This work has also shown that multiple object structures of different sizes and distances in the image provide multiple peaks in the ALV function and that some of these structures are not implicitly recognized as such from our perspective. However, the magnitude of these peaks depends on the response mix in the structures, complicating their interpretation and analysis. The analysis of the ALV Function is, thus, more complex than that generally reported in the literature.

Operations management system advanced automation: Fault detection isolation and recovery prototyping

NASA Technical Reports Server (NTRS)

Hanson, Matt

1990-01-01

The purpose of this project is to address the global fault detection, isolation and recovery (FDIR) requirements for Operation's Management System (OMS) automation within the Space Station Freedom program. This shall be accomplished by developing a selected FDIR prototype for the Space Station Freedom distributed processing systems. The prototype shall be based on advanced automation methodologies in addition to traditional software methods to meet the requirements for automation. A secondary objective is to expand the scope of the prototyping to encompass multiple aspects of station-wide fault management (SWFM) as discussed in OMS requirements documentation.

Infrared-Proximity-Sensor Modules For Robot

NASA Technical Reports Server (NTRS)

Parton, William; Wegerif, Daniel; Rosinski, Douglas

1995-01-01

Collision-avoidance system for articulated robot manipulators uses infrared proximity sensors grouped together in array of sensor modules. Sensor modules, called "sensorCells," distributed processing board-level products for acquiring data from proximity-sensors strategically mounted on robot manipulators. Each sensorCell self-contained and consists of multiple sensing elements, discrete electronics, microcontroller and communications components. Modules connected to central control computer by redundant serial digital communication subsystem including both serial and a multi-drop bus. Detects objects made of various materials at distance of up to 50 cm. For some materials, such as thermal protection system tiles, detection range reduced to approximately 20 cm.

CAFÉ-BEANS: An exhaustive hunt for high-mass binaries

NASA Astrophysics Data System (ADS)

Negueruela, I.; Maíz-Apellániz, J.; Simón-Díaz, S.; Alfaro, E. J.; Herrero, A.; Alonso, J.; Barbá, R.; Lorenzo, J.; Marco, A.; Monguió, M.; Morrell, N.; Pellerin, A.; Sota, A.; Walborn, N. R.

2015-05-01

CAFÉ-BEANS is an on-going survey running on the 2.2 m telescope at Calar Alto. For more than two years, CAFÉ-BEANS has been collecting high-resolution spectra of early-type stars with the aim of detecting and characterising spectroscopic binaries. The main goal of this project is a thorough characterisation of multiplicity in high-mass stars by detecting all spectroscopic and visual binaries in a large sample of Galactic O-type stars, and solving their orbits. Our final objective is eliminating all biases in the high-mass-star IMF created by undetected binaries.

A systematic comparison between visual cues for boundary detection.

PubMed

Mély, David A; Kim, Junkyung; McGill, Mason; Guo, Yuliang; Serre, Thomas

2016-03-01

The detection of object boundaries is a critical first step for many visual processing tasks. Multiple cues (we consider luminance, color, motion and binocular disparity) available in the early visual system may signal object boundaries but little is known about their relative diagnosticity and how to optimally combine them for boundary detection. This study thus aims at understanding how early visual processes inform boundary detection in natural scenes. We collected color binocular video sequences of natural scenes to construct a video database. Each scene was annotated with two full sets of ground-truth contours (one set limited to object boundaries and another set which included all edges). We implemented an integrated computational model of early vision that spans all considered cues, and then assessed their diagnosticity by training machine learning classifiers on individual channels. Color and luminance were found to be most diagnostic while stereo and motion were least. Combining all cues yielded a significant improvement in accuracy beyond that of any cue in isolation. Furthermore, the accuracy of individual cues was found to be a poor predictor of their unique contribution for the combination. This result suggested a complex interaction between cues, which we further quantified using regularization techniques. Our systematic assessment of the accuracy of early vision models for boundary detection together with the resulting annotated video dataset should provide a useful benchmark towards the development of higher-level models of visual processing. Copyright © 2016 Elsevier Ltd. All rights reserved.

A real-time method for autonomous passive acoustic detection-classification of humpback whales.

PubMed

Abbot, Ted A; Premus, Vincent E; Abbot, Philip A

2010-05-01

This paper describes a method for real-time, autonomous, joint detection-classification of humpback whale vocalizations. The approach adapts the spectrogram correlation method used by Mellinger and Clark [J. Acoust. Soc. Am. 107, 3518-3529 (2000)] for bowhead whale endnote detection to the humpback whale problem. The objective is the implementation of a system to determine the presence or absence of humpback whales with passive acoustic methods and to perform this classification with low false alarm rate in real time. Multiple correlation kernels are used due to the diversity of humpback song. The approach also takes advantage of the fact that humpbacks tend to vocalize repeatedly for extended periods of time, and identification is declared only when multiple song units are detected within a fixed time interval. Humpback whale vocalizations from Alaska, Hawaii, and Stellwagen Bank were used to train the algorithm. It was then tested on independent data obtained off Kaena Point, Hawaii in February and March of 2009. Results show that the algorithm successfully classified humpback whales autonomously in real time, with a measured probability of correct classification in excess of 74% and a measured probability of false alarm below 1%.

A fluorescence-based centrifugal microfluidic system for parallel detection of multiple allergens

NASA Astrophysics Data System (ADS)

Chen, Q. L.; Ho, H. P.; Cheung, K. L.; Kong, S. K.; Suen, Y. K.; Kwan, Y. W.; Li, W. J.; Wong, C. K.

2010-02-01

This paper reports a robust polymer based centrifugal microfluidic analysis system that can provide parallel detection of multiple allergens in vitro. Many commercial food products (milk, bean, pollen, etc.) may introduce allergy to people. A low-cost device for rapid detection of allergens is highly desirable. With this as the objective, we have studied the feasibility of using a rotating disk device incorporating centrifugal microfluidics for performing actuationfree and multi-analyte detection of different allergen species with minimum sample usage and fast response time. Degranulation in basophils or mast cells is an indicator to demonstrate allergic reaction. In this connection, we used acridine orange (AO) to demonstrate degranulation in KU812 human basophils. It was found that the AO was released from granules when cells were stimulated by ionomycin, thus signifying the release of histamine which accounts for allergy symptoms [1-2]. Within this rotating optical platform, major microfluidic components including sample reservoirs, reaction chambers, microchannel and flow-control compartments are integrated into a single bio-compatible polydimethylsiloxane (PDMS) substrate. The flow sequence and reaction time can be controlled precisely. Sequentially through varying the spinning speed, the disk may perform a variety of steps on sample loading, reaction and detection. Our work demonstrates the feasibility of using centrifugation as a possible immunoassay system in the future.

Experimental Demonstration of Adaptive Infrared Multispectral Imaging using Plasmonic Filter Array

PubMed Central

Jang, Woo-Yong; Ku, Zahyun; Jeon, Jiyeon; Kim, Jun Oh; Lee, Sang Jun; Park, James; Noyola, Michael J.; Urbas, Augustine

2016-01-01

In our previous theoretical study, we performed target detection using a plasmonic sensor array incorporating the data-processing technique termed “algorithmic spectrometry”. We achieved the reconstruction of a target spectrum by extracting intensity at multiple wavelengths with high resolution from the image data obtained from the plasmonic array. The ultimate goal is to develop a full-scale focal plane array with a plasmonic opto-coupler in order to move towards the next generation of versatile infrared cameras. To this end, and as an intermediate step, this paper reports the experimental demonstration of adaptive multispectral imagery using fabricated plasmonic spectral filter arrays and proposed target detection scenarios. Each plasmonic filter was designed using periodic circular holes perforated through a gold layer, and an enhanced target detection strategy was proposed to refine the original spectrometry concept for spatial and spectral computation of the data measured from the plasmonic array. Both the spectrum of blackbody radiation and a metal ring object at multiple wavelengths were successfully reconstructed using the weighted superposition of plasmonic output images as specified in the proposed detection strategy. In addition, plasmonic filter arrays were theoretically tested on a target at extremely high temperature as a challenging scenario for the detection scheme. PMID:27721506

Threshold selection for classification of MR brain images by clustering method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moldovanu, Simona; Dumitru Moţoc High School, 15 Milcov St., 800509, Galaţi; Obreja, Cristian

Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzedmore » images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.« less

Resolving occlusion and segmentation errors in multiple video object tracking

NASA Astrophysics Data System (ADS)

Cheng, Hsu-Yung; Hwang, Jenq-Neng

2009-02-01

In this work, we propose a method to integrate the Kalman filter and adaptive particle sampling for multiple video object tracking. The proposed framework is able to detect occlusion and segmentation error cases and perform adaptive particle sampling for accurate measurement selection. Compared with traditional particle filter based tracking methods, the proposed method generates particles only when necessary. With the concept of adaptive particle sampling, we can avoid degeneracy problem because the sampling position and range are dynamically determined by parameters that are updated by Kalman filters. There is no need to spend time on processing particles with very small weights. The adaptive appearance for the occluded object refers to the prediction results of Kalman filters to determine the region that should be updated and avoids the problem of using inadequate information to update the appearance under occlusion cases. The experimental results have shown that a small number of particles are sufficient to achieve high positioning and scaling accuracy. Also, the employment of adaptive appearance substantially improves the positioning and scaling accuracy on the tracking results.

Stereo vision tracking of multiple objects in complex indoor environments.

PubMed

Marrón-Romera, Marta; García, Juan C; Sotelo, Miguel A; Pizarro, Daniel; Mazo, Manuel; Cañas, José M; Losada, Cristina; Marcos, Alvaro

2010-01-01

This paper presents a novel system capable of solving the problem of tracking multiple targets in a crowded, complex and dynamic indoor environment, like those typical of mobile robot applications. The proposed solution is based on a stereo vision set in the acquisition step and a probabilistic algorithm in the obstacles position estimation process. The system obtains 3D position and speed information related to each object in the robot's environment; then it achieves a classification between building elements (ceiling, walls, columns and so on) and the rest of items in robot surroundings. All objects in robot surroundings, both dynamic and static, are considered to be obstacles but the structure of the environment itself. A combination of a Bayesian algorithm and a deterministic clustering process is used in order to obtain a multimodal representation of speed and position of detected obstacles. Performance of the final system has been tested against state of the art proposals; test results validate the authors' proposal. The designed algorithms and procedures provide a solution to those applications where similar multimodal data structures are found.

Refractive multiple optical tweezers for parallel biochemical analysis in micro-fluidics

NASA Astrophysics Data System (ADS)

Merenda, Fabrice; Rohner, Johann; Pascoal, Pedro; Fournier, Jean-Marc; Vogel, Horst; Salathé, René-Paul

2007-02-01

We present a multiple laser tweezers system based on refractive optics. The system produces an array of 100 optical traps thanks to a refractive microlens array, whose focal plane is imaged into the focal plane of a high-NA microscope objective. This refractive multi-tweezers system is combined to micro-fluidics, aiming at performing simultaneous biochemical reactions on ensembles of free floating objects. Micro-fluidics allows both transporting the particles to the trapping area, and conveying biochemical reagents to the trapped particles. Parallel trapping in micro-fluidics is achieved with polystyrene beads as well as with native vesicles produced from mammalian cells. The traps can hold objects against fluid flows exceeding 100 micrometers per second. Parallel fluorescence excitation and detection on the ensemble of trapped particles is also demonstrated. Additionally, the system is capable of selectively and individually releasing particles from the tweezers array using a complementary steerable laser beam. Strategies for high-yield particle capture and individual particle release in a micro-fluidic environment are discussed. A comparison with diffractive optical tweezers enhances the pros and cons of refractive systems.

Automatic Polyp Detection via A Novel Unified Bottom-up and Top-down Saliency Approach.

PubMed

Yuan, Yixuan; Li, Dengwang; Meng, Max Q-H

2017-07-31

In this paper, we propose a novel automatic computer-aided method to detect polyps for colonoscopy videos. To find the perceptually and semantically meaningful salient polyp regions, we first segment images into multilevel superpixels. Each level corresponds to different sizes of superpixels. Rather than adopting hand-designed features to describe these superpixels in images, we employ sparse autoencoder (SAE) to learn discriminative features in an unsupervised way. Then a novel unified bottom-up and top-down saliency method is proposed to detect polyps. In the first stage, we propose a weak bottom-up (WBU) saliency map by fusing the contrast based saliency and object-center based saliency together. The contrast based saliency map highlights image parts that show different appearances compared with surrounding areas while the object-center based saliency map emphasizes the center of the salient object. In the second stage, a strong classifier with Multiple Kernel Boosting (MKB) is learned to calculate the strong top-down (STD) saliency map based on samples directly from the obtained multi-level WBU saliency maps. We finally integrate these two stage saliency maps from all levels together to highlight polyps. Experiment results achieve 0.818 recall for saliency calculation, validating the effectiveness of our method. Extensive experiments on public polyp datasets demonstrate that the proposed saliency algorithm performs favorably against state-of-the-art saliency methods to detect polyps.

VizieR Online Data Catalog: Visible colors of Centaurs and KBOs (Peixinho+, 2015)

NASA Astrophysics Data System (ADS)

Peixinho, N.; Delsanti, A.; Doressoundiram, A.

2015-02-01

Table 2: significant Spearman-rho correlations detected between all colors and all orbital parameters of Centaurs, scattered disk objects, scattered or detached objects, Plutinos, other resonants, classical KBOs, binary or multiple KBOs, KBOs (without Haumea family and retrograde orbits), all objects (also without Haumea family and retrograde orbits), and KBOs except classical KBOs (also without Haumea family and retrograde orbits). First and second columns indicate the variables, third column the number of objects with both variables measured, forth column indicate the correlation value and its 68.2% error interval, fifth column indicates the p-value of the correlation, sixth column indicate the equivalent confidence level of the p-value in Gaussian sigmas, columns seven to nine indicate the detail of the False Discovery Correction for confidence levels of 2.5σ and 3σ (see Sect. 3.4), tenth column indicates the maximum detectable rho at a 2.5σ confidence level with a 10% risk of missing it, eleventh column indicates the maximum detectable rho at a 3σ confidence level with a 10% risk of missing it (see Sect. 3.2) Table 5: Compilation of R-band absolute magnitude, not corrected for the phase-angle, of Spectral gradient, B-V, V-R, R-I, V-I, B-I, B-R, and corresponding orbital and orbital related parameters of 366 Centaurs and KBOs. For each object/observation, we computed the reflectance spectrum using equation (3) from Delsanti et al. (2001A&A...380..347D), when 2 or more filters were available. The resulting spectra were manually checked, and obviously deviant data from a given filter were removed from the dataset. Color indexes are computed within one given epoch, leading to colors obtained from "simultaneous" photometry (the different bands were observed over a maximum timespan of 2 hours). Then the average colors indexes and their one σ errors from different papers and epochs are computed for each object using equations (1) and (2) from Hainaut and Delsanti (2002A&A...389..641H), providing more accurate estimates when multiple measurements are available. Absolute magnitude in R band (HR) are computed for each object/epoch whenever a R-band magnitude is available, using: HR=R-5log(rΔ), where R is the R-band magnitude, r and Δ the helio- and geocentric distances at the time of observations, respectively.Different values for a given object were also averaged using the aforementioned equations (1) and (2). We did not correct for any phase effect. (5 data files).

Detection and Localization of Subsurface Two-Dimensional Metallic Objects

NASA Astrophysics Data System (ADS)

Meschino, S.; Pajewski, L.; Schettini, G.

2009-04-01

"Roma Tre" University, Applied Electronics Dept.v. Vasca Navale 84, 00146 Rome, Italy Non-invasive identification of buried objects in the near-field of a receiver array is a subject of great interest, due to its application to the remote sensing of the earth's subsurface, to the detection of landmines, pipes, conduits, to the archaeological site characterization, and more. In this work, we present a Sub-Array Processing (SAP) approach for the detection and localization of subsurface perfectly-conducting circular cylinders. We consider a plane wave illuminating the region of interest, which is assumed to be a homogeneous, unlossy medium of unknown permittivity containing one or more targets. In a first step, we partition the receiver array so that the field scattered from the targets result to be locally plane at each sub-array. Then, we apply a Direction of Arrival (DOA) technique to obtain a set of angles for each locally plane wave, and triangulate these directions obtaining a collection of crossing crowding in the expected object locations [1]. We compare several DOA algorithms such as the traditional Bartlett and Capon Beamforming, the Pisarenko Harmonic Decomposition (PHD), the Minimum-Norm method, the Multiple Signal Classification (MUSIC) and the Estimation of Signal Parameters via Rotational Techinque (ESPRIT) [2]. In a second stage, we develop a statistical Poisson based model to manage the crossing pattern in order to extract the probable target's centre position. In particular, if the crossings are Poisson distributed, it is possible to feature two different distribution parameters [3]. These two parameters perform two density rate for the crossings, so that we can previously divide the crossing pattern in a certain number of equal-size windows and we can collect the windows of the crossing pattern with low rate parameters (that probably are background windows) and remove them. In this way we can consider only the high rate parameter windows (that most probably locate the target) and extract the center position of the object. We also consider some other localization-connected aspects. For example how to obtain a likely estimation of the soil permittivity and of the cylinders radius. Finally, when multiple objects are present, we refine our localization procedure by performing a Clustering Analysis of the crossing pattern. In particular, we apply the K-means algorithm to extract the coordinates of the objects centroids and the clusters extension. References [1] Şahin A., Miller L., "Object Detection Using High Resolution Near-Field Array Processing", IEEE Trans. on Geoscience and Remote Sensing, vol.39, no.1, Jan. 2001, pp. 136-141. [2] Gross F.B., "Smart Antennas for Wireless Communications", Mc.Graw-Hill 2005. [3] Hoaglin D.C., "A Poisonnes Plot", The American Statistician, vol.34, no.3 August 1980, pp.146-149.

An automated data exploitation system for airborne sensors

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike

2014-06-01

Advanced wide area persistent surveillance (WAPS) sensor systems on manned or unmanned airborne vehicles are essential for wide-area urban security monitoring in order to protect our people and our warfighter from terrorist attacks. Currently, human (imagery) analysts process huge data collections from full motion video (FMV) for data exploitation and analysis (real-time and forensic), providing slow and inaccurate results. An Automated Data Exploitation System (ADES) is urgently needed. In this paper, we present a recently developed ADES for airborne vehicles under heavy urban background clutter conditions. This system includes four processes: (1) fast image registration, stabilization, and mosaicking; (2) advanced non-linear morphological moving target detection; (3) robust multiple target (vehicles, dismounts, and human) tracking (up to 100 target tracks); and (4) moving or static target/object recognition (super-resolution). Test results with real FMV data indicate that our ADES can reliably detect, track, and recognize multiple vehicles under heavy urban background clutters. Furthermore, our example shows that ADES as a baseline platform can provide capability for vehicle abnormal behavior detection to help imagery analysts quickly trace down potential threats and crimes.

The Color “Fruit”: Object Memories Defined by Color

PubMed Central

Lewis, David E.; Pearson, Joel; Khuu, Sieu K.

2013-01-01

Most fruits and other highly color-diagnostic objects have color as a central aspect of their identity, which can facilitate detection and visual recognition. It has been theorized that there may be a large amount of overlap between the neural representations of these objects and processing involved in color perception. In accordance with this theory we sought to determine if the recognition of highly color diagnostic fruit objects could be facilitated by the visual presentation of their known color associates. In two experiments we show that color associate priming is possible, but contingent upon multiple factors. Color priming was found to be maximally effective for the most highly color diagnostic fruits, when low spatial-frequency information was present in the image, and when determination of the object's specific identity, not merely its category, was required. These data illustrate the importance of color for determining the identity of certain objects, and support the theory that object knowledge involves sensory specific systems. PMID:23717677

Object-graphs for context-aware visual category discovery.

PubMed

Lee, Yong Jae; Grauman, Kristen

2012-02-01

How can knowing about some categories help us to discover new ones in unlabeled images? Unsupervised visual category discovery is useful to mine for recurring objects without human supervision, but existing methods assume no prior information and thus tend to perform poorly for cluttered scenes with multiple objects. We propose to leverage knowledge about previously learned categories to enable more accurate discovery, and address challenges in estimating their familiarity in unsegmented, unlabeled images. We introduce two variants of a novel object-graph descriptor to encode the 2D and 3D spatial layout of object-level co-occurrence patterns relative to an unfamiliar region and show that by using them to model the interaction between an image’s known and unknown objects, we can better detect new visual categories. Rather than mine for all categories from scratch, our method identifies new objects while drawing on useful cues from familiar ones. We evaluate our approach on several benchmark data sets and demonstrate clear improvements in discovery over conventional purely appearance-based baselines.

Space Debris Measurements using the Advanced Modular Incoherent Scatter Radar

NASA Astrophysics Data System (ADS)

Nicolls, M.

The Advanced Modular Incoherent Scatter Radar (AMISR) is a modular, mobile UHF phased-array radar facility developed and used for scientific studies of the ionosphere. The radars are completely remotely operated and allow for pulse-to-pulse beam steering over the field-of-view. A satellite and debris tracking capability fully interleaved with scientific operations has been developed, and the AMISR systems are now used to routinely observe LEO space debris, with the ability to simultaneously track and detect multiple objects. The system makes use of wide-bandwidth radar pulses and coherent processing to detect objects as small as 5-10 cm in size through LEO, achieving a range resolution better than 20 meters for LEO targets. The interleaved operations allow for ionospheric effects on UHF space debris measurements, such as dispersion, to be assessed. The radar architecture, interleaved operations, and impact of space weather on the measurements will be discussed.

Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

NASA Astrophysics Data System (ADS)

Hamdan, H. G. Muhammad; Khalifah, O. O.

2017-11-01

Object detection and tracking is a field of research that has many applications in the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT). In this paper, a traffic intensity monitoring system is implemented based on the Macroscopic Urban Traffic model is proposed using computer vision as its source. The input of this program is extracted from a traffic surveillance camera which has another program running a neural network classification which can identify and differentiate the vehicle type is implanted. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated. relevant articles in literature searches, great care should be taken in constructing both. Lastly the limitation and the future work is concluded.

Optimal Sensor Scheduling for Multiple Hypothesis Testing

DTIC Science & Technology

1981-09-01

Naval Research, under contract N00014-77-0532 is gratpfully acknowledged. 2 Laboratory for Information and Decision Systems , MIT Room 35-213, Cambridge...treat the more general problem [9,10]. However, two common threads connect these approaches: they obtain feedback laws mapping posterior destributions ...objective of a detection or identification algorithm is to produce correct estimates of the true state of a system . It is also bene- ficial if these

Bayesian Multiscale Modeling of Closed Curves in Point Clouds

PubMed Central

Gu, Kelvin; Pati, Debdeep; Dunson, David B.

2014-01-01

Modeling object boundaries based on image or point cloud data is frequently necessary in medical and scientific applications ranging from detecting tumor contours for targeted radiation therapy, to the classification of organisms based on their structural information. In low-contrast images or sparse and noisy point clouds, there is often insufficient data to recover local segments of the boundary in isolation. Thus, it becomes critical to model the entire boundary in the form of a closed curve. To achieve this, we develop a Bayesian hierarchical model that expresses highly diverse 2D objects in the form of closed curves. The model is based on a novel multiscale deformation process. By relating multiple objects through a hierarchical formulation, we can successfully recover missing boundaries by borrowing structural information from similar objects at the appropriate scale. Furthermore, the model’s latent parameters help interpret the population, indicating dimensions of significant structural variability and also specifying a ‘central curve’ that summarizes the collection. Theoretical properties of our prior are studied in specific cases and efficient Markov chain Monte Carlo methods are developed, evaluated through simulation examples and applied to panorex teeth images for modeling teeth contours and also to a brain tumor contour detection problem. PMID:25544786

LOGISMOS—Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces: Cartilage Segmentation in the Knee Joint

PubMed Central

Zhang, Xiangmin; Williams, Rachel; Wu, Xiaodong; Anderson, Donald D.; Sonka, Milan

2011-01-01

A novel method for simultaneous segmentation of multiple interacting surfaces belonging to multiple interacting objects, called LOGISMOS (layered optimal graph image segmentation of multiple objects and surfaces), is reported. The approach is based on the algorithmic incorporation of multiple spatial inter-relationships in a single n-dimensional graph, followed by graph optimization that yields a globally optimal solution. The LOGISMOS method’s utility and performance are demonstrated on a bone and cartilage segmentation task in the human knee joint. Although trained on only a relatively small number of nine example images, this system achieved good performance. Judged by dice similarity coefficients (DSC) using a leave-one-out test, DSC values of 0.84 ± 0.04, 0.80 ± 0.04 and 0.80 ± 0.04 were obtained for the femoral, tibial, and patellar cartilage regions, respectively. These are excellent DSC values, considering the narrow-sheet character of the cartilage regions. Similarly, low signed mean cartilage thickness errors were obtained when compared to a manually-traced independent standard in 60 randomly selected 3-D MR image datasets from the Osteoarthritis Initiative database—0.11 ± 0.24, 0.05 ± 0.23, and 0.03 ± 0.17 mm for the femoral, tibial, and patellar cartilage thickness, respectively. The average signed surface positioning errors for the six detected surfaces ranged from 0.04 ± 0.12 mm to 0.16 ± 0.22 mm. The reported LOGISMOS framework provides robust and accurate segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multiobject multisurface segmentation problems. PMID:20643602

The impact of attentional, linguistic, and visual features during object naming

PubMed Central

Clarke, Alasdair D. F.; Coco, Moreno I.; Keller, Frank

2013-01-01

Object detection and identification are fundamental to human vision, and there is mounting evidence that objects guide the allocation of visual attention. However, the role of objects in tasks involving multiple modalities is less clear. To address this question, we investigate object naming, a task in which participants have to verbally identify objects they see in photorealistic scenes. We report an eye-tracking study that investigates which features (attentional, visual, and linguistic) influence object naming. We find that the amount of visual attention directed toward an object, its position and saliency, along with linguistic factors such as word frequency, animacy, and semantic proximity, significantly influence whether the object will be named or not. We then ask how features from different modalities are combined during naming, and find significant interactions between saliency and position, saliency and linguistic features, and attention and position. We conclude that when the cognitive system performs tasks such as object naming, it uses input from one modality to constraint or enhance the processing of other modalities, rather than processing each input modality independently. PMID:24379792

Detection of buried objects by fusing dual-band infrared images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, G.A.; Sengupta, S.K.; Sherwood, R.J.

1993-11-01

We have conducted experiments to demonstrate the enhanced detectability of buried land mines using sensor fusion techniques. Multiple sensors, including visible imagery, infrared imagery, and ground penetrating radar (GPR), have been used to acquire data on a number of buried mines and mine surrogates. Because the visible wavelength and GPR data are currently incomplete. This paper focuses on the fusion of two-band infrared images. We use feature-level fusion and supervised learning with the probabilistic neural network (PNN) to evaluate detection performance. The novelty of the work lies in the application of advanced target recognition algorithms, the fusion of dual-band infraredmore » images and evaluation of the techniques using two real data sets.« less

Design of short-range terahertz wave passive detecting system

NASA Astrophysics Data System (ADS)

Zhang, Chao; Lou, Guowei; Zhu, Li; Qian, Songsong; Li, Ting

2016-09-01

Based on the study of radiation and transmission characteristics on THz waveband, a short-range passive detecting system is designed. The scheme originated from microwave passive detecting system. A prototype was developed following the design of key components including antennas and a harmonic mixer. The system operated at 0.36 THz. A dual-beam Cassegrain antenna was adopted for receiving signals which radiated by object and background. Local oscillator signal was generated by frequency multiplication. Harmonic mixing is adopted for reducing local oscillator signal frequency required by half. Superheterodyne technology is employed for signal acquisition. The system implemented easily. Tests and measurements were taken, which showed that the scheme was feasible and the performance of the prototype system met the design requirements.

Open-Filter Optical SSA Analysis Considerations

NASA Astrophysics Data System (ADS)

Lambert, J.

2016-09-01

Optical Space Situational Awareness (SSA) sensors used for space object detection and orbit refinement measurements are typically operated in an "open-filter" mode without any spectral filters to maximize sensitivity and signal-to-noise. These same optical brightness measurements are often also employed for size determination (e.g., for orbital debris), object correlation, and object status change. These functions, especially when performed using multiple sensors, are highly dependent on sensor calibration for measurement accuracy. Open-filter SSA sensors are traditionally calibrated against the cataloged visual magnitudes of solar-type stars which have similar spectral distributions as the illuminating source, the Sun. The stellar calibration is performed to a high level of accuracy, a few hundredths of a magnitude, by observing many stars over a range of elevation angles to determine sensor, telescope, and atmospheric effects. However, space objects have individual color properties which alter the reflected solar illumination producing spectral distributions which differ from those of the calibration stars. When the stellar calibrations are applied to the space object measurements, visual magnitude values are obtained which are systematically biased. These magnitudes combined with the unknown Bond albedos of the space objects result in systematically biased size determinations which will differ between sensors. Measurements of satellites of known sizes and surface materials have been analyzed to characterize these effects. The results have combined into standardized Bond albedos to correct the measured magnitudes into object sizes. However, the actual albedo values will vary between objects and represent a mean correction subject to some uncertainty. The objective of this discussion is to characterize the sensor spectral biases that are present in open-filter optical observations and examine the resulting brightness and albedo uncertainties that should accompany object size, correlation, or status change determinations, especially in the SSA analyses of individual space objects using data from multiple sensors.

Exploration of available feature detection and identification systems and their performance on radiographs

NASA Astrophysics Data System (ADS)

Wantuch, Andrew C.; Vita, Joshua A.; Jimenez, Edward S.; Bray, Iliana E.

2016-10-01

Despite object detection, recognition, and identification being very active areas of computer vision research, many of the available tools to aid in these processes are designed with only photographs in mind. Although some algorithms used specifically for feature detection and identification may not take explicit advantage of the colors available in the image, they still under-perform on radiographs, which are grayscale images. We are especially interested in the robustness of these algorithms, specifically their performance on a preexisting database of X-ray radiographs in compressed JPEG form, with multiple ways of describing pixel information. We will review various aspects of the performance of available feature detection and identification systems, including MATLABs Computer Vision toolbox, VLFeat, and OpenCV on our non-ideal database. In the process, we will explore possible reasons for the algorithms' lessened ability to detect and identify features from the X-ray radiographs.

Object acquisition and tracking for space-based surveillance

NASA Astrophysics Data System (ADS)

1991-11-01

This report presents the results of research carried out by Space Computer Corporation under the U.S. government's Small Business Innovation Research (SBIR) Program. The work was sponsored by the Strategic Defense Initiative Organization and managed by the Office of Naval Research under Contracts N00014-87-C-0801 (Phase 1) and N00014-89-C-0015 (Phase 2). The basic purpose of this research was to develop and demonstrate a new approach to the detection of, and initiation of track on, moving targets using data from a passive infrared or visual sensor. This approach differs in very significant ways from the traditional approach of dividing the required processing into time dependent, object dependent, and data dependent processing stages. In that approach individual targets are first detected in individual image frames, and the detections are then assembled into tracks. That requires that the signal to noise ratio in each image frame be sufficient for fairly reliable target detection. In contrast, our approach bases detection of targets on multiple image frames, and, accordingly, requires a smaller signal to noise ratio. It is sometimes referred to as track before detect, and can lead to a significant reduction in total system cost. For example, it can allow greater detection range for a single sensor, or it can allow the use of smaller sensor optics. Both the traditional and track before detect approaches are applicable to systems using scanning sensors, as well as those which use staring sensors.

Object acquisition and tracking for space-based surveillance. Final report, Dec 88-May 90

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1991-11-27

This report presents the results of research carried out by Space Computer Corporation under the U.S. government's Small Business Innovation Research (SBIR) Program. The work was sponsored by the Strategic Defense Initiative Organization and managed by the Office of Naval Research under Contracts N00014-87-C-0801 (Phase I) and N00014-89-C-0015 (Phase II). The basic purpose of this research was to develop and demonstrate a new approach to the detection of, and initiation of track on, moving targets using data from a passive infrared or visual sensor. This approach differs in very significant ways from the traditional approach of dividing the required processingmore » into time dependent, object-dependent, and data-dependent processing stages. In that approach individual targets are first detected in individual image frames, and the detections are then assembled into tracks. That requires that the signal to noise ratio in each image frame be sufficient for fairly reliable target detection. In contrast, our approach bases detection of targets on multiple image frames, and, accordingly, requires a smaller signal to noise ratio. It is sometimes referred to as track before detect, and can lead to a significant reduction in total system cost. For example, it can allow greater detection range for a single sensor, or it can allow the use of smaller sensor optics. Both the traditional and track before detect approaches are applicable to systems using scanning sensors, as well as those which use staring sensors.« less

Fish Geometry and Electric Organ Discharge Determine Functional Organization of the Electrosensory Epithelium

PubMed Central

Sanguinetti-Scheck, Juan Ignacio; Pedraja, Eduardo Federico; Cilleruelo, Esteban; Migliaro, Adriana; Aguilera, Pedro; Caputi, Angel Ariel; Budelli, Ruben

2011-01-01

Active electroreception in Gymnotus omarorum is a sensory modality that perceives the changes that nearby objects cause in a self generated electric field. The field is emitted as repetitive stereotyped pulses that stimulate skin electroreceptors. Differently from mormyriformes electric fish, gymnotiformes have an electric organ distributed along a large portion of the body, which fires sequentially. As a consequence shape and amplitude of both, the electric field generated and the image of objects, change during the electric pulse. To study how G. omarorum constructs a perceptual representation, we developed a computational model that allows the determination of the self-generated field and the electric image. We verify and use the model as a tool to explore image formation in diverse experimental circumstances. We show how the electric images of objects change in shape as a function of time and position, relative to the fish's body. We propose a theoretical framework about the organization of the different perceptive tasks made by electroreception: 1) At the head region, where the electrosensory mosaic presents an electric fovea, the field polarizing nearby objects is coherent and collimated. This favors the high resolution sampling of images of small objects and perception of electric color. Besides, the high sensitivity of the fovea allows the detection and tracking of large faraway objects in rostral regions. 2) In the trunk and tail region a multiplicity of sources illuminate different regions of the object, allowing the characterization of the shape and position of a large object. In this region, electroreceptors are of a unique type and capacitive detection should be based in the pattern of the afferents response. 3) Far from the fish, active electroreception is not possible but the collimated field is suitable to be used for electrocommunication and detection of large objects at the sides and caudally. PMID:22096578

Integration trumps selection in object recognition.

PubMed

Saarela, Toni P; Landy, Michael S

2015-03-30

Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several "cues" (color, luminance, texture, etc.), and humans can integrate sensory cues to improve detection and recognition [1-3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue invariance by responding to a given shape independent of the visual cue defining it [5-8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10, 11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11, 12], imaging [13-16], and single-cell and neural population recordings [17, 18]. Besides single features, attention can select whole objects [19-21]. Objects are among the suggested "units" of attention because attention to a single feature of an object causes the selection of all of its features [19-21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. Copyright © 2015 Elsevier Ltd. All rights reserved.

Integration trumps selection in object recognition

PubMed Central

Saarela, Toni P.; Landy, Michael S.

2015-01-01

Summary Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several “cues” (color, luminance, texture etc.), and humans can integrate sensory cues to improve detection and recognition [1–3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue-invariance by responding to a given shape independent of the visual cue defining it [5–8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10,11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11,12], imaging [13–16], and single-cell and neural population recordings [17,18]. Besides single features, attention can select whole objects [19–21]. Objects are among the suggested “units” of attention because attention to a single feature of an object causes the selection of all of its features [19–21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near-optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. PMID:25802154

Uninformative Prior Multiple Target Tracking Using Evidential Particle Filters

NASA Astrophysics Data System (ADS)

Worthy, J. L., III; Holzinger, M. J.

Space situational awareness requires the ability to initialize state estimation from short measurements and the reliable association of observations to support the characterization of the space environment. The electro-optical systems used to observe space objects cannot fully characterize the state of an object given a short, unobservable sequence of measurements. Further, it is difficult to associate these short-arc measurements if many such measurements are generated through the observation of a cluster of satellites, debris from a satellite break-up, or from spurious detections of an object. An optimization based, probabilistic short-arc observation association approach coupled with a Dempster-Shafer based evidential particle filter in a multiple target tracking framework is developed and proposed to address these problems. The optimization based approach is shown in literature to be computationally efficient and can produce probabilities of association, state estimates, and covariances while accounting for systemic errors. Rigorous application of Dempster-Shafer theory is shown to be effective at enabling ignorance to be properly accounted for in estimation by augmenting probability with belief and plausibility. The proposed multiple hypothesis framework will use a non-exclusive hypothesis formulation of Dempster-Shafer theory to assign belief mass to candidate association pairs and generate tracks based on the belief to plausibility ratio. The proposed algorithm is demonstrated using simulated observations of a GEO satellite breakup scenario.

Assessment of Gamma-Ray Spectra Analysis Method Utilizing the Fireworks Algorithm for various Error Measures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alamaniotis, Miltiadis; Tsoukalas, Lefteri H.

2018-01-01

Significant role in enhancing nuclear nonproliferation plays the analysis of obtained data and the inference of the presence or not of special nuclear materials in them. Among various types of measurements, gamma-ray spectra is the widest used type of data utilized for analysis in nonproliferation. In this chapter, a method that employs the fireworks algorithm (FWA) for analyzing gamma-ray spectra aiming at detecting gamma signatures is presented. In particular FWA is utilized to fit a set of known signatures to a measured spectrum by optimizing an objective function, with non-zero coefficients expressing the detected signatures. FWA is tested on amore » set of experimentally obtained measurements and various objective functions -MSE, RMSE, Theil-2, MAE, MAPE, MAP- with results exhibiting its potential in providing high accuracy and high precision of detected signatures. Furthermore, FWA is benchmarked against genetic algorithms, and multiple linear regression with results exhibiting its superiority over the rest tested algorithms with respect to precision for MAE, MAPE and MAP measures.« less

Affective brain-computer music interfacing

NASA Astrophysics Data System (ADS)

Daly, Ian; Williams, Duncan; Kirke, Alexis; Weaver, James; Malik, Asad; Hwang, Faustina; Miranda, Eduardo; Nasuto, Slawomir J.

2016-08-01

Objective. We aim to develop and evaluate an affective brain-computer music interface (aBCMI) for modulating the affective states of its users. Approach. An aBCMI is constructed to detect a user's current affective state and attempt to modulate it in order to achieve specific objectives (for example, making the user calmer or happier) by playing music which is generated according to a specific affective target by an algorithmic music composition system and a case-based reasoning system. The system is trained and tested in a longitudinal study on a population of eight healthy participants, with each participant returning for multiple sessions. Main results. The final online aBCMI is able to detect its users current affective states with classification accuracies of up to 65% (3 class, p\\lt 0.01) and modulate its user's affective states significantly above chance level (p\\lt 0.05). Significance. Our system represents one of the first demonstrations of an online aBCMI that is able to accurately detect and respond to user's affective states. Possible applications include use in music therapy and entertainment.

Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery

PubMed Central

Ma, Yalong; Wu, Xinkai; Yu, Guizhen; Xu, Yongzheng; Wang, Yunpeng

2016-01-01

Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness. PMID:27023564

Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery.

PubMed

Ma, Yalong; Wu, Xinkai; Yu, Guizhen; Xu, Yongzheng; Wang, Yunpeng

2016-03-26

Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness.

Multispectra CWT-based algorithm (MCWT) in mass spectra for peak extraction.

PubMed

Hsueh, Huey-Miin; Kuo, Hsun-Chih; Tsai, Chen-An

2008-01-01

An important objective in mass spectrometry (MS) is to identify a set of biomarkers that can be used to potentially distinguish patients between distinct treatments (or conditions) from tens or hundreds of spectra. A common two-step approach involving peak extraction and quantification is employed to identify the features of scientific interest. The selected features are then used for further investigation to understand underlying biological mechanism of individual protein or for development of genomic biomarkers to early diagnosis. However, the use of inadequate or ineffective peak detection and peak alignment algorithms in peak extraction step may lead to a high rate of false positives. Also, it is crucial to reduce the false positive rate in detecting biomarkers from ten or hundreds of spectra. Here a new procedure is introduced for feature extraction in mass spectrometry data that extends the continuous wavelet transform-based (CWT-based) algorithm to multiple spectra. The proposed multispectra CWT-based algorithm (MCWT) not only can perform peak detection for multiple spectra but also carry out peak alignment at the same time. The author' MCWT algorithm constructs a reference, which integrates information of multiple raw spectra, for feature extraction. The algorithm is applied to a SELDI-TOF mass spectra data set provided by CAMDA 2006 with known polypeptide m/z positions. This new approach is easy to implement and it outperforms the existing peak extraction method from the Bioconductor PROcess package.

Assessment of platelet function in healthy cats in response to commonly prescribed antiplatelet drugs using three point-of-care platelet function tests.

PubMed

Ho, Kimberly K; Abrams-Ogg, Anthony Cg; Wood, R Darren; O'Sullivan, M Lynne; Kirby, Gordon M; Blois, Shauna L

2017-06-01

Objectives The objective was to determine if decreased platelet function could be detected after treatment with aspirin and/or clopidogrel in healthy cats using three point-of-care platelet function tests that evaluate platelet function by different methods: Multiplate (by impedance), Platelet Function Analyzer 100 (by mechanical aperture closure) and Plateletworks (by platelet counting). Methods Thirty-six healthy cats were randomly assigned to receive one of three oral treatments over an 8 day period: (1) aspirin 5 mg q72h; (2) aspirin 20.25 mg q72h; or (3) clopidogrel 18.75 mg q24h. Cats treated with 5 and 20.25 mg aspirin also received clopidogrel on days 4-8. Platelet aggregation in response to adenosine diphosphate and collagen ± arachidonic acid was assessed on days 1 (baseline), 4 and 8. Aspirin and clopidogrel metabolites were measured by high-performance liquid chromatography. Platelet function in response to treatment was analyzed by ANCOVA, linear regression and Spearman correlation. Results The only solitary aspirin effect was detected using Plateletworks with collagen in cats treated with 20.25 mg. The only effect detected by Multiplate was using arachidonic acid in cats treated with both aspirin 20.25 mg and clopidogrel. All clopidogrel treatment effects were detected by Platelet Function Analyzer 100, Plateletworks (adenosine diphosphate) and Plateletworks (collagen). Drug metabolites were present in all cats, but concentrations were minimally correlated to platelet function test results. Conclusions and relevance Platelet Function Analyzer 100 and Plateletworks using adenosine diphosphate ± collagen agonists may be used to detect decreased platelet function in response to clopidogrel treatment. Either aspirin is not as effective an antiplatelet drug as clopidogrel, or the tests used were not optimal to measure aspirin effect. Cats with heart disease are commonly prescribed antiplatelet drugs to decrease the risk of aortic thromboembolism. Platelet Function Analyzer 100 and Plateletworks may be useful for confirming clopidogrel treatment in these cats.

JPRS report: Science and technology. Central Eurasia

NASA Astrophysics Data System (ADS)

1994-05-01

Translated articles cover the following topics: optimal systems to detect and classify moving objects; multiple identification of optical readings in multisensor information and measurement system; method of first integrals in synthesis of optimal control; study of the development of turbulence in the region of a break above a triangular wing; electroerosion machining in aviation engine construction; and cumulation of a flat shock wave in a tube by a thin parietal gas layer of lower density.

Variety of geologic silhouette shapes distinguishable by multiple rotations method of quantitative shape analysis text

DOE Office of Scientific and Technical Information (OSTI.GOV)

Collins, D.G.; Parks, J.M.

1984-04-01

Silhouette shapes are two-dimensional projections of three-dimensional objects such as sand grains, gravel, and fossils. Within-the-margin markings such as chamber boundaries, sutures, or ribs are ignored. Comparisons between populations of objects from similar and differential origins (i.e., environments, species or genera, growth series, etc) is aided by quantifying the shapes. The Multiple Rotations Method (MRM) uses a variation of ''eigenshapes'', which is capable of distinguishing most of the subtle variations that the ''trained eye'' can detect. With a video-digitizer and microcomputer, MRM is fast, more accurate, and more objective than the human eye. The resulting shape descriptors comprise 5 ormore » 6 numbers per object that can be stored and retrieved to compare with similar descriptions of other objects. The original-shape outlines can be reconstituted sufficiently for gross recognition from these few numerical descriptors. Thus, a semi-automated data-retrieval system becomes feasible, with silhouette-shape descriptions as one of several recognition criteria. MRM consists of four ''rotations'': rotation about a center to a comparable orientation; a principal-components rotation to reduce the many original shape descriptors to a few; a VARIMAX orthogonal-factor rotation to achieve simple structure; and a rotation to achieve factor scores on individual objects. A variety of subtly different shapes includes sand grains from several locations, ages, and environments, and fossils of several types. This variety illustrates the feasibility of quantitative comparisons by MRM.« less

Abnormal center-periphery gradient in spatial attention in simultanagnosia.

PubMed

Balslev, Daniela; Odoj, Bartholomaeus; Rennig, Johannes; Karnath, Hans-Otto

2014-12-01

Patients suffering from simultanagnosia cannot perceive more than one object at a time. The underlying mechanism is incompletely understood. One hypothesis is that simultanagnosia reflects "tunnel vision," a constricted attention window around gaze, which precludes the grouping of individual objects. Although this idea has a long history in neuropsychology, the question whether the patients indeed have an abnormal attention gradient around the gaze has so far not been addressed. Here we tested this hypothesis in two simultanagnosia patients with bilateral parieto-occipital lesions and two control groups, with and without brain damage. We assessed the participants' ability to discriminate letters presented briefly at fixation with and without a peripheral distractor or in the visual periphery, with or without a foveal distractor. A constricted span of attention around gaze would predict an increased susceptibility to foveated versus peripheral distractors. Contrary to this prediction and unlike both control groups, the patients' ability to discriminate the target decreased more in the presence of peripheral compared with foveated distractors. Thus, the attentional spotlight in simultanagnosia does not fall on foveated objects as previously assumed, but rather abnormally highlights the periphery. Furthermore, we found the same center-periphery gradient in the patients' ability to recognize multiple objects. They detected multiple, but not single objects more accurately in the periphery than at fixation. These results suggest that an abnormal allocation of attention around the gaze can disrupt the grouping of individual objects into an integrated visual scene.

Multiple object tracking with non-unique data-to-object association via generalized hypothesis testing. [tracking several aircraft near each other or ships at sea

NASA Technical Reports Server (NTRS)

Porter, D. W.; Lefler, R. M.

1979-01-01

A generalized hypothesis testing approach is applied to the problem of tracking several objects where several different associations of data with objects are possible. Such problems occur, for instance, when attempting to distinctly track several aircraft maneuvering near each other or when tracking ships at sea. Conceptually, the problem is solved by first, associating data with objects in a statistically reasonable fashion and then, tracking with a bank of Kalman filters. The objects are assumed to have motion characterized by a fixed but unknown deterministic portion plus a random process portion modeled by a shaping filter. For example, the object might be assumed to have a mean straight line path about which it maneuvers in a random manner. Several hypothesized associations of data with objects are possible because of ambiguity as to which object the data comes from, false alarm/detection errors, and possible uncertainty in the number of objects being tracked. The statistical likelihood function is computed for each possible hypothesized association of data with objects. Then the generalized likelihood is computed by maximizing the likelihood over parameters that define the deterministic motion of the object.

Point pattern match-based change detection in a constellation of previously detected objects

DOEpatents

Paglieroni, David W.

2016-06-07

A method and system is provided that applies attribute- and topology-based change detection to objects that were detected on previous scans of a medium. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, detection strength, size, elongation, orientation, etc. The locations define a three-dimensional network topology forming a constellation of previously detected objects. The change detection system stores attributes of the previously detected objects in a constellation database. The change detection system detects changes by comparing the attributes and topological consistency of newly detected objects encountered during a new scan of the medium to previously detected objects in the constellation database. The change detection system may receive the attributes of the newly detected objects as the objects are detected by an object detection system in real time.

Multiple Hypothesis Tracking (MHT) for Space Surveillance: Results and Simulation Studies

NASA Astrophysics Data System (ADS)

Singh, N.; Poore, A.; Sheaff, C.; Aristoff, J.; Jah, M.

2013-09-01

With the anticipated installation of more accurate sensors and the increased probability of future collisions between space objects, the potential number of observable space objects is likely to increase by an order of magnitude within the next decade, thereby placing an ever-increasing burden on current operational systems. Moreover, the need to track closely-spaced objects due, for example, to breakups as illustrated by the recent Chinese ASAT test or the Iridium-Kosmos collision, requires new, robust, and autonomous methods for space surveillance to enable the development and maintenance of the present and future space catalog and to support the overall space surveillance mission. The problem of correctly associating a stream of uncorrelated tracks (UCTs) and uncorrelated optical observations (UCOs) into common objects is critical to mitigating the number of UCTs and is a prerequisite to subsequent space catalog maintenance. Presently, such association operations are mainly performed using non-statistical simple fixed-gate association logic. In this paper, we report on the salient features and the performance of a newly-developed statistically-robust system-level multiple hypothesis tracking (MHT) system for advanced space surveillance. The multiple-frame assignment (MFA) formulation of MHT, together with supporting astrodynamics algorithms, provides a new joint capability for space catalog maintenance, UCT/UCO resolution, and initial orbit determination. The MFA-MHT framework incorporates multiple hypotheses for report to system track data association and uses a multi-arc construction to accommodate recently developed algorithms for multiple hypothesis filtering (e.g., AEGIS, CAR-MHF, UMAP, and MMAE). This MHT framework allows us to evaluate the benefits of many different algorithms ranging from single- and multiple-frame data association to filtering and uncertainty quantification. In this paper, it will be shown that the MHT system can provide superior tracking performance compared to existing methods at a lower computational cost, especially for closely-spaced objects, in realistic multi-sensor multi-object tracking scenarios over multiple regimes of space. Specifically, we demonstrate that the prototype MHT system can accurately and efficiently process tens of thousands of UCTs and angles-only UCOs emanating from thousands of objects in LEO, GEO, MEO and HELO, many of which are closely-spaced, in real-time on a single laptop computer, thereby making it well-suited for large-scale breakup and tracking scenarios. This is possible in part because complexity reduction techniques are used to control the runtime of MHT without sacrificing accuracy. We assess the performance of MHT in relation to other tracking methods in multi-target, multi-sensor scenarios ranging from easy to difficult (i.e., widely-spaced objects to closely-spaced objects), using realistic physics and probabilities of detection less than one. In LEO, it is shown that the MHT system is able to address the challenges of processing breakups by analyzing multiple frames of data simultaneously in order to improve association decisions, reduce cross-tagging, and reduce unassociated UCTs. As a result, the multi-frame MHT system can establish orbits up to ten times faster than single-frame methods. Finally, it is shown that in GEO, MEO and HELO, the MHT system is able to address the challenges of processing angles-only optical observations by providing a unified multi-frame framework.

Electrophysiological evidence for effects of color knowledge in object recognition.

PubMed

Lu, Aitao; Xu, Guiping; Jin, Hua; Mo, Lei; Zhang, Jijia; Zhang, John X

2010-01-29

Knowledge about the typical colors associated with familiar everyday objects (i.e., strawberries are red) is well-known to be represented in the conceptual semantic system. Evidence that such knowledge may also play a role in early perceptual processes for object recognition is scant. In the present ERP study, participants viewed a list of object pictures and detected infrequent stimulus repetitions. Results show that shortly after stimulus onset, ERP components indexing early perceptual processes, including N1, P2, and N2, differentiated between objects in their appropriate or congruent color from these objects in an inappropriate or incongruent color. Such congruence effect also occurred in N3 associated with semantic processing of pictures but not in N4 for domain-general semantic processing. Our results demonstrate a clear effect of color knowledge in early object recognition stages and support the following proposal-color as a surface property is stored in a multiple-memory system where pre-semantic perceptual and semantic conceptual representations interact during object recognition. (c) 2009 Elsevier Ireland Ltd. All rights reserved.

Cloud Detection by Fusing Multi-Scale Convolutional Features

NASA Astrophysics Data System (ADS)

Li, Zhiwei; Shen, Huanfeng; Wei, Yancong; Cheng, Qing; Yuan, Qiangqiang

2018-04-01

Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.

7 Tesla Magnetic Resonance Imaging to Detect Cortical Pathology in Multiple Sclerosis

PubMed Central

van Gelderen, Peter; Merkle, Hellmuth; Chen, Christina; Lassmann, Hans; Duyn, Jeff H.; Bagnato, Francesca

2014-01-01

Background Neocortical lesions (NLs) are an important pathological component of multiple sclerosis (MS), but their visualization by magnetic resonance imaging (MRI) remains challenging. Objectives We aimed at assessing the sensitivity of multi echo gradient echo (ME-GRE) T2 *-weighted MRI at 7.0 Tesla in depicting NLs compared to myelin and iron staining. Methods Samples from two MS patients were imaged post mortem using a whole body 7T MRI scanner with a 24-channel receive-only array. Isotropic 200 micron resolution images with varying T2 * weighting were reconstructed from the ME-GRE data and converted into R2 * maps. Immunohistochemical staining for myelin (proteolipid protein, PLP) and diaminobenzidine-enhanced Turnbull blue staining for iron were performed. Results Prospective and retrospective sensitivities of MRI for the detection of NLs were 48% and 67% respectively. We observed MRI maps detecting only a small portion of 20 subpial NLs extending over large cortical areas on PLP stainings. No MRI signal changes suggestive of iron accumulation in NLs were observed. Conversely, R2 * maps indicated iron loss in NLs, which was confirmed by histological quantification. Conclusions High-resolution post mortem imaging using R2 * and magnitude maps permits detection of focal NLs. However, disclosing extensive subpial demyelination with MRI remains challenging. PMID:25303286

[Preoperative assessment of renal vascular anatomy for donor nephrectomy: Is CT superior to MRI?].

PubMed

Arvin-Berod, A; Bricault, I; Terrier, N; Skowron, O; Cadi, P; Boillot, B; Thuillier, C; Cluze, C; Descotes, J-L; Rambeaud, J-J; Long, J-A

2011-01-01

computed tomography angiography (CTA) and magnetic resonance angiography (MRA) are both used in the preoperative assessment of vascular anatomy before donor nephrectomy. Our objective was to determine retrospectively and to compare the sensitivity of CTA and MRA imaging in preoperative renal vascularisation in living kidney donors. between 1999 and 2007, 42 kidney donors were assessed in our center: 27 by MRA, 10 by CTA, and five by both techniques. Images were interpreted using multiplanar reconstructions. Results were compared retrospectively with peroperative findings; discordant cases were re-examined by an experienced radiologist. Numbers of vessels detected with imaging methods was compared with numbers actually found at the operating time. MRA showed 35/43 arteries (Se 81.4 %) and 33/34 veins (Se 97.1 %), and CTA showed 18/18 arteries (Se 100 %) and 15/16 veins (Se 93.8 %). The presence of multiple arteries was detected in only one third of cases (3/9) on MRI scans; this difference was statistically significant. The missed arteries were not detected on second examination of the MRI scans with the knowledge of peroperative findings. MRA is less sensitive than CTA for preoperative vascularisation imaging in living renal donors, especially in the detection of multiple renal arteries. 2010 Elsevier Masson SAS. All rights reserved.

Prognostic factors versus markers of response to treatment versus surrogate endpoints: Three different concepts.

PubMed

Sormani, Maria Pia

2017-03-01

Multiple sclerosis is a highly heterogeneous disease; the quantitative assessment of disease progression is problematic for many reasons, including the lack of objective methods to measure disability and the long follow-up times needed to detect relevant and stable changes. For these reasons, the importance of prognostic markers, markers of response to treatments and of surrogate endpoints, is crucial in multiple sclerosis research. Aim of this report is to clarify some basic definitions and methodological issues about baseline factors to be considered prognostic markers or markers of response to treatment; to define the dynamic role that variables must have to be considered surrogate markers in relation to specific treatments.

Location perception: the X-Files parable.

PubMed

Prinzmetal, William

2005-01-01

Three aspects of visual object location were investigated: (1) how the visual system integrates information for locating objects, (2) how attention operates to affect location perception, and (3) how the visual system deals with locating an object when multiple objects are present. The theories were described in terms of a parable (the X-Files parable). Then, computer simulations were developed. Finally, predictions derived from the simulations were tested. In the scenario described in the parable, we ask how a system of detectors might locate an alien spaceship, how attention might be implemented in such a spaceship detection system, and how the presence of one spaceship might influence the location perception of another alien spaceship. Experiment 1 demonstrated that location information is integrated with a spatial average rule. In Experiment 2, this rule was applied to a more-samples theory of attention. Experiment 3 demonstrated how the integration rule could account for various visual illusions.

Sensor modeling and demonstration of a multi-object spectrometer for performance-driven sensing

NASA Astrophysics Data System (ADS)

Kerekes, John P.; Presnar, Michael D.; Fourspring, Kenneth D.; Ninkov, Zoran; Pogorzala, David R.; Raisanen, Alan D.; Rice, Andrew C.; Vasquez, Juan R.; Patel, Jeffrey P.; MacIntyre, Robert T.; Brown, Scott D.

2009-05-01

A novel multi-object spectrometer (MOS) is being explored for use as an adaptive performance-driven sensor that tracks moving targets. Developed originally for astronomical applications, the instrument utilizes an array of micromirrors to reflect light to a panchromatic imaging array. When an object of interest is detected the individual micromirrors imaging the object are tilted to reflect the light to a spectrometer to collect a full spectrum. This paper will present example sensor performance from empirical data collected in laboratory experiments, as well as our approach in designing optical and radiometric models of the MOS channels and the micromirror array. Simulation of moving vehicles in a highfidelity, hyperspectral scene is used to generate a dynamic video input for the adaptive sensor. Performance-driven algorithms for feature-aided target tracking and modality selection exploit multiple electromagnetic observables to track moving vehicle targets.

Joint Target Detection and Tracking Filter for Chilbolton Advanced Meteorological Radar Data Processing

NASA Astrophysics Data System (ADS)

Pak, A.; Correa, J.; Adams, M.; Clark, D.; Delande, E.; Houssineau, J.; Franco, J.; Frueh, C.

2016-09-01

Recently, the growing number of inactive Resident Space Objects (RSOs), or space debris, has provoked increased interest in the field of Space Situational Awareness (SSA) and various investigations of new methods for orbital object tracking. In comparison with conventional tracking scenarios, state estimation of an orbiting object entails additional challenges, such as orbit determination and orbital state and covariance propagation in the presence of highly nonlinear system dynamics. The sensors which are available for detecting and tracking space debris are prone to multiple clutter measurements. Added to this problem, is the fact that it is unknown whether or not a space debris type target is present within such sensor measurements. Under these circumstances, traditional single-target filtering solutions such as Kalman Filters fail to produce useful trajectory estimates. The recent Random Finite Set (RFS) based Finite Set Statistical (FISST) framework has yielded filters which are more appropriate for such situations. The RFS based Joint Target Detection and Tracking (JoTT) filter, also known as the Bernoulli filter, is a single target, multiple measurements filter capable of dealing with cluttered and time-varying backgrounds as well as modeling target appearance and disappearance in the scene. Therefore, this paper presents the application of the Gaussian mixture-based JoTT filter for processing measurements from Chilbolton Advanced Meteorological Radar (CAMRa) which contain both defunct and operational satellites. The CAMRa is a fully-steerable radar located in southern England, which was recently modified to be used as a tracking asset in the European Space Agency SSA program. The experiments conducted show promising results regarding the capability of such filters in processing cluttered radar data. The work carried out in this paper was funded by the USAF Grant No. FA9550-15-1-0069, Chilean Conicyt - Fondecyt grant number 1150930, EU Erasmus Mundus MSc Scholarship, Defense Science and Technology Laboratory (DSTL), U. K., and the Chilean Conicyt, Fondecyt project grant number 1150930.

A multisensor system for detection and characterization of UXO(MM-0437) - Demonstration Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gasperikova, Erika; Smith, J.T.; Morrison, H.F.

2006-06-01

The Berkeley UXO discriminator (BUD) (Figure 1) is a portable Active Electromagnetic (AEM) system for UXO detection and characterization that quickly determines the location, size, and symmetry properties of a suspected UXO. The BUD comprises of three orthogonal transmitters that 'illuminate' a target with fields in three independent directions in order to stimulate the three polarization modes that, in general, characterize the target EM response. In addition, the BUD uses eight pairs of differenced receivers for response recording. Eight receiver coils are placed horizontally along the two diagonals of the upper and lower planes of the two horizontal transmitter loops.more » These receiver coil pairs are located on symmetry lines through the center of the system and each pair sees identical fields during the on-time of the pulse in all of the transmitter coils. They are wired in opposition to produce zero output during the on-time of the pulses in three orthogonal transmitters. Moreover, this configuration dramatically reduces noise in the measurements by canceling the background electromagnetic fields (these fields are uniform over the scale of the receiver array and are consequently nulled by the differencing operation), and by canceling the noise contributed by the tilt of the receivers in the Earth's magnetic field, and greatly enhances receivers sensitivity to the gradients of the target response. The BUD performs target characterization from a single position of the sensor platform above a target. BUD was designed to detect and characterize UXO in the 20 mm to 155 mm size range for depths between 0 and 1 m. The relationship between the object size and the depth at which it can be detected is illustrated in Figure 2. This curve was calculated for BUD assuming that the receiver plane is 20 cm above the ground. Figure 2 shows that, for example, BUD can detect and characterize an object with 10 cm diameter down to the depth of 90 cm with depth uncertainty of 10%. Any objects buried at the depth more than 1 m have a low probability of detection. With existing algorithms in the system computer it is not possible to recover the principal polarizabilities of large objects close to the system. Detection of large shallow objects is assured, but at present real time discrimination for shallow objects is not. Post processing of the field data is required for shape discrimination of large shallow targets. Next generation of BUD software will not have this limitation. Successful application of the inversion algorithm that solves for the target parameters is contingent upon resolution of this limitation. At the moment, interpretation software is developed for a single object only. In case of multiple objects the software indicates the presence of a cluster of objects but is unable to provide characteristics of each individual object.« less

Evaluation of EIT system performance.

PubMed

Yasin, Mamatjan; Böhm, Stephan; Gaggero, Pascal O; Adler, Andy

2011-07-01

An electrical impedance tomography (EIT) system images internal conductivity from surface electrical stimulation and measurement. Such systems necessarily comprise multiple design choices from cables and hardware design to calibration and image reconstruction. In order to compare EIT systems and study the consequences of changes in system performance, this paper describes a systematic approach to evaluate the performance of the EIT systems. The system to be tested is connected to a saline phantom in which calibrated contrasting test objects are systematically positioned using a position controller. A set of evaluation parameters are proposed which characterize (i) data and image noise, (ii) data accuracy, (iii) detectability of single contrasts and distinguishability of multiple contrasts, and (iv) accuracy of reconstructed image (amplitude, resolution, position and ringing). Using this approach, we evaluate three different EIT systems and illustrate the use of these tools to evaluate and compare performance. In order to facilitate the use of this approach, all details of the phantom, test objects and position controller design are made publicly available including the source code of the evaluation and reporting software.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Agalgaonkar, Yashodhan P.; Hammerstrom, Donald J.

The Pacific Northwest Smart Grid Demonstration (PNWSGD) was a smart grid technology performance evaluation project that included multiple U.S. states and cooperation from multiple electric utilities in the northwest region. One of the local objectives for the project was to achieve improved distribution system reliability. Toward this end, some PNWSGD utilities automated their distribution systems, including the application of fault detection, isolation, and restoration and advanced metering infrastructure. In light of this investment, a major challenge was to establish a correlation between implementation of these smart grid technologies and actual improvements of distribution system reliability. This paper proposes using Welch’smore » t-test to objectively determine and quantify whether distribution system reliability is improving over time. The proposed methodology is generic, and it can be implemented by any utility after calculation of the standard reliability indices. The effectiveness of the proposed hypothesis testing approach is demonstrated through comprehensive practical results. It is believed that wider adoption of the proposed approach can help utilities to evaluate a realistic long-term performance of smart grid technologies.« less

Radiation portal monitor system and method

DOEpatents

Morris, Christopher [Los Alamos, NM; Borozdin, Konstantin N [Los Alamos, NM; Green, J Andrew [Los Alamos, NM; Hogan, Gary E [Los Alamos, NM; Makela, Mark F [Los Alamos, NM; Priedhorsky, William C [Los Alamos, NM; Saunders, Alexander [Los Alamos, NM; Schultz, Larry J [Los Alamos, NM; Sossong, Michael J [Los Alamos, NM

2009-12-15

A portal monitoring system has a cosmic ray charged particle tracker with a plurality of drift cells. The drift cells, which can be for example aluminum drift tubes, can be arranged at least above and below a volume to be scanned to thereby track incoming and outgoing charged particles, such as cosmic ray muons, whilst also detecting gamma rays. The system can selectively detect devices or materials, such as iron, lead, gold and/or tungsten, occupying the volume from multiple scattering of the charged particles passing through the volume and can also detect any radioactive sources occupying the volume from gamma rays emitted therefrom. If necessary, the drift tubes can be sealed to eliminate the need for a gas handling system. The system can be employed to inspect occupied vehicles at border crossings for nuclear threat objects.

Lesion detection and quantification performance of the Tachyon-I time-of-flight PET scanner: phantom and human studies.

PubMed

Zhang, Xuezhu; Peng, Qiyu; Zhou, Jian; Huber, Jennifer S; Moses, William W; Qi, Jinyi

2018-03-16

The first generation Tachyon PET (Tachyon-I) is a demonstration single-ring PET scanner that reaches a coincidence timing resolution of 314 ps using LSO scintillator crystals coupled to conventional photomultiplier tubes. The objective of this study was to quantify the improvement in both lesion detection and quantification performance resulting from the improved time-of-flight (TOF) capability of the Tachyon-I scanner. We developed a quantitative TOF image reconstruction method for the Tachyon-I and evaluated its TOF gain for lesion detection and quantification. Scans of either a standard NEMA torso phantom or healthy volunteers were used as the normal background data. Separately scanned point source and sphere data were superimposed onto the phantom or human data after accounting for the object attenuation. We used the bootstrap method to generate multiple independent noisy datasets with and without a lesion present. The signal-to-noise ratio (SNR) of a channelized hotelling observer (CHO) was calculated for each lesion size and location combination to evaluate the lesion detection performance. The bias versus standard deviation trade-off of each lesion uptake was also calculated to evaluate the quantification performance. The resulting CHO-SNR measurements showed improved performance in lesion detection with better timing resolution. The detection performance was also dependent on the lesion size and location, in addition to the background object size and shape. The results of bias versus noise trade-off showed that the noise (standard deviation) reduction ratio was about 1.1-1.3 over the TOF 500 ps and 1.5-1.9 over the non-TOF modes, similar to the SNR gains for lesion detection. In conclusion, this Tachyon-I PET study demonstrated the benefit of improved time-of-flight capability on lesion detection and ROI quantification for both phantom and human subjects.

Lesion detection and quantification performance of the Tachyon-I time-of-flight PET scanner: phantom and human studies

NASA Astrophysics Data System (ADS)

Zhang, Xuezhu; Peng, Qiyu; Zhou, Jian; Huber, Jennifer S.; Moses, William W.; Qi, Jinyi

2018-03-01

The first generation Tachyon PET (Tachyon-I) is a demonstration single-ring PET scanner that reaches a coincidence timing resolution of 314 ps using LSO scintillator crystals coupled to conventional photomultiplier tubes. The objective of this study was to quantify the improvement in both lesion detection and quantification performance resulting from the improved time-of-flight (TOF) capability of the Tachyon-I scanner. We developed a quantitative TOF image reconstruction method for the Tachyon-I and evaluated its TOF gain for lesion detection and quantification. Scans of either a standard NEMA torso phantom or healthy volunteers were used as the normal background data. Separately scanned point source and sphere data were superimposed onto the phantom or human data after accounting for the object attenuation. We used the bootstrap method to generate multiple independent noisy datasets with and without a lesion present. The signal-to-noise ratio (SNR) of a channelized hotelling observer (CHO) was calculated for each lesion size and location combination to evaluate the lesion detection performance. The bias versus standard deviation trade-off of each lesion uptake was also calculated to evaluate the quantification performance. The resulting CHO-SNR measurements showed improved performance in lesion detection with better timing resolution. The detection performance was also dependent on the lesion size and location, in addition to the background object size and shape. The results of bias versus noise trade-off showed that the noise (standard deviation) reduction ratio was about 1.1–1.3 over the TOF 500 ps and 1.5–1.9 over the non-TOF modes, similar to the SNR gains for lesion detection. In conclusion, this Tachyon-I PET study demonstrated the benefit of improved time-of-flight capability on lesion detection and ROI quantification for both phantom and human subjects.

Some See It, Some Don’t: Exploring the Relation between Inattentional Blindness and Personality Factors

PubMed Central

Kreitz, Carina; Schnuerch, Robert; Gibbons, Henning; Memmert, Daniel

2015-01-01

Human awareness is highly limited, which is vividly demonstrated by the phenomenon that unexpected objects go unnoticed when attention is focused elsewhere (inattentional blindness). Typically, some people fail to notice unexpected objects while others detect them instantaneously. Whether this pattern reflects stable individual differences is unclear to date. In particular, hardly anything is known about the influence of personality on the likelihood of inattentional blindness. To fill this empirical gap, we examined the role of multiple personality factors, namely the Big Five, BIS/BAS, absorption, achievement motivation, and schizotypy, in these failures of awareness. In a large-scale sample (N = 554), susceptibility to inattentional blindness was associated with a low level of openness to experience and marginally with a low level of achievement motivation. However, in a multiple regression analysis, only openness emerged as an independent, negative predictor. This suggests that the general tendency to be open to experience extends to the domain of perception. Our results complement earlier work on the possible link between inattentional blindness and personality by demonstrating, for the first time, that failures to consciously perceive unexpected objects reflect individual differences on a fundamental dimension of personality. PMID:26011567

Application of Visual Attention in Seismic Attribute Analysis

NASA Astrophysics Data System (ADS)

He, M.; Gu, H.; Wang, F.

2016-12-01

It has been proved that seismic attributes can be used to predict reservoir. The joint of multi-attribute and geological statistics, data mining, artificial intelligence, further promote the development of the seismic attribute analysis. However, the existing methods tend to have multiple solutions and insufficient generalization ability, which is mainly due to the complex relationship between seismic data and geological information, and undoubtedly own partly to the methods applied. Visual attention is a mechanism model of the human visual system which can concentrate on a few significant visual objects rapidly, even in a mixed scene. Actually, the model qualify good ability of target detection and recognition. In our study, the targets to be predicted are treated as visual objects, and an object representation based on well data is made in the attribute dimensions. Then in the same attribute space, the representation is served as a criterion to search the potential targets outside the wells. This method need not predict properties by building up a complicated relation between attributes and reservoir properties, but with reference to the standard determined before. So it has pretty good generalization ability, and the problem of multiple solutions can be weakened by defining the threshold of similarity.

New structures of power density spectra for four Kepler active galactic nuclei

NASA Astrophysics Data System (ADS)

Dobrotka, A.; Antonuccio-Delogu, V.; Bajčičáková, I.

2017-09-01

Many nearby active galactic nuclei display a significant short-term variability. In this work, we reanalyse photometric data of four active galactic nuclei observed by Kepler in order to study the flickering activity, with our main goal to search for multiple components in the power density spectra. We find that all four objects have similar characteristics, with two break frequencies at approximately log( f /Hz) = -5.2 and -4.7. We consider some physical phenomena whose characteristic time-scales are consistent with those observed, in particular mass accretion fluctuations in the inner geometrically thick disc (hot X-ray corona) and unstable relativistic Rayleigh-Taylor modes. The former is supported by detection of the same break frequencies in the Swift X-ray data of ZW229-15. We also discuss rms-flux relations, and we detect a possible typical linear trend at lower flux levels. Our findings support the hypothesis of a multiplicative character of variability, in agreement with the propagating accretion fluctuation model.

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

NASA Technical Reports Server (NTRS)

Solarna, David; Moser, Gabriele; Le Moigne-Stewart, Jacqueline; Serpico, Sebastiano B.

2017-01-01

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

Possible Synergistic Interactions Among Multiple HPV Genotypes in Women Suffering from Genital Neoplasia

PubMed

Hajia, Massoud; Sohrabi, Amir

2018-03-27

Objective: Persistence of HPV infection is the true cause of cervical disorders. It is reported that competition may exist among HPV genotypes for colonization. This survey was designed to establish the multiple HPV genotype status in our community and the probability of multiple HPV infections involvement. Methods: All multiple HPV infections were selected for investigation in women suffering from genital infections referred to private laboratories in Tehran, Iran. A total of 160 multi HPV positive specimens from cervical scraping were identified by the HPV genotyping methods, "INNO-LiPA and Geno Array". Result: In present study, HPV 6 (LR), 16 (HR), 53 (pHR), 31 (HR) and 11 (LR) were included in 48.8% of detected infections as the most five dominant genotypes. HPV 16 was detected at the highest rate with genotypes 53, 31 and 52, while HPV 53 appeared linked with HPV 16, 51 and 56 in concurrent infections. It appears that HPV 16 and 53 may have significant tendencies to associate with each other rather than with other genotypes. Analysis of the data revealed there may be some synergistic interactions with a few particular genotypes such as "HPV 53". Conclusion: Multiple HPV genotypes appear more likely to be linked with development of cervical abnormalities especially in patients with genital infections. Since, there are various patterns of dominant HPV genotypes in different regions of world, more investigations of this type should be performed for careHPV programs in individual countries. Creative Commons Attribution License

Target Coverage in Wireless Sensor Networks with Probabilistic Sensors

PubMed Central

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao

2016-01-01

Sensing coverage is a fundamental problem in wireless sensor networks (WSNs), which has attracted considerable attention. Conventional research on this topic focuses on the 0/1 coverage model, which is only a coarse approximation to the practical sensing model. In this paper, we study the target coverage problem, where the objective is to find the least number of sensor nodes in randomly-deployed WSNs based on the probabilistic sensing model. We analyze the joint detection probability of target with multiple sensors. Based on the theoretical analysis of the detection probability, we formulate the minimum ϵ-detection coverage problem. We prove that the minimum ϵ-detection coverage problem is NP-hard and present an approximation algorithm called the Probabilistic Sensor Coverage Algorithm (PSCA) with provable approximation ratios. To evaluate our design, we analyze the performance of PSCA theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm. PMID:27618902

TU-EF-204-11: Impact of Using Multi-Slice Training Sets On the Performance of a Channelized Hotelling Observer in a Low-Contrast Detection Task in CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Favazza, C; Yu, L; Leng, S

2015-06-15

Purpose: To investigate using multiple CT image slices from a single acquisition as independent training images for a channelized Hotelling observer (CHO) model to reduce the number of repeated scans for CHO-based CT image quality assessment. Methods: We applied a previously validated CHO model to detect low contrast disk objects formed from cross-sectional images of three epoxy-resin-based rods (diameters: 3, 5, and 9 mm; length: ∼5cm). The rods were submerged in a 35x 25 cm2 iodine-doped water filled phantom, yielding-15 HU object contrast. The phantom was scanned 100 times with and without the rods present. Scan and reconstruction parameters include:more » 5 mm slice thickness at 0.5 mm intervals, 120 kV, 480 Quality Reference mAs, and a 128-slice scanner. The CHO’s detectability index was evaluated as a function of factors related to incorporating multi-slice image data: object misalignment along the z-axis, inter-slice pixel correlation, and number of unique slice locations. In each case, the CHO training set was fixed to 100 images. Results: Artificially shifting the object’s center position by as much as 3 pixels in any direction relative to the Gabor channel filters had insignificant impact on object detectability. An inter-slice pixel correlation of >∼0.2 yielded positive bias in the model’s performance. Incorporating multi-slice image data yielded slight negative bias in detectability with increasing number of slices, likely due to physical variations in the objects. However, inclusion of image data from up to 5 slice locations yielded detectability indices within measurement error of the single slice value. Conclusion: For the investigated model and task, incorporating image data from 5 different slice locations of at least 5 mm intervals into the CHO model yielded detectability indices within measurement error of the single slice value. Consequently, this methodology would Result in a 5-fold reduction in number of image acquisitions. This project was supported by National Institutes of Health grants R01 EB017095 and U01 EB017185 from the National Institute of Biomedical Imaging and Bioengineering.« less

Compositional mining of multiple object API protocols through state abstraction.

PubMed

Dai, Ziying; Mao, Xiaoguang; Lei, Yan; Qi, Yuhua; Wang, Rui; Gu, Bin

2013-01-01

API protocols specify correct sequences of method invocations. Despite their usefulness, API protocols are often unavailable in practice because writing them is cumbersome and error prone. Multiple object API protocols are more expressive than single object API protocols. However, the huge number of objects of typical object-oriented programs poses a major challenge to the automatic mining of multiple object API protocols: besides maintaining scalability, it is important to capture various object interactions. Current approaches utilize various heuristics to focus on small sets of methods. In this paper, we present a general, scalable, multiple object API protocols mining approach that can capture all object interactions. Our approach uses abstract field values to label object states during the mining process. We first mine single object typestates as finite state automata whose transitions are annotated with states of interacting objects before and after the execution of the corresponding method and then construct multiple object API protocols by composing these annotated single object typestates. We implement our approach for Java and evaluate it through a series of experiments.

Compositional Mining of Multiple Object API Protocols through State Abstraction

PubMed Central

Mao, Xiaoguang; Qi, Yuhua; Wang, Rui; Gu, Bin

2013-01-01

API protocols specify correct sequences of method invocations. Despite their usefulness, API protocols are often unavailable in practice because writing them is cumbersome and error prone. Multiple object API protocols are more expressive than single object API protocols. However, the huge number of objects of typical object-oriented programs poses a major challenge to the automatic mining of multiple object API protocols: besides maintaining scalability, it is important to capture various object interactions. Current approaches utilize various heuristics to focus on small sets of methods. In this paper, we present a general, scalable, multiple object API protocols mining approach that can capture all object interactions. Our approach uses abstract field values to label object states during the mining process. We first mine single object typestates as finite state automata whose transitions are annotated with states of interacting objects before and after the execution of the corresponding method and then construct multiple object API protocols by composing these annotated single object typestates. We implement our approach for Java and evaluate it through a series of experiments. PMID:23844378

Neural dynamics of object-based multifocal visual spatial attention and priming: Object cueing, useful-field-of-view, and crowding

PubMed Central

Foley, Nicholas C.; Grossberg, Stephen; Mingolla, Ennio

2015-01-01

How are spatial and object attention coordinated to achieve rapid object learning and recognition during eye movement search? How do prefrontal priming and parietal spatial mechanisms interact to determine the reaction time costs of intra-object attention shifts, inter-object attention shifts, and shifts between visible objects and covertly cued locations? What factors underlie individual differences in the timing and frequency of such attentional shifts? How do transient and sustained spatial attentional mechanisms work and interact? How can volition, mediated via the basal ganglia, influence the span of spatial attention? A neural model is developed of how spatial attention in the where cortical stream coordinates view-invariant object category learning in the what cortical stream under free viewing conditions. The model simulates psychological data about the dynamics of covert attention priming and switching requiring multifocal attention without eye movements. The model predicts how “attentional shrouds” are formed when surface representations in cortical area V4 resonate with spatial attention in posterior parietal cortex (PPC) and prefrontal cortex (PFC), while shrouds compete among themselves for dominance. Winning shrouds support invariant object category learning, and active surface-shroud resonances support conscious surface perception and recognition. Attentive competition between multiple objects and cues simulates reaction-time data from the two-object cueing paradigm. The relative strength of sustained surface-driven and fast-transient motion-driven spatial attention controls individual differences in reaction time for invalid cues. Competition between surface-driven attentional shrouds controls individual differences in detection rate of peripheral targets in useful-field-of-view tasks. The model proposes how the strength of competition can be mediated, though learning or momentary changes in volition, by the basal ganglia. A new explanation of crowding shows how the cortical magnification factor, among other variables, can cause multiple object surfaces to share a single surface-shroud resonance, thereby preventing recognition of the individual objects. PMID:22425615

Neural dynamics of object-based multifocal visual spatial attention and priming: object cueing, useful-field-of-view, and crowding.

PubMed

Foley, Nicholas C; Grossberg, Stephen; Mingolla, Ennio

2012-08-01

How are spatial and object attention coordinated to achieve rapid object learning and recognition during eye movement search? How do prefrontal priming and parietal spatial mechanisms interact to determine the reaction time costs of intra-object attention shifts, inter-object attention shifts, and shifts between visible objects and covertly cued locations? What factors underlie individual differences in the timing and frequency of such attentional shifts? How do transient and sustained spatial attentional mechanisms work and interact? How can volition, mediated via the basal ganglia, influence the span of spatial attention? A neural model is developed of how spatial attention in the where cortical stream coordinates view-invariant object category learning in the what cortical stream under free viewing conditions. The model simulates psychological data about the dynamics of covert attention priming and switching requiring multifocal attention without eye movements. The model predicts how "attentional shrouds" are formed when surface representations in cortical area V4 resonate with spatial attention in posterior parietal cortex (PPC) and prefrontal cortex (PFC), while shrouds compete among themselves for dominance. Winning shrouds support invariant object category learning, and active surface-shroud resonances support conscious surface perception and recognition. Attentive competition between multiple objects and cues simulates reaction-time data from the two-object cueing paradigm. The relative strength of sustained surface-driven and fast-transient motion-driven spatial attention controls individual differences in reaction time for invalid cues. Competition between surface-driven attentional shrouds controls individual differences in detection rate of peripheral targets in useful-field-of-view tasks. The model proposes how the strength of competition can be mediated, though learning or momentary changes in volition, by the basal ganglia. A new explanation of crowding shows how the cortical magnification factor, among other variables, can cause multiple object surfaces to share a single surface-shroud resonance, thereby preventing recognition of the individual objects. Copyright © 2012 Elsevier Inc. All rights reserved.

Real-time optical multiple object recognition and tracking system and method

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin (Inventor); Liu, Hua Kuang (Inventor)

1987-01-01

The invention relates to an apparatus and associated methods for the optical recognition and tracking of multiple objects in real time. Multiple point spatial filters are employed that pre-define the objects to be recognized at run-time. The system takes the basic technology of a Vander Lugt filter and adds a hololens. The technique replaces time, space and cost-intensive digital techniques. In place of multiple objects, the system can also recognize multiple orientations of a single object. This later capability has potential for space applications where space and weight are at a premium.

Predicting hearing thresholds and occupational hearing loss with multiple-frequency auditory steady-state responses.

PubMed

Hsu, Ruey-Fen; Ho, Chi-Kung; Lu, Sheng-Nan; Chen, Shun-Sheng

2010-10-01

An objective investigation is needed to verify the existence and severity of hearing impairments resulting from work-related, noise-induced hearing loss in arbitration of medicolegal aspects. We investigated the accuracy of multiple-frequency auditory steady-state responses (Mf-ASSRs) between subjects with sensorineural hearing loss (SNHL) with and without occupational noise exposure. Cross-sectional study. Tertiary referral medical centre. Pure-tone audiometry and Mf-ASSRs were recorded in 88 subjects (34 patients had occupational noise-induced hearing loss [NIHL], 36 patients had SNHL without noise exposure, and 18 volunteers were normal controls). Inter- and intragroup comparisons were made. A predicting equation was derived using multiple linear regression analysis. ASSRs and pure-tone thresholds (PTTs) showed a strong correlation for all subjects (r = .77 ≈ .94). The relationship is demonstrated by the equationThe differences between the ASSR and PTT were significantly higher for the NIHL group than for the subjects with non-noise-induced SNHL (p < .001). Mf-ASSR is a promising tool for objectively evaluating hearing thresholds. Predictive value may be lower in subjects with occupational hearing loss. Regardless of carrier frequencies, the severity of hearing loss affects the steady-state response. Moreover, the ASSR may assist in detecting noise-induced injury of the auditory pathway. A multiple linear regression equation to accurately predict thresholds was shown that takes into consideration all effect factors.

Imaging, object detection, and change detection with a polarized multistatic GPR array

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beer, N. Reginald; Paglieroni, David W.

A polarized detection system performs imaging, object detection, and change detection factoring in the orientation of an object relative to the orientation of transceivers. The polarized detection system may operate on one of several modes of operation based on whether the imaging, object detection, or change detection is performed separately for each transceiver orientation. In combined change mode, the polarized detection system performs imaging, object detection, and change detection separately for each transceiver orientation, and then combines changes across polarizations. In combined object mode, the polarized detection system performs imaging and object detection separately for each transceiver orientation, and thenmore » combines objects across polarizations and performs change detection on the result. In combined image mode, the polarized detection system performs imaging separately for each transceiver orientation, and then combines images across polarizations and performs object detection followed by change detection on the result.« less

Attention to Attributes and Objects in Working Memory

PubMed Central

Cowan, Nelson; Blume, Christopher L.; Saults, J. Scott

2013-01-01

It has been debated on the basis of change-detection procedures whether visual working memory is limited by the number of objects, task-relevant attributes within those objects, or bindings between attributes. This debate, however, has been hampered by several limitations, including the use of conditions that vary between studies and the absence of appropriate mathematical models to estimate the number of items in working memory in different stimulus conditions. We re-examined working memory limits in two experiments with a wide array of conditions involving color and shape attributes, relying on a set of new models to fit various stimulus situations. In Experiment 2, a new procedure allowed identical retrieval conditions across different conditions of attention at encoding. The results show that multiple attributes compete for attention, but that retaining the binding between attributes is accomplished only by retaining the attributes themselves. We propose a theoretical account in which a fixed object capacity limit contains within it the possibility of the incomplete retention of object attributes, depending on the direction of attention. PMID:22905929

Detection and Purging of Specular Reflective and Transparent Object Influences in 3d Range Measurements

NASA Astrophysics Data System (ADS)

Koch, R.; May, S.; Nüchter, A.

2017-02-01

3D laser scanners are favoured sensors for mapping in mobile service robotics at indoor and outdoor applications, since they deliver precise measurements at a wide scanning range. The resulting maps are detailed since they have a high resolution. Based on these maps robots navigate through rough terrain, fulfil advanced manipulation, and inspection tasks. In case of specular reflective and transparent objects, e.g., mirrors, windows, shiny metals, the laser measurements get corrupted. Based on the type of object and the incident angle of the incoming laser beam there are three results possible: a measurement point on the object plane, a measurement behind the object plane, and a measurement of a reflected object. It is important to detect such situations to be able to handle these corrupted points. This paper describes why it is difficult to distinguish between specular reflective and transparent surfaces. It presents a 3DReflection- Pre-Filter Approach to identify specular reflective and transparent objects in point clouds of a multi-echo laser scanner. Furthermore, it filters point clouds from influences of such objects and extract the object properties for further investigations. Based on an Iterative-Closest-Point-algorithm reflective objects are identified. Object surfaces and points behind surfaces are masked according to their location. Finally, the processed point cloud is forwarded to a mapping module. Furthermore, the object surface corners and the type of the surface is broadcasted. Four experiments demonstrate the usability of the 3D-Reflection-Pre-Filter. The first experiment was made in a empty room containing a mirror, the second experiment was made in a stairway containing a glass door, the third experiment was made in a empty room containing two mirrors, the fourth experiment was made in an office room containing a mirror. This paper demonstrate that for single scans the detection of specular reflective and transparent objects in 3D is possible. It is more reliable in 3D as in 2D. Nevertheless, collect the data of multiple scans and post-filter them as soon as the object was bypassed should pursued. This is why future work concentrates on implementing a post-filter module. Besides, it is the aim to improve the discrimination between specular reflective and transparent objects.

Measuring the Yarkovsky effect with Las Cumbres Observatory

NASA Astrophysics Data System (ADS)

Greenstreet, Sarah; Farnocchia, Davide; Lister, Tim

2017-10-01

The Las Cumbres Observatory (LCO) provides an ideal platform for follow-up and characterization of Solar System objects (e.g. asteroids, Kuiper Belt Objects, comets, Near-Earth Objects) and ultimately for the discovery of new objects. We have used LCO's global network of nine 1-meter telescopes to measure the Yarkovsky effect on tens of asteroids through precise astrometric measurements using the Gaia-DR1 catalog, providing lower uncertainty with each detection. The target asteroids were picked through simulated observations each month to determine the objects for which new astrometry would yield the most improvement. The Gaia-DR1 release has greatly improved the quality of the astrometry obtained, making the detection of the Yarkovsky effect more likely and secure by greatly reducing systematic catalog zonal errors. With the release of DR2 next year and the availability of good reference star colors, we will be able to take other more subtle effects into account in the astrometric reduction. In addition, the availability of the Gaia catalog would allow re-measurement of past data with more accurate star catalogs. The amount of Yarkovsky acceleration depends on several physical properties, such as the asteroid spin state, size, mass, and thermal properties, to which detection of the effect can give important constraints. The effect is also important for understanding the transportation of asteroids and meteorites into near-Earth space from the main belt, producing the NEOs and for the formation and evolution of asteroid families. Determining and modeling the Yarkovsky effect can be critical for accurate prediction of asteroid trajectories and even for impact hazard assessment. The measurements made with the help of LCO have significantly increased the number of known asteroids with Yarkovsky detections. LCO is ideally suited to perform these observations due to its ability to monitor several targets over several days by employing dynamic scheduling, weather avoidance, and use of multiple sites around the globe.

Quadratic grating apodized photon sieves for simultaneous multiplane microscopy

NASA Astrophysics Data System (ADS)

Cheng, Yiguang; Zhu, Jiangping; He, Yu; Tang, Yan; Hu, Song; Zhao, Lixin

2017-10-01

We present a new type of imaging device, named quadratic grating apodized photon sieve (QGPS), used as the objective for simultaneous multiplane imaging in X-rays. The proposed QGPS is structured based on the combination of two concepts: photon sieves and quadratic gratings. Its design principles are also expounded in detail. Analysis of imaging properties of QGPS in terms of point-spread function shows that QGPS can image multiple layers within an object field onto a single image plane. Simulated and experimental results in visible light both demonstrate the feasibility of QGPS for simultaneous multiplane imaging, which is extremely promising to detect dynamic specimens by X-ray microscopy in the physical and life sciences.

Efficient and automatic image reduction framework for space debris detection based on GPU technology

NASA Astrophysics Data System (ADS)

Diprima, Francesco; Santoni, Fabio; Piergentili, Fabrizio; Fortunato, Vito; Abbattista, Cristoforo; Amoruso, Leonardo

2018-04-01

In the last years, the increasing number of space debris has triggered the need of a distributed monitoring system for the prevention of possible space collisions. Space surveillance based on ground telescope allows the monitoring of the traffic of the Resident Space Objects (RSOs) in the Earth orbit. This space debris surveillance has several applications such as orbit prediction and conjunction assessment. In this paper is proposed an optimized and performance-oriented pipeline for sources extraction intended to the automatic detection of space debris in optical data. The detection method is based on the morphological operations and Hough Transform for lines. Near real-time detection is obtained using General Purpose computing on Graphics Processing Units (GPGPU). The high degree of processing parallelism provided by GPGPU allows to split data analysis over thousands of threads in order to process big datasets with a limited computational time. The implementation has been tested on a large and heterogeneous images data set, containing both imaging satellites from different orbit ranges and multiple observation modes (i.e. sidereal and object tracking). These images were taken during an observation campaign performed from the EQUO (EQUatorial Observatory) observatory settled at the Broglio Space Center (BSC) in Kenya, which is part of the ASI-Sapienza Agreement.

The wire optical test: a thorough analytical study in and out of caustic surface, and advantages of a dynamical adaptation

NASA Astrophysics Data System (ADS)

Alejandro Juárez-Reyes, Salvador; Sosa-Sánchez, Citlalli Teresa; Silva-Ortigoza, Gilberto; de Jesús Cabrera-Rosas, Omar; Espíndola-Ramos, Ernesto; Ortega-Vidals, Paula

2018-03-01

Among the best known non-interferometric optical tests are the wire test, the Foucault test and Ronchi test with a low frequency grating. Since the wire test is the seed to understand the other ones, the aim of the present work is to do a thorough study of this test for a lens with symmetry of revolution and to do this study for any configuration of the object and detection planes where both planes could intersect: two, one or no branches of the caustic region (including the marginal and paraxial foci). To this end, we calculated the vectorial representation for the caustic region, and we found the analytical expression for the pattern; we report that the analytical pattern explicitly depends on the magnitude of a branch of the caustic. With the analytical pattern we computed a set of simulations of a dynamical adaptation of the optical wire test. From those simulations, we have done a thorough analysis of the topological structure of the pattern; so we explain how the multiple image formation process and the image collapse process take place for each configuration, in particular, when both the wire and the detection planes are placed inside the caustic region, which has not been studied before. For the first time, we remark that not only the intersections of the object and detection planes with the caustic are important in the change of pattern topology; but also the projection of the intersection between the caustic and the object plane mapped onto the detection plane; and the virtual projection of the intersection between the caustic and the detection plane mapped onto the object plane. We present that for the new configurations of the optical system, the wire image is curves of the Tschirnhausen’s cubic, the piriform and the deformed eight-curve types.

Detection of Multiple Stationary Humans Using UWB MIMO Radar.

PubMed

Liang, Fulai; Qi, Fugui; An, Qiang; Lv, Hao; Chen, Fuming; Li, Zhao; Wang, Jianqi

2016-11-16

Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls.

Detection of Multiple Stationary Humans Using UWB MIMO Radar

PubMed Central

Liang, Fulai; Qi, Fugui; An, Qiang; Lv, Hao; Chen, Fuming; Li, Zhao; Wang, Jianqi

2016-01-01

Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls. PMID:27854356

Robust multiperson detection and tracking for mobile service and social robots.

PubMed

Li, Liyuan; Yan, Shuicheng; Yu, Xinguo; Tan, Yeow Kee; Li, Haizhou

2012-10-01

This paper proposes an efficient system which integrates multiple vision models for robust multiperson detection and tracking for mobile service and social robots in public environments. The core technique is a novel maximum likelihood (ML)-based algorithm which combines the multimodel detections in mean-shift tracking. First, a likelihood probability which integrates detections and similarity to local appearance is defined. Then, an expectation-maximization (EM)-like mean-shift algorithm is derived under the ML framework. In each iteration, the E-step estimates the associations to the detections, and the M-step locates the new position according to the ML criterion. To be robust to the complex crowded scenarios for multiperson tracking, an improved sequential strategy to perform the mean-shift tracking is proposed. Under this strategy, human objects are tracked sequentially according to their priority order. To balance the efficiency and robustness for real-time performance, at each stage, the first two objects from the list of the priority order are tested, and the one with the higher score is selected. The proposed method has been successfully implemented on real-world service and social robots. The vision system integrates stereo-based and histograms-of-oriented-gradients-based human detections, occlusion reasoning, and sequential mean-shift tracking. Various examples to show the advantages and robustness of the proposed system for multiperson tracking from mobile robots are presented. Quantitative evaluations on the performance of multiperson tracking are also performed. Experimental results indicate that significant improvements have been achieved by using the proposed method.

ROKU: a novel method for identification of tissue-specific genes.

PubMed

Kadota, Koji; Ye, Jiazhen; Nakai, Yuji; Terada, Tohru; Shimizu, Kentaro

2006-06-12

One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes.

Multitask saliency detection model for synthetic aperture radar (SAR) image and its application in SAR and optical image fusion

NASA Astrophysics Data System (ADS)

Liu, Chunhui; Zhang, Duona; Zhao, Xintao

2018-03-01

Saliency detection in synthetic aperture radar (SAR) images is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR images. We extract four features of the SAR image, which include the intensity, orientation, uniqueness, and global contrast, as the input of the MSD model. The saliency map is generated by the multitask sparsity pursuit, which integrates the multiple features collaboratively. Detection of different scale features is also taken into consideration. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps obtained by the MSD model, we apply the saliency map of the SAR image to the SAR and color optical image fusion. The experimental results of real data show that the saliency map obtained by the MSD model helps to improve the fusion effect, and the salient areas in the SAR image can be highlighted in the fusion results.

The concurrent multiplicative-additive approach for gauge-radar/satellite multisensor precipitation estimates

NASA Astrophysics Data System (ADS)

Garcia-Pintado, J.; Barberá, G. G.; Erena Arrabal, M.; Castillo, V. M.

2010-12-01

Objective analysis schemes (OAS), also called ``succesive correction methods'' or ``observation nudging'', have been proposed for multisensor precipitation estimation combining remote sensing data (meteorological radar or satellite) with data from ground-based raingauge networks. However, opposite to the more complex geostatistical approaches, the OAS techniques for this use are not optimized. On the other hand, geostatistical techniques ideally require, at the least, modelling the covariance from the rain gauge data at every time step evaluated, which commonly cannot be soundly done. Here, we propose a new procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) for operational rainfall estimation using rain gauges and meteorological radar, which does not require explicit modelling of spatial covariances. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on the OAS, whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The approach considers radar estimates as background a priori information (first guess), so that nudging to observations (gauges) may be relaxed smoothly to the first guess, and the relaxation shape is obtained from the sequential optimization. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km2. Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, an OAS spatially variable adjustment with multiplicative factors, ordinary cokriging, and kriging with external drift. In theory, it could be equally applicable to gauge-satellite estimates and other hydrometeorological variables.

Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application

PubMed Central

Maxwell, Susan K.

2010-01-01

Satellite imagery and aerial photography represent a vast resource to significantly enhance environmental mapping and modeling applications for use in understanding spatio-temporal relationships between environment and health. Deriving boundaries of land cover objects, such as trees, buildings, and crop fields, from image data has traditionally been performed manually using a very time consuming process of hand digitizing. Boundary detection algorithms are increasingly being applied using object-based image analysis (OBIA) technology to automate the process. The purpose of this paper is to present an overview and demonstrate the application of OBIA for delineating land cover features at multiple scales using a high resolution aerial photograph (1 m) and a medium resolution Landsat image (30 m) time series in the context of a pesticide spray drift exposure application. PMID:21135917

Extreme magnification of an individual star at redshift 1.5 by a galaxy-cluster lens

NASA Astrophysics Data System (ADS)

Kelly, Patrick L.; Diego, Jose M.; Rodney, Steven; Kaiser, Nick; Broadhurst, Tom; Zitrin, Adi; Treu, Tommaso; Pérez-González, Pablo G.; Morishita, Takahiro; Jauzac, Mathilde; Selsing, Jonatan; Oguri, Masamune; Pueyo, Laurent; Ross, Timothy W.; Filippenko, Alexei V.; Smith, Nathan; Hjorth, Jens; Cenko, S. Bradley; Wang, Xin; Howell, D. Andrew; Richard, Johan; Frye, Brenda L.; Jha, Saurabh W.; Foley, Ryan J.; Norman, Colin; Bradac, Marusa; Zheng, Weikang; Brammer, Gabriel; Benito, Alberto Molino; Cava, Antonio; Christensen, Lise; de Mink, Selma E.; Graur, Or; Grillo, Claudio; Kawamata, Ryota; Kneib, Jean-Paul; Matheson, Thomas; McCully, Curtis; Nonino, Mario; Pérez-Fournon, Ismael; Riess, Adam G.; Rosati, Piero; Schmidt, Kasper Borello; Sharon, Keren; Weiner, Benjamin J.

2018-04-01

Galaxy-cluster gravitational lenses can magnify background galaxies by a total factor of up to 50. Here we report an image of an individual star at redshift z = 1.49 (dubbed MACS J1149 Lensed Star 1) magnified by more than ×2,000. A separate image, detected briefly 0.26″ from Lensed Star 1, is probably a counterimage of the first star demagnified for multiple years by an object of ≳3 solar masses in the cluster. For reasonable assumptions about the lensing system, microlensing fluctuations in the stars' light curves can yield evidence about the mass function of intracluster stars and compact objects, including binary fractions and specific stellar evolution and supernova models. Dark-matter subhaloes or massive compact objects may help to account for the two images' long-term brightness ratio.

Implications of clinical trial design on sample size requirements.

PubMed

Leon, Andrew C

2008-07-01

The primary goal in designing a randomized controlled clinical trial (RCT) is to minimize bias in the estimate of treatment effect. Randomized group assignment, double-blinded assessments, and control or comparison groups reduce the risk of bias. The design must also provide sufficient statistical power to detect a clinically meaningful treatment effect and maintain a nominal level of type I error. An attempt to integrate neurocognitive science into an RCT poses additional challenges. Two particularly relevant aspects of such a design often receive insufficient attention in an RCT. Multiple outcomes inflate type I error, and an unreliable assessment process introduces bias and reduces statistical power. Here we describe how both unreliability and multiple outcomes can increase the study costs and duration and reduce the feasibility of the study. The objective of this article is to consider strategies that overcome the problems of unreliability and multiplicity.

Human immunodeficiency virus (HIV) is highly associated with giant idiopathic esophageal ulcers in acquired immunodeficiency syndrome (AIDS) patients

PubMed Central

Lv, Bei; Cheng, Xin; Gao, Jackson; Zhao, Hong; Chen, Liping; Wang, Liwei; Huang, Shaoping; Fan, Zhenyu; Zhang, Renfang; Shen, Yinzhong; Li, Lei; Liu, Baochi; Qi, Tangkai; Wang, Jing; Cheng, Jilin

2016-01-01

Objective: This study aimed to determine whether the human immunodeficiency virus (HIV) exists in giant idiopathic esophageal ulcers in the patients with acquired immune deficiency syndrome (AIDS). Methods: 16 AIDS patients with a primary complaint of epigastric discomfort were examined by gastroscopy. Multiple and giant esophageal ulcers were biopsied and analyzed with pathology staining and reverse transcription-polymerase chain reaction (RT-PCR) to determine the potential pathogenic microorganisms, including HIV, cytomegalovirus (CMV) and herpes simplex viruses (HSV). Results: HIV was detected in ulcer samples from 12 out of these 16 patients. Ulcers in 2 patients were infected with CMV and ulcers in another 2 patients were found HSV positive. No obvious cancerous pathological changes were found in these multiple giant esophageal ulcer specimens. Conclusion: HIV may be one of the major causative agents of multiple benign giant esophageal ulcers in AIDS patients. PMID:27830031

Salient object detection based on discriminative boundary and multiple cues integration

NASA Astrophysics Data System (ADS)

Jiang, Qingzhu; Wu, Zemin; Tian, Chang; Liu, Tao; Zeng, Mingyong; Hu, Lei

2016-01-01

In recent years, many saliency models have achieved good performance by taking the image boundary as the background prior. However, if all boundaries of an image are equally and artificially selected as background, misjudgment may happen when the object touches the boundary. We propose an algorithm called weighted contrast optimization based on discriminative boundary (wCODB). First, a background estimation model is reliably constructed through discriminating each boundary via Hausdorff distance. Second, the background-only weighted contrast is improved by fore-background weighted contrast, which is optimized through weight-adjustable optimization framework. Then to objectively estimate the quality of a saliency map, a simple but effective metric called spatial distribution of saliency map and mean saliency in covered window ratio (MSR) is designed. Finally, in order to further promote the detection result using MSR as the weight, we propose a saliency fusion framework to integrate three other cues-uniqueness, distribution, and coherence from three representative methods into our wCODB model. Extensive experiments on six public datasets demonstrate that our wCODB performs favorably against most of the methods based on boundary, and the integrated result outperforms all state-of-the-art methods.

Semantic Image Segmentation with Contextual Hierarchical Models.

PubMed

Seyedhosseini, Mojtaba; Tasdizen, Tolga

2016-05-01

Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in semantic segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM), which learns contextual information in a hierarchical framework for semantic segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at multiple resolutions through the hierarchy. Contextual hierarchical model is purely based on the input image patches and does not make use of any fragments or shape examples. Hence, it is applicable to a variety of problems such as object segmentation and edge detection. We demonstrate that CHM performs at par with state-of-the-art on Stanford background and Weizmann horse datasets. It also outperforms state-of-the-art edge detection methods on NYU depth dataset and achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500).

Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels

PubMed Central

Hyun, Seung Won; Wong, Weng Kee

2016-01-01

We construct an optimal design to simultaneously estimate three common interesting features in a dose-finding trial with possibly different emphasis on each feature. These features are (1) the shape of the dose-response curve, (2) the median effective dose and (3) the minimum effective dose level. A main difficulty of this task is that an optimal design for a single objective may not perform well for other objectives. There are optimal designs for dual objectives in the literature but we were unable to find optimal designs for 3 or more objectives to date with a concrete application. A reason for this is that the approach for finding a dual-objective optimal design does not work well for a 3 or more multiple-objective design problem. We propose a method for finding multiple-objective optimal designs that estimate the three features with user-specified higher efficiencies for the more important objectives. We use the flexible 4-parameter logistic model to illustrate the methodology but our approach is applicable to find multiple-objective optimal designs for other types of objectives and models. We also investigate robustness properties of multiple-objective optimal designs to mis-specification in the nominal parameter values and to a variation in the optimality criterion. We also provide computer code for generating tailor made multiple-objective optimal designs. PMID:26565557

Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels.

PubMed

Hyun, Seung Won; Wong, Weng Kee

2015-11-01

We construct an optimal design to simultaneously estimate three common interesting features in a dose-finding trial with possibly different emphasis on each feature. These features are (1) the shape of the dose-response curve, (2) the median effective dose and (3) the minimum effective dose level. A main difficulty of this task is that an optimal design for a single objective may not perform well for other objectives. There are optimal designs for dual objectives in the literature but we were unable to find optimal designs for 3 or more objectives to date with a concrete application. A reason for this is that the approach for finding a dual-objective optimal design does not work well for a 3 or more multiple-objective design problem. We propose a method for finding multiple-objective optimal designs that estimate the three features with user-specified higher efficiencies for the more important objectives. We use the flexible 4-parameter logistic model to illustrate the methodology but our approach is applicable to find multiple-objective optimal designs for other types of objectives and models. We also investigate robustness properties of multiple-objective optimal designs to mis-specification in the nominal parameter values and to a variation in the optimality criterion. We also provide computer code for generating tailor made multiple-objective optimal designs.

Rate of Detection of Multiple Organisms and Clostridium difficile with Stool Multiplex PCR Detection Test in Pediatrics

PubMed Central

Mangla, Saisho; Villalobos, Tibisay

2017-01-01

Abstract Background New multiplex molecular assays have been developed to determine the etiology of infectious gastroenteritis. Unfortunately, these assays can detect multiple organisms simultaneously along with Clostridium difficile (C.diff), making it difficult to differentiate true pathogen vs. colonization. In January 2015, our institution switched from traditional testing methods to a multiplex polymerase chain reaction (PCR) detection test (FilmArrayTM Gastrointestinal Panel. BioFireDX, Salt Lake City, Utah). The objective of our study was to determine the number of FilmArrayTMpanels that detected C.diff and/or multiple organisms. Methods We conducted a retrospective data review of FilmArray™ panels in pediatric patients 18 years and younger from January 2015 to December 2016. Stool samples were received from both inpatient and outpatient setting. Results In 2016, 495 FilmArray™ panels were reviewed and 300 (61%) isolated at least one organism. Among the positives panels, 206 (69%) detected one organism, 73 (24%) detected 2 organisms and 21 (7.0%) detected 3 or more organisms. No more than 4 organisms were detected in a single panel. C.diff was most commonly isolated, found 105 times (25%), and 34 (31%) of these were in children younger than 2 years. Amongst the 105 C.diff isolates, 64% were alone and 35% with another organism. Amongst children younger than 2, C.diff was isolated alone in 13 (38%) samples and with another organism in 21 (62%) samples. In 2015, 353 panels were reviewed with a detection rate of 60.3%. C.Diff was isolated 70 times (24% of total isolates) and 22 (31%) were in children younger than 2 years. Amongst those C.diff isolates, 49% were alone and 51% with another organism. Amongst children younger than 2, C.diff was isolated alone in 8 (38%) samples and with another organism in 14 (62%) samples. Conclusion Although the FilmArray™ Gastrointestinal Panel is a useful single modality for determining the etiology of infectious gastroenteritis, more than one organism is frequently detected. C.diff has become the most common organism isolated among children at our institution. Caution should be used when interpreting the isolation of C.diff in younger children and when isolated with other organisms. Disclosures All authors: No reported disclosures.

Determining root correspondence between previously and newly detected objects

DOEpatents

Paglieroni, David W.; Beer, N Reginald

2014-06-17

A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

Multirobot autonomous landmine detection using distributed multisensor information aggregation

NASA Astrophysics Data System (ADS)

Jumadinova, Janyl; Dasgupta, Prithviraj

2012-06-01

We consider the problem of distributed sensor information fusion by multiple autonomous robots within the context of landmine detection. We assume that different landmines can be composed of different types of material and robots are equipped with different types of sensors, while each robot has only one type of landmine detection sensor on it. We introduce a novel technique that uses a market-based information aggregation mechanism called a prediction market. Each robot is provided with a software agent that uses sensory input of the robot and performs calculations of the prediction market technique. The result of the agent's calculations is a 'belief' representing the confidence of the agent in identifying the object as a landmine. The beliefs from different robots are aggregated by the market mechanism and passed on to a decision maker agent. The decision maker agent uses this aggregate belief information about a potential landmine and makes decisions about which other robots should be deployed to its location, so that the landmine can be confirmed rapidly and accurately. Our experimental results show that, for identical data distributions and settings, using our prediction market-based information aggregation technique increases the accuracy of object classification favorably as compared to two other commonly used techniques.

Detection of the Phoenicids meteor shower in 2014

NASA Astrophysics Data System (ADS)

Sato, Mikiya; Watanabe, Jun-ichi; Tsuchiya, Chie; Moorhead, Althea V.; Moser, Danielle E.; Brown, Peter G.; Cooke, William J.

2017-09-01

An appearance of the Phoenicids meteor shower was predicted in 2014 by using a dust trail simulation of an outburst of 1956. We detected Phoenicids meteors on December 2 through multiple observation methods. The NASA All Sky Fireball Network and the Southern Ontario Meteor Network detected five meteors of Phoenicids via video observation. The Canadian Meteor Orbit Radar (CMOR) found fourteen candidate meteors, eight of which were confirmed as Phoenicids. The observed radiant point is consistent with that of our model predictions. In addition to the above observations, a visual observation was carried out by the Japanese team near the Observatorio del Roque de los Muchachos (ORM) of Instituto de Astrofisica de Canarias (IAC) in La Palma Island. The obtained zenithal hourly rate (ZHR) was 16.4±4.9. The maximum ZHR was roughly estimated to be between 20 and 30, which indicates that the cometary activity of parent object 289P/Blanpain in the early 20th century was only about one fifth or one eighth as high as its activity in the late 18th and early 19th century. Accordingly, it seems to be the case that 289P/Blanpain is gradually transforming from a comet to a dormant object.

Revealing Nucleic Acid Mutations Using Förster Resonance Energy Transfer-Based Probes

PubMed Central

Junager, Nina P. L.; Kongsted, Jacob; Astakhova, Kira

2016-01-01

Nucleic acid mutations are of tremendous importance in modern clinical work, biotechnology and in fundamental studies of nucleic acids. Therefore, rapid, cost-effective and reliable detection of mutations is an object of extensive research. Today, Förster resonance energy transfer (FRET) probes are among the most often used tools for the detection of nucleic acids and in particular, for the detection of mutations. However, multiple parameters must be taken into account in order to create efficient FRET probes that are sensitive to nucleic acid mutations. In this review; we focus on the design principles for such probes and available computational methods that allow for their rational design. Applications of advanced, rationally designed FRET probes range from new insights into cellular heterogeneity to gaining new knowledge of nucleic acid structures directly in living cells. PMID:27472344

Attribute and topology based change detection in a constellation of previously detected objects

DOEpatents

Paglieroni, David W.; Beer, Reginald N.

2016-01-19

A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

A comparison of moving object detection methods for real-time moving object detection

NASA Astrophysics Data System (ADS)

Roshan, Aditya; Zhang, Yun

2014-06-01

Moving object detection has a wide variety of applications from traffic monitoring, site monitoring, automatic theft identification, face detection to military surveillance. Many methods have been developed across the globe for moving object detection, but it is very difficult to find one which can work globally in all situations and with different types of videos. The purpose of this paper is to evaluate existing moving object detection methods which can be implemented in software on a desktop or laptop, for real time object detection. There are several moving object detection methods noted in the literature, but few of them are suitable for real time moving object detection. Most of the methods which provide for real time movement are further limited by the number of objects and the scene complexity. This paper evaluates the four most commonly used moving object detection methods as background subtraction technique, Gaussian mixture model, wavelet based and optical flow based methods. The work is based on evaluation of these four moving object detection methods using two (2) different sets of cameras and two (2) different scenes. The moving object detection methods have been implemented using MatLab and results are compared based on completeness of detected objects, noise, light change sensitivity, processing time etc. After comparison, it is observed that optical flow based method took least processing time and successfully detected boundary of moving objects which also implies that it can be implemented for real-time moving object detection.

Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm

NASA Astrophysics Data System (ADS)

Hao, Yufang; Xie, Shaodong

2018-03-01

Air quality monitoring networks play a significant role in identifying the spatiotemporal patterns of air pollution, and they need to be deployed efficiently, with a minimum number of sites. The revision and optimal adjustment of existing monitoring networks is crucial for cities that have undergone rapid urban expansion and experience temporal variations in pollution patterns. The approach based on the Weather Research and Forecasting-California PUFF (WRF-CALPUFF) model and genetic algorithm (GA) was developed to design an optimal monitoring network. The maximization of coverage with minimum overlap and the ability to detect violations of standards were developed as the design objectives for redistributed networks. The non-dominated sorting genetic algorithm was applied to optimize the network size and site locations simultaneously for Shijiazhuang city, one of the most polluted cities in China. The assessment on the current network identified the insufficient spatial coverage of SO2 and NO2 monitoring for the expanding city. The optimization results showed that significant improvements were achieved in multiple objectives by redistributing the original network. Efficient coverage of the resulting designs improved to 60.99% and 76.06% of the urban area for SO2 and NO2, respectively. The redistributing design for multi-pollutant including 8 sites was also proposed, with the spatial representation covered 52.30% of the urban area and the overlapped areas decreased by 85.87% compared with the original network. The abilities to detect violations of standards were not improved as much as the other two objectives due to the conflicting nature between the multiple objectives. Additionally, the results demonstrated that the algorithm was slightly sensitive to the parameter settings, with the number of generations presented the most significant effect. Overall, our study presents an effective and feasible procedure for air quality network optimization at a city scale.

Multi-Temporal Multi-Sensor Analysis of Urbanization and Environmental/Climate Impact in China for Sustainable Urban Development

NASA Astrophysics Data System (ADS)

Ban, Yifang; Gong, Peng; Gamba, Paolo; Taubenbock, Hannes; Du, Peijun

2016-08-01

The overall objective of this research is to investigate multi-temporal, multi-scale, multi-sensor satellite data for analysis of urbanization and environmental/climate impact in China to support sustainable planning. Multi- temporal multi-scale SAR and optical data have been evaluated for urban information extraction using innovative methods and algorithms, including KTH- Pavia Urban Extractor, Pavia UEXT, and an "exclusion- inclusion" framework for urban extent extraction, and KTH-SEG, a novel object-based classification method for detailed urban land cover mapping. Various pixel- based and object-based change detection algorithms were also developed to extract urban changes. Several Chinese cities including Beijing, Shanghai and Guangzhou are selected as study areas. Spatio-temporal urbanization patterns and environmental impact at regional, metropolitan and city core were evaluated through ecosystem service, landscape metrics, spatial indices, and/or their combinations. The relationship between land surface temperature and land-cover classes was also analyzed.The urban extraction results showed that urban areas and small towns could be well extracted using multitemporal SAR data with the KTH-Pavia Urban Extractor and UEXT. The fusion of SAR data at multiple scales from multiple sensors was proven to improve urban extraction. For urban land cover mapping, the results show that the fusion of multitemporal SAR and optical data could produce detailed land cover maps with improved accuracy than that of SAR or optical data alone. Pixel-based and object-based change detection algorithms developed with the project were effective to extract urban changes. Comparing the urban land cover results from mulitemporal multisensor data, the environmental impact analysis indicates major losses for food supply, noise reduction, runoff mitigation, waste treatment and global climate regulation services through landscape structural changes in terms of decreases in service area, edge contamination and fragmentation. In terms ofclimate impact, the results indicate that land surface temperature can be related to land use/land cover classes.

A Class of CFAR Detectors Implemented in the SAR-GMTI Processor gmtipro2: Mathematical Formulation of the Algorithms

DTIC Science & Technology

2015-02-01

Right of Canada as represented by the Minister of National Defence, 2015 c© Sa Majesté la Reine (en droit du Canada), telle que représentée par le...References [1] Chiu, S. (2010), Moving target parameter estimation for RADARSAT-2 Moving Object Detection EXperiment (MODEX), International Journal of...of multiple sinusoids in noise, In Proceedings. (ICASSP ’01). 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 5

[Fatal air embolism during open eye surgery].

PubMed

Dermigny, F; Daelman, F; Guinot, P-G; Hubert, V; Jezraoui, P; Thomas, F; Milazzo, S; Dupont, H

2008-10-01

Gas embolism is well known for a specific subset of surgical interventions. Prevention and early detection are the main objectives of the anesthetic and surgical team. However, it may exceptionally occur during eye surgery with dramatic outcomes. We report the case of a 51-year-old man, ASA physical status 1, who presented a cardiac arrest during an open eye surgery for the extraction of a foreign body with intraocular air injection. Multiple organ failure has not been improved by hyperbaric oxygen therapy and the outcome was fatal.

Mach Cones in a Coulomb Lattice and a Dusty Plasma

NASA Astrophysics Data System (ADS)

Samsonov, D.; Goree, J.; Ma, Z. W.; Bhattacharjee, A.; Thomas, H. M.; Morfill, G. E.

1999-11-01

Mach cones, or V-shaped disturbances created by supersonic objects, have been detected in a two-dimensional Coulomb crystal. Electrically charged microspheres levitated in a glow-discharge plasma formed a dusty plasma, with particles arranged in a hexagonal lattice in a horizontal plane. Beneath this lattice plane, a sphere moved faster than the lattice sound speed. Mach cones were double, first compressive then rarefactive, due to the strongly coupled crystalline state. Molecular dynamics simulations using a Yukawa potential also show multiple Mach cones.

How does the brain rapidly learn and reorganize view-invariant and position-invariant object representations in the inferotemporal cortex?

PubMed

Cao, Yongqiang; Grossberg, Stephen; Markowitz, Jeffrey

2011-12-01

All primates depend for their survival on being able to rapidly learn about and recognize objects. Objects may be visually detected at multiple positions, sizes, and viewpoints. How does the brain rapidly learn and recognize objects while scanning a scene with eye movements, without causing a combinatorial explosion in the number of cells that are needed? How does the brain avoid the problem of erroneously classifying parts of different objects together at the same or different positions in a visual scene? In monkeys and humans, a key area for such invariant object category learning and recognition is the inferotemporal cortex (IT). A neural model is proposed to explain how spatial and object attention coordinate the ability of IT to learn invariant category representations of objects that are seen at multiple positions, sizes, and viewpoints. The model clarifies how interactions within a hierarchy of processing stages in the visual brain accomplish this. These stages include the retina, lateral geniculate nucleus, and cortical areas V1, V2, V4, and IT in the brain's What cortical stream, as they interact with spatial attention processes within the parietal cortex of the Where cortical stream. The model builds upon the ARTSCAN model, which proposed how view-invariant object representations are generated. The positional ARTSCAN (pARTSCAN) model proposes how the following additional processes in the What cortical processing stream also enable position-invariant object representations to be learned: IT cells with persistent activity, and a combination of normalizing object category competition and a view-to-object learning law which together ensure that unambiguous views have a larger effect on object recognition than ambiguous views. The model explains how such invariant learning can be fooled when monkeys, or other primates, are presented with an object that is swapped with another object during eye movements to foveate the original object. The swapping procedure is predicted to prevent the reset of spatial attention, which would otherwise keep the representations of multiple objects from being combined by learning. Li and DiCarlo (2008) have presented neurophysiological data from monkeys showing how unsupervised natural experience in a target swapping experiment can rapidly alter object representations in IT. The model quantitatively simulates the swapping data by showing how the swapping procedure fools the spatial attention mechanism. More generally, the model provides a unifying framework, and testable predictions in both monkeys and humans, for understanding object learning data using neurophysiological methods in monkeys, and spatial attention, episodic learning, and memory retrieval data using functional imaging methods in humans. Copyright © 2011 Elsevier Ltd. All rights reserved.

Using YOLO based deep learning network for real time detection and localization of lung nodules from low dose CT scans

NASA Astrophysics Data System (ADS)

Ramachandran S., Sindhu; George, Jose; Skaria, Shibon; V. V., Varun

2018-02-01

Lung cancer is the leading cause of cancer related deaths in the world. The survival rate can be improved if the presence of lung nodules are detected early. This has also led to more focus being given to computer aided detection (CAD) and diagnosis of lung nodules. The arbitrariness of shape, size and texture of lung nodules is a challenge to be faced when developing these detection systems. In the proposed work we use convolutional neural networks to learn the features for nodule detection, replacing the traditional method of handcrafting features like geometric shape or texture. Our network uses the DetectNet architecture based on YOLO (You Only Look Once) to detect the nodules in CT scans of lung. In this architecture, object detection is treated as a regression problem with a single convolutional network simultaneously predicting multiple bounding boxes and class probabilities for those boxes. By performing training using chest CT scans from Lung Image Database Consortium (LIDC), NVIDIA DIGITS and Caffe deep learning framework, we show that nodule detection using this single neural network can result in reasonably low false positive rates with high sensitivity and precision.

Studying visual attention using the multiple object tracking paradigm: A tutorial review.

PubMed

Meyerhoff, Hauke S; Papenmeier, Frank; Huff, Markus

2017-07-01

Human observers are capable of tracking multiple objects among identical distractors based only on their spatiotemporal information. Since the first report of this ability in the seminal work of Pylyshyn and Storm (1988, Spatial Vision, 3, 179-197), multiple object tracking has attracted many researchers. A reason for this is that it is commonly argued that the attentional processes studied with the multiple object paradigm apparently match the attentional processing during real-world tasks such as driving or team sports. We argue that multiple object tracking provides a good mean to study the broader topic of continuous and dynamic visual attention. Indeed, several (partially contradicting) theories of attentive tracking have been proposed within the almost 30 years since its first report, and a large body of research has been conducted to test these theories. With regard to the richness and diversity of this literature, the aim of this tutorial review is to provide researchers who are new in the field of multiple object tracking with an overview over the multiple object tracking paradigm, its basic manipulations, as well as links to other paradigms investigating visual attention and working memory. Further, we aim at reviewing current theories of tracking as well as their empirical evidence. Finally, we review the state of the art in the most prominent research fields of multiple object tracking and how this research has helped to understand visual attention in dynamic settings.

Random forest learning of ultrasonic statistical physics and object spaces for lesion detection in 2D sonomammography

NASA Astrophysics Data System (ADS)

Sheet, Debdoot; Karamalis, Athanasios; Kraft, Silvan; Noël, Peter B.; Vag, Tibor; Sadhu, Anup; Katouzian, Amin; Navab, Nassir; Chatterjee, Jyotirmoy; Ray, Ajoy K.

2013-03-01

Breast cancer is the most common form of cancer in women. Early diagnosis can significantly improve lifeexpectancy and allow different treatment options. Clinicians favor 2D ultrasonography for breast tissue abnormality screening due to high sensitivity and specificity compared to competing technologies. However, inter- and intra-observer variability in visual assessment and reporting of lesions often handicaps its performance. Existing Computer Assisted Diagnosis (CAD) systems though being able to detect solid lesions are often restricted in performance. These restrictions are inability to (1) detect lesion of multiple sizes and shapes, and (2) differentiate between hypo-echoic lesions from their posterior acoustic shadowing. In this work we present a completely automatic system for detection and segmentation of breast lesions in 2D ultrasound images. We employ random forests for learning of tissue specific primal to discriminate breast lesions from surrounding normal tissues. This enables it to detect lesions of multiple shapes and sizes, as well as discriminate between hypo-echoic lesion from associated posterior acoustic shadowing. The primal comprises of (i) multiscale estimated ultrasonic statistical physics and (ii) scale-space characteristics. The random forest learns lesion vs. background primal from a database of 2D ultrasound images with labeled lesions. For segmentation, the posterior probabilities of lesion pixels estimated by the learnt random forest are hard thresholded to provide a random walks segmentation stage with starting seeds. Our method achieves detection with 99.19% accuracy and segmentation with mean contour-to-contour error < 3 pixels on a set of 40 images with 49 lesions.

Detection of Buried Objects by Means of a SAP Technique: Comparing MUSIC- and SVR-Based Approaches

NASA Astrophysics Data System (ADS)

Meschino, S.; Pajewski, L.; Pastorino, M.; Randazzo, A.; Schettini, G.

2012-04-01

This work is focused on the application of a Sub-Array Processing (SAP) technique to the detection of metallic cylindrical objects embedded in a dielectric half-space. The identification of buried cables, pipes, conduits, and other cylindrical utilities, is an important problem that has been extensively studied in the last years. Most commonly used approaches are based on the use of electromagnetic sensing: a set of antennas illuminates the ground and the collected echo is analyzed in order to extract information about the scenario and to localize the sought objects [1]. In a SAP approach, algorithms for the estimation of Directions of Arrival (DOAs) are employed [2]: they assume that the sources (in this paper, currents induced on buried targets) are in the far-field region of the receiving array, so that the received wavefront can be considered as planar, and the main angular direction of the field can be estimated. However, in electromagnetic sensing of buried objects, the scatterers are usually quite near to the antennas. Nevertheless, by dividing the whole receiving array in a suitable number of sub-arrays, and by finding a dominant DOA for each one, it is possible to localize objects that are in the far-field of the sub-array, although being in the near-field of the array. The DOAs found by the sub-arrays can be triangulated, obtaining a set of crossings with intersections condensed around object locations. In this work, the performances of two different DOA algorithms are compared. In particular, a MUltiple SIgnal Classification (MUSIC)-type method [3] and Support Vector Regression (SVR) based approach [4] are employed. The results of a Cylindrical-Wave Approach forward solver are used as input data of the detection procedure [5]. To process the crossing pattern, the region of interest is divided in small windows, and a Poisson model is adopted for the statistical distribution of intersections in the windows. Hypothesis testing procedures are used (imposing a suitable threshold from a desired false-alarm rate), to ascribe each window to the ground or to the sought objects. Numerical results are presented, for a test scenario with a circular-section cylinder in a dielectric half-space. Different values of the ground permittivity, target size, and its position with respect to the receiving array, are considered. Preliminary results on the application of MUSIC and SVR to multiple-object localization are reported. [1] H. Jol, Ground Penetrating Radar: Theory and Applications, Elsevier, Amsterdam, NL, 2009. [2] Gross F.B., Smart Antennas for Wireless Communications, McGraw-Hill, New York 2005. [3] S. Meschino, L. Pajewski, G. Schettini, "Use of a Sub-Array Statistical Approach for the Detection of a Buried Object", Near Surface Geophysics, vol. 8(5), pp. 365-375, 2010. [4] M. Pastorino, A. Randazzo, "A Smart Antenna System for Direction of Arrival Estimation based on a Support Vector Regression," IEEE Trans. Antennas Propagat., vol. 53(7), pp. 2161-2168, 2005. [5] M. Di Vico, F. Frezza, L. Pajewski, G. Schettini, "Scattering by a Finite Set of Perfectly Conducting Cylinders Buried in a Dielectric Half-Space: a Spectral-Domain Solution," IEEE Trans. Antennas Propagat., vol. 53(2), pp. 719-727, 2005.

Detection of dim targets in multiple environments

NASA Astrophysics Data System (ADS)

Mirsky, Grace M.; Woods, Matthew; Grasso, Robert J.

2013-10-01

The proliferation of a wide variety of weapons including Anti-Aircraft Artillery (AAA), rockets, and small arms presents a substantial threat to both military and civilian aircraft. To address this ever-present threat, Northrop Grumman has assessed unguided threat phenomenology to understand the underlying physical principles for detection. These principles, based upon threat transit through the atmosphere, exploit a simple phenomenon universal to all objects moving through an atmosphere comprised of gaseous media to detect and track the threat in the presence of background and clutter. Threat detection has rapidly become a crucial component of aircraft survivability systems that provide situational awareness to the crew. It is particularly important to platforms which may spend a majority of their time at low altitudes and within the effective range of a large variety of weapons. Detection of these threats presents a unique challenge as this class of threat typically has a dim signature coupled with a short duration. Correct identification of each of the threat components (muzzle flash and projectile) is important to determine trajectory and intent while minimizing false alarms and maintaining a high detection probability in all environments.

The Use of Multiple Correspondence Analysis to Explore Associations between Categories of Qualitative Variables in Healthy Ageing.

PubMed

Costa, Patrício Soares; Santos, Nadine Correia; Cunha, Pedro; Cotter, Jorge; Sousa, Nuno

2013-01-01

The main focus of this study was to illustrate the applicability of multiple correspondence analysis (MCA) in detecting and representing underlying structures in large datasets used to investigate cognitive ageing. Principal component analysis (PCA) was used to obtain main cognitive dimensions, and MCA was used to detect and explore relationships between cognitive, clinical, physical, and lifestyle variables. Two PCA dimensions were identified (general cognition/executive function and memory), and two MCA dimensions were retained. Poorer cognitive performance was associated with older age, less school years, unhealthier lifestyle indicators, and presence of pathology. The first MCA dimension indicated the clustering of general/executive function and lifestyle indicators and education, while the second association was between memory and clinical parameters and age. The clustering analysis with object scores method was used to identify groups sharing similar characteristics. The weaker cognitive clusters in terms of memory and executive function comprised individuals with characteristics contributing to a higher MCA dimensional mean score (age, less education, and presence of indicators of unhealthier lifestyle habits and/or clinical pathologies). MCA provided a powerful tool to explore complex ageing data, covering multiple and diverse variables, showing if a relationship exists and how variables are related, and offering statistical results that can be seen both analytically and visually.

Sensor Fusion Techniques for Phased-Array Eddy Current and Phased-Array Ultrasound Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arrowood, Lloyd F.

Sensor (or Data) fusion is the process of integrating multiple data sources to produce more consistent, accurate and comprehensive information than is provided by a single data source. Sensor fusion may also be used to combine multiple signals from a single modality to improve the performance of a particular inspection technique. Industrial nondestructive testing may utilize multiple sensors to acquire inspection data depending upon the object under inspection and the anticipated types of defects that can be identified. Sensor fusion can be performed at various levels of signal abstraction with each having its strengths and weaknesses. A multimodal data fusionmore » strategy first proposed by Heideklang and Shokouhi that combines spatially scattered detection locations to improve detection performance of surface-breaking and near-surface cracks in ferromagnetic metals is shown using a surface inspection example and is then extended for volumetric inspections. Utilizing data acquired from an Olympus Omniscan MX2 from both phased array eddy current and ultrasound probes on test phantoms, single and multilevel fusion techniques are employed to integrate signals from the two modalities. Preliminary results demonstrate how confidence in defect identification and interpretation benefit from sensor fusion techniques. Lastly, techniques for integrating data into radiographic and volumetric imagery from computed tomography are described and results are presented.« less

Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors.

PubMed

Kim, Jong Hyun; Hong, Hyung Gil; Park, Kang Ryoung

2017-05-08

Because intelligent surveillance systems have recently undergone rapid growth, research on accurately detecting humans in videos captured at a long distance is growing in importance. The existing research using visible light cameras has mainly focused on methods of human detection for daytime hours when there is outside light, but human detection during nighttime hours when there is no outside light is difficult. Thus, methods that employ additional near-infrared (NIR) illuminators and NIR cameras or thermal cameras have been used. However, in the case of NIR illuminators, there are limitations in terms of the illumination angle and distance. There are also difficulties because the illuminator power must be adaptively adjusted depending on whether the object is close or far away. In the case of thermal cameras, their cost is still high, which makes it difficult to install and use them in a variety of places. Because of this, research has been conducted on nighttime human detection using visible light cameras, but this has focused on objects at a short distance in an indoor environment or the use of video-based methods to capture multiple images and process them, which causes problems related to the increase in the processing time. To resolve these problems, this paper presents a method that uses a single image captured at night on a visible light camera to detect humans in a variety of environments based on a convolutional neural network. Experimental results using a self-constructed Dongguk night-time human detection database (DNHD-DB1) and two open databases (Korea advanced institute of science and technology (KAIST) and computer vision center (CVC) databases), as well as high-accuracy human detection in a variety of environments, show that the method has excellent performance compared to existing methods.

Detection of Brain Reorganization in Pediatric Multiple Sclerosis Using Functional MRI

DTIC Science & Technology

2015-10-01

Page | 2 AWARD NUMBER: W81XWH-13-1-0464 TITLE: Detection of Brain Reorganization in Pediatric Multiple Sclerosis Using Functional MRI...Sep 2014 – 29 Sep 2015 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Detection of Brain Reorganization in Pediatric Multiple Sclerosis Using Functional...findings include: 1) detection of brain organization in a cohort of 24 pediatric onset multiple sclerosis patients (POMS) and 25 healthy controls

Sensor feature fusion for detecting buried objects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, G.A.; Sengupta, S.K.; Sherwood, R.J.

1993-04-01

Given multiple registered images of the earth`s surface from dual-band sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised teaming pattern recognition approach to detect metal and plastic land mines buried in soil. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in amore » two step process to classify a subimage. Thee first step, referred to as feature selection, determines the features of sub-images which result in the greatest separability among the classes. The second step, image labeling, uses the selected features and the decisions from a pattern classifier to label the regions in the image which are likely to correspond to buried mines. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the sensors add value to the detection system. The most important features from the various sensors are fused using supervised teaming pattern classifiers (including neural networks). We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing feature information from multiple sensor types, including dual-band infrared and ground penetrating radar. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved operational problem of detecting buried land mines from an airborne standoff platform.« less

The association between cecal insertion time and colorectal neoplasm detection

PubMed Central

2013-01-01

Background Information on the impact of cecal insertion time on colorectal neoplasm detection is limited. Our objective was to determine the association between cecal insertion time and colorectal neoplasm detection rate in colonoscopy screening. Methods We performed a cross-sectional study of 12,679 consecutive subjects aged 40–79 years undergoing screening colonoscopy in routine health check-ups at the Center for Health Promotion of the Samsung Medical Center from December 2007 to June 2009. Fixed effects logistic regression conditioning on colonoscopist was used to eliminate confounding due to differences in technical ability and other characteristics across colonoscopists. Results The mean cecal insertion time was 5.9 (SD, 4.4 minutes). We identified 4,249 (33.5%) participants with colorectal neoplasms, of whom 1,956 had small single adenomas (<5 mm), 595 had medium single adenomas (5–9 mm), and 1,699 had multiple adenomas or advanced colorectal neoplasms. The overall rates of colorectal neoplasm detection by quartiles of cecal insertion time were 36.8%, 33.4%, 32.7%, and 31.0%, respectively (p trend <0.001).The odds for small single colorectal adenoma detection was 16% lower (adjusted OR 0.84; 95% CI 0.71 to 0.99) in the fourth compared to the first quartile of insertion time (p trend 0.005). Insertion time was not associated with the detection rate of single adenomas ≥5 mm, multiple adenomas or advanced colorectal neoplasms. Conclusion Shorter insertion times were associated with increased rates of detection of small colorectal adenomas <5 mm. Cecal insertion time may be clinically relevant as missed small colorectal adenomas may progress to more advanced lesions. PMID:23915303

Visual Search as a Tool for a Quick and Reliable Assessment of Cognitive Functions in Patients with Multiple Sclerosis

PubMed Central

Utz, Kathrin S.; Hankeln, Thomas M. A.; Jung, Lena; Lämmer, Alexandra; Waschbisch, Anne; Lee, De-Hyung; Linker, Ralf A.; Schenk, Thomas

2013-01-01

Background Despite the high frequency of cognitive impairment in multiple sclerosis, its assessment has not gained entrance into clinical routine yet, due to lack of time-saving and suitable tests for patients with multiple sclerosis. Objective The aim of the study was to compare the paradigm of visual search with neuropsychological standard tests, in order to identify the test that discriminates best between patients with multiple sclerosis and healthy individuals concerning cognitive functions, without being susceptible to practice effects. Methods Patients with relapsing remitting multiple sclerosis (n = 38) and age-and gender-matched healthy individuals (n = 40) were tested with common neuropsychological tests and a computer-based visual search task, whereby a target stimulus has to be detected amongst distracting stimuli on a touch screen. Twenty-eight of the healthy individuals were re-tested in order to determine potential practice effects. Results Mean reaction time reflecting visual attention and movement time indicating motor execution in the visual search task discriminated best between healthy individuals and patients with multiple sclerosis, without practice effects. Conclusions Visual search is a promising instrument for the assessment of cognitive functions and potentially cognitive changes in patients with multiple sclerosis thanks to its good discriminatory power and insusceptibility to practice effects. PMID:24282604

Characteristics of objective daytime sleep among individuals with earthquake-related posttraumatic stress disorder: A pilot community-based polysomnographic and multiple sleep latency test study.

PubMed

Zhang, Yan; Li, Yun; Zhu, Hongru; Cui, Haofei; Qiu, Changjian; Tang, Xiangdong; Zhang, Wei

2017-01-01

Little is known about the objective sleep characteristics of patients with posttraumatic stress disorder (PTSD). The present study examines the association between PTSD symptom severity and objective daytime sleep characteristics measured using the Multiple Sleep Latency Test (MSLT) in therapy-naïve patients with earthquake-related PTSD. A total of 23 PTSD patients and 13 trauma-exposed non-PTSD (TEN-PTSD) subjects completed one-night in-lab polysomnography (PSG) followed by a standard MSLT. 8 of the 23 PTSD patients received paroxetine treatment. Compared to the TEN-PTSD subjects, no significant nighttime sleep disturbances were detected by PSG in the subjects with PTSD; however, a shorter mean MSLT value was found in the subjects with PTSD. After adjustment for age, sex, and body mass index, PTSD symptoms, particularly hyperarousal, were found to be independently associated with a shorter MSLT value. Further, the mean MSLT value increased significantly after therapy in PTSD subjects. A shorter MSLT value may be a reliable index of the medical severity of PTSD, while an improvement in MSLT values might also be a reliable marker for evaluating therapeutic efficacy in PTSD patients. Copyright © 2016. Published by Elsevier Ireland Ltd.

Characterization of fluid flow by digital correlation of scattered light

NASA Technical Reports Server (NTRS)

Gilbert, John A.; Matthys, Donald R.

1989-01-01

The objective is to produce a physical system suitable for a space environment that can measure fluid velocities in a three-dimensional volume by the development of a particle correlation velocimetry technique. Experimental studies were conducted on a field test cell to demonstrate the suitability and accuracy of digital correlation techniques for measuring two-dimensional fluid flows. This objective was satisfied by: (1) the design of an appropriate illumination and detection system for making velocity measurements within a test cell; (2) the design and construction of a test cell; (3) the preliminary evaluations on fluid and seeding requirements; and (4) the performance of controlled tests using a multiple exposure correlation technique. This presentation is represented by viewgraphs with very little text.

Optical encryption of multiple three-dimensional objects based on multiple interferences and single-pixel digital holography

NASA Astrophysics Data System (ADS)

Wang, Ying; Liu, Qi; Wang, Jun; Wang, Qiong-Hua

2018-03-01

We present an optical encryption method of multiple three-dimensional objects based on multiple interferences and single-pixel digital holography. By modifying the Mach–Zehnder interferometer, the interference of the multiple objects beams and the one reference beam is used to simultaneously encrypt multiple objects into a ciphertext. During decryption, each three-dimensional object can be decrypted independently without having to decrypt other objects. Since the single-pixel digital holography based on compressive sensing theory is introduced, the encrypted data of this method is effectively reduced. In addition, recording fewer encrypted data can greatly reduce the bandwidth of network transmission. Moreover, the compressive sensing essentially serves as a secret key that makes an intruder attack invalid, which means that the system is more secure than the conventional encryption method. Simulation results demonstrate the feasibility of the proposed method and show that the system has good security performance. Project supported by the National Natural Science Foundation of China (Grant Nos. 61405130 and 61320106015).

Beyond scene gist: Objects guide search more than scene background.

PubMed

Koehler, Kathryn; Eckstein, Miguel P

2017-06-01

Although the facilitation of visual search by contextual information is well established, there is little understanding of the independent contributions of different types of contextual cues in scenes. Here we manipulated 3 types of contextual information: object co-occurrence, multiple object configurations, and background category. We isolated the benefits of each contextual cue to target detectability, its impact on decision bias, confidence, and the guidance of eye movements. We find that object-based information guides eye movements and facilitates perceptual judgments more than scene background. The degree of guidance and facilitation of each contextual cue can be related to its inherent informativeness about the target spatial location as measured by human explicit judgments about likely target locations. Our results improve the understanding of the contributions of distinct contextual scene components to search and suggest that the brain's utilization of cues to guide eye movements is linked to the cue's informativeness about the target's location. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Polarization-based index of refraction and reflection angle estimation for remote sensing applications.

PubMed

Thilak, Vimal; Voelz, David G; Creusere, Charles D

2007-10-20

A passive-polarization-based imaging system records the polarization state of light reflected by objects that are illuminated with an unpolarized and generally uncontrolled source. Such systems can be useful in many remote sensing applications including target detection, object segmentation, and material classification. We present a method to jointly estimate the complex index of refraction and the reflection angle (reflected zenith angle) of a target from multiple measurements collected by a passive polarimeter. An expression for the degree of polarization is derived from the microfacet polarimetric bidirectional reflectance model for the case of scattering in the plane of incidence. Using this expression, we develop a nonlinear least-squares estimation algorithm for extracting an apparent index of refraction and the reflection angle from a set of polarization measurements collected from multiple source positions. Computer simulation results show that the estimation accuracy generally improves with an increasing number of source position measurements. Laboratory results indicate that the proposed method is effective for recovering the reflection angle and that the estimated index of refraction provides a feature vector that is robust to the reflection angle.

Polarization-based index of refraction and reflection angle estimation for remote sensing applications

NASA Astrophysics Data System (ADS)

Thilak, Vimal; Voelz, David G.; Creusere, Charles D.

2007-10-01

A passive-polarization-based imaging system records the polarization state of light reflected by objects that are illuminated with an unpolarized and generally uncontrolled source. Such systems can be useful in many remote sensing applications including target detection, object segmentation, and material classification. We present a method to jointly estimate the complex index of refraction and the reflection angle (reflected zenith angle) of a target from multiple measurements collected by a passive polarimeter. An expression for the degree of polarization is derived from the microfacet polarimetric bidirectional reflectance model for the case of scattering in the plane of incidence. Using this expression, we develop a nonlinear least-squares estimation algorithm for extracting an apparent index of refraction and the reflection angle from a set of polarization measurements collected from multiple source positions. Computer simulation results show that the estimation accuracy generally improves with an increasing number of source position measurements. Laboratory results indicate that the proposed method is effective for recovering the reflection angle and that the estimated index of refraction provides a feature vector that is robust to the reflection angle.

Multiagent Work Practice Simulation: Progress and Challenges

NASA Technical Reports Server (NTRS)

Clancey, William J.; Sierhuis, Maarten; Shaffe, Michael G. (Technical Monitor)

2001-01-01

Modeling and simulating complex human-system interactions requires going beyond formal procedures and information flows to analyze how people interact with each other. Such work practices include conversations, modes of communication, informal assistance, impromptu meetings, workarounds, and so on. To make these social processes visible, we have developed a multiagent simulation tool, called Brahms, for modeling the activities of people belonging to multiple groups, situated in a physical environment (geographic regions, buildings, transport vehicles, etc.) consisting of tools, documents, and a computer system. We are finding many useful applications of Brahms for system requirements analysis, instruction, implementing software agents, and as a workbench for relating cognitive and social theories of human behavior. Many challenges remain for representing work practices, including modeling: memory over multiple days, scheduled activities combining physical objects, groups, and locations on a timeline (such as a Space Shuttle mission), habitat vehicles with trajectories (such as the Shuttle), agent movement in 3D space (e.g., inside the International Space Station), agent posture and line of sight, coupled movements (such as carrying objects), and learning (mimicry, forming habits, detecting repetition, etc.).

Multiagent Work Practice Simulation: Progress and Challenges

NASA Technical Reports Server (NTRS)

Clancey, William J.; Sierhuis, Maarten

2002-01-01

Modeling and simulating complex human-system interactions requires going beyond formal procedures and information flows to analyze how people interact with each other. Such work practices include conversations, modes of communication, informal assistance, impromptu meetings, workarounds, and so on. To make these social processes visible, we have developed a multiagent simulation tool, called Brahms, for modeling the activities of people belonging to multiple groups, situated in a physical environment (geographic regions, buildings, transport vehicles, etc.) consisting of tools, documents, and computer systems. We are finding many useful applications of Brahms for system requirements analysis, instruction, implementing software agents, and as a workbench for relating cognitive and social theories of human behavior. Many challenges remain for representing work practices, including modeling: memory over multiple days, scheduled activities combining physical objects, groups, and locations on a timeline (such as a Space Shuttle mission), habitat vehicles with trajectories (such as the Shuttle), agent movement in 3d space (e.g., inside the International Space Station), agent posture and line of sight, coupled movements (such as carrying objects), and learning (mimicry, forming habits, detecting repetition, etc.).

Multidimensional Extension of Multiple Indicators Multiple Causes Models to Detect DIF

ERIC Educational Resources Information Center

Lee, Soo; Bulut, Okan; Suh, Youngsuk

2017-01-01

A number of studies have found multiple indicators multiple causes (MIMIC) models to be an effective tool in detecting uniform differential item functioning (DIF) for individual items and item bundles. A recently developed MIMIC-interaction model is capable of detecting both uniform and nonuniform DIF in the unidimensional item response theory…

Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application.

PubMed

Maxwell, Susan K

2010-12-01

Satellite imagery and aerial photography represent a vast resource to significantly enhance environmental mapping and modeling applications for use in understanding spatio-temporal relationships between environment and health. Deriving boundaries of land cover objects, such as trees, buildings, and crop fields, from image data has traditionally been performed manually using a very time consuming process of hand digitizing. Boundary detection algorithms are increasingly being applied using object-based image analysis (OBIA) technology to automate the process. The purpose of this paper is to present an overview and demonstrate the application of OBIA for delineating land cover features at multiple scales using a high resolution aerial photograph (1 m) and a medium resolution Landsat image (30 m) time series in the context of a pesticide spray drift exposure application. Copyright © 2010. Published by Elsevier Ltd.

Unidentified line in x-ray spectra of the Andromeda galaxy and Perseus galaxy cluster.

PubMed

Boyarsky, A; Ruchayskiy, O; Iakubovskyi, D; Franse, J

2014-12-19

We report a weak line at 3.52±0.02  keV in x-ray spectra of the Andromeda galaxy and the Perseus galaxy cluster observed by the metal-oxide-silicon (MOS) and p-n (PN) CCD cameras of the XMM-Newton telescope. This line is not known as an atomic line in the spectra of galaxies or clusters. It becomes stronger towards the centers of the objects; is stronger for Perseus than for M31; is absent in the spectrum of a deep "blank sky" data set. Although for each object it is hard to exclude that the feature is due to an instrumental effect or an atomic line, it is consistent with the behavior of a dark matter decay line. Future (non-)detections of this line in multiple objects may help to reveal its nature.

Accurate metacognition for visual sensory memory representations.

PubMed

Vandenbroucke, Annelinde R E; Sligte, Ilja G; Barrett, Adam B; Seth, Anil K; Fahrenfort, Johannes J; Lamme, Victor A F

2014-04-01

The capacity to attend to multiple objects in the visual field is limited. However, introspectively, people feel that they see the whole visual world at once. Some scholars suggest that this introspective feeling is based on short-lived sensory memory representations, whereas others argue that the feeling of seeing more than can be attended to is illusory. Here, we investigated this phenomenon by combining objective memory performance with subjective confidence ratings during a change-detection task. This allowed us to compute a measure of metacognition--the degree of knowledge that subjects have about the correctness of their decisions--for different stages of memory. We show that subjects store more objects in sensory memory than they can attend to but, at the same time, have similar metacognition for sensory memory and working memory representations. This suggests that these subjective impressions are not an illusion but accurate reflections of the richness of visual perception.

2004 EW95: A Phyllosilicate-bearing Carbonaceous Asteroid in the Kuiper Belt

NASA Astrophysics Data System (ADS)

Seccull, Tom; Fraser, Wesley C.; Puzia, Thomas H.; Brown, Michael E.; Schönebeck, Frederik

2018-03-01

Models of the Solar System’s dynamical evolution predict the dispersal of primitive planetesimals from their formative regions among the gas-giant planets due to the early phases of planetary migration. Consequently, carbonaceous objects were scattered both into the outer asteroid belt and out to the Kuiper Belt. These models predict that the Kuiper Belt should contain a small fraction of objects with carbonaceous surfaces, though to date, all reported visible reflectance spectra of small Kuiper Belt Objects (KBOs) are linear and featureless. We report the unusual reflectance spectrum of a small KBO, (120216) 2004 EW95, exhibiting a large drop in its near-UV reflectance and a broad shallow optical absorption feature centered at ∼700 nm, which is detected at greater than 4σ significance. These features, confirmed through multiple epochs of spectral photometry and spectroscopy, have respectively been associated with ferric oxides and phyllosilicates. The spectrum bears striking resemblance to those of some C-type asteroids, suggesting that 2004 EW95 may share a common origin with those objects. 2004 EW95 orbits the Sun in a stable mean motion resonance with Neptune, at relatively high eccentricity and inclination, suggesting it may have been emplaced there by some past dynamical instability. These results appear consistent with the aforementioned model predictions and are the first to show a reliably confirmed detection of silicate material on a small KBO.

SDSS-IV MaNGA: the spectroscopic discovery of strongly lensed galaxies

NASA Astrophysics Data System (ADS)

Talbot, Michael S.; Brownstein, Joel R.; Bolton, Adam S.; Bundy, Kevin; Andrews, Brett H.; Cherinka, Brian; Collett, Thomas E.; More, Anupreeta; More, Surhud; Sonnenfeld, Alessandro; Vegetti, Simona; Wake, David A.; Weijmans, Anne-Marie; Westfall, Kyle B.

2018-06-01

We present a catalogue of 38 spectroscopically detected strong galaxy-galaxy gravitational lens candidates identified in the Sloan Digital Sky Survey IV (SDSS-IV). We were able to simulate narrow-band images for eight of them demonstrating evidence of multiple images. Two of our systems are compound lens candidates, each with two background source-planes. One of these compound systems shows clear lensing features in the narrow-band image. Our sample is based on 2812 galaxies observed by the Mapping Nearby Galaxies at APO (MaNGA) integral field unit (IFU). This Spectroscopic Identification of Lensing Objects (SILO) survey extends the methodology of the Sloan Lens ACS Survey (SLACS) and BOSS Emission-Line Survey (BELLS) to lower redshift and multiple IFU spectra. We searched ˜1.5 million spectra, of which 3065 contained multiple high signal-to-noise ratio background emission-lines or a resolved [O II] doublet, that are included in this catalogue. Upon manual inspection, we discovered regions with multiple spectra containing background emission-lines at the same redshift, providing evidence of a common source-plane geometry which was not possible in previous SLACS and BELLS discovery programs. We estimate more than half of our candidates have an Einstein radius ≳ 1.7 arcsec, which is significantly greater than seen in SLACS and BELLS. These larger Einstein radii produce more extended images of the background galaxy increasing the probability that a background emission-line will enter one of the IFU spectroscopic fibres, making detection more likely.

Efficacy of Distortion Product Oto-Acoustic Emission (OAE)/Auditory Brainstem Evoked Response (ABR) Protocols in Universal Neonatal Hearing Screening and Detecting Hearing Loss in Children <2 Years of Age.

PubMed

Mishra, Girish; Sharma, Yojana; Mehta, Kanishk; Patel, Gunjan

2013-04-01

Deafness is commonest curable childhood handicap. Most remedies and programmes don't address this issue at childhood level leading to detrimental impact on development of newborns. Aims and objectives are (A) screen all newborns for deafness and detect prevalence of deafness in children less than 2 years of age. and (B) assess efficacy of multi-staged OAE/ABR protocol for hearing screening. Non-randomized, prospective study from August 2008 to August 2011. All infants underwent a series of oto-acoustic emission (OAE) and final confirmatory auditory brainstem evoked response (ABR) audiometry. Finally, out of 1,101 children, 1,069 children passed the test while 12 children had impaired hearing after final testing, confirmed by ABR. Positive predictive value of OAE after multiple test increased to 100 %. OAE-ABR test series is effective in screening neonates and multiple tests reduce economic burden. High risk screening will miss nearly 50 % deaf children, thus universal screening is indispensable in picking early deafness.

Under-sampling in a Multiple-Channel Laser Vibrometry System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corey, Jordan

2007-03-01

Laser vibrometry is a technique used to detect vibrations on objects using the interference of coherent light with itself. Most vibrometry systems process only one target location at a time, but processing multiple locations simultaneously provides improved detection capabilities. Traditional laser vibrometry systems employ oversampling to sample the incoming modulated-light signal, however as the number of channels increases in these systems, certain issues arise such a higher computational cost, excessive heat, increased power requirements, and increased component cost. This thesis describes a novel approach to laser vibrometry that utilizes undersampling to control the undesirable issues associated with over-sampled systems. Undersamplingmore » allows for significantly less samples to represent the modulated-light signals, which offers several advantages in the overall system design. These advantages include an improvement in thermal efficiency, lower processing requirements, and a higher immunity to the relative intensity noise inherent in laser vibrometry applications. A unique feature of this implementation is the use of a parallel architecture to increase the overall system throughput. This parallelism is realized using a hierarchical multi-channel architecture based on off-the-shelf programmable logic devices (PLDs).« less

NGC 6362: THE LEAST MASSIVE GLOBULAR CLUSTER WITH CHEMICALLY DISTINCT MULTIPLE POPULATIONS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mucciarelli, Alessio; Dalessandro, Emanuele; Ferraro, Francesco R.

2016-06-20

We present the first measure of Fe and Na abundances in NGC 6362, a low-mass globular cluster (GC) where first- and second-generation stars are fully spatially mixed. A total of 160 member stars (along the red giant branch (RGB) and the red horizontal branch (RHB)) were observed with the multi-object spectrograph FLAMES at the Very Large Telescope. We find that the cluster has an iron abundance of [Fe/H] = −1.09 ± 0.01 dex, without evidence of intrinsic dispersion. On the other hand, the [Na/Fe] distribution turns out to be intrinsically broad and bimodal. The Na-poor and Na-rich stars populate, respectively,more » the bluest and the reddest RGBs detected in the color–magnitude diagrams including the U filter. The RGB is composed of a mixture of first- and second-generation stars in a similar proportion, while almost all the RHB stars belong to the first cluster generation. To date, NGC 6362 is the least massive GC where both the photometric and spectroscopic signatures of multiple populations have been detected.« less

The Clinical Relevance of Force Platform Measures in Multiple Sclerosis: A Review

PubMed Central

Prosperini, Luca; Pozzilli, Carlo

2013-01-01

Balance impairment and falls are frequent in patients with multiple sclerosis (PwMS), and they may occur even at the earliest stage of the disease and in minimally impaired patients. The introduction of computer-based force platform measures (i.e., static and dynamic posturography) has provided an objective and sensitive tool to document both deficits and improvements in balance. By using more challenging test conditions, force platform measures can also reveal subtle balance disorders undetectable by common clinical scales. Furthermore, posturographic techniques may also allow to reliably identify PwMS who are at risk of accidental falls. Although force platform measures offer several theoretical advantages, only few studies extensively investigated their role in better managing PwMS. Standardised procedures, as well as clinical relevance of changes detected by static or dynamic posturography, are still lacking. In this review, we summarized studies which investigated balance deficit by means of force platform measures, focusing on their ability in detecting patients at high risk of falls and in estimating rehabilitation-induced changes, highlighting the pros and the cons with respect to clinical scales. PMID:23766910

SETI Observations of Exoplanets with the Allen Telescope Array

NASA Astrophysics Data System (ADS)

Harp, G. R.; Richards, Jon; Tarter, Jill C.; Dreher, John; Jordan, Jane; Shostak, Seth; Smolek, Ken; Kilsdonk, Tom; Wilcox, Bethany R.; Wimberly, M. K. R.; Ross, John; Barott, W. C.; Ackermann, R. F.; Blair, Samantha

2016-12-01

We report radio SETI observations on a large number of known exoplanets and other nearby star systems using the Allen Telescope Array (ATA). Observations were made over about 19000 hr from 2009 May to 2015 December. This search focused on narrowband radio signals from a set totaling 9293 stars, including 2015 exoplanet stars and Kepler objects of interest and an additional 65 whose planets may be close to their habitable zones. The ATA observations were made using multiple synthesized beams and an anticoincidence filter to help identify terrestrial radio interference. Stars were observed over frequencies from 1 to 9 GHz in multiple bands that avoid strong terrestrial communication frequencies. Data were processed in near-real time for narrowband (0.7-100 Hz) continuous and pulsed signals with transmitter/receiver relative accelerations from -0.3 to 0.3 m s-2. A total of 1.9 × 108 unique signals requiring immediate follow-up were detected in observations covering more than 8 × 106 star-MHz. We detected no persistent signals from extraterrestrial technology exceeding our frequency-dependent sensitivity threshold of 180-310 × 10-26 W m-2.

Coronary artery stenosis detection with holographic display of 3D angiograms

NASA Astrophysics Data System (ADS)

Ritman, Erik L.; Schwanke, Todd D.; Simari, Robert D.; Schwartz, Robert S.; Thomas, Paul J.

1995-05-01

The objective of this study was to establish the accuracy of an holographic display approach for detection of stenoses in coronary arteries. The rationale for using an holographic display approach is that multiple angles of view of the coronary arteriogram are provided by a single 'x-ray'-like film, backlit by a special light box. This should be more convenient in that the viewer does not have to page back and forth through a cine angiogram to obtain the multiple angles of view. The method used to test this technique involved viewing 100 3D coronary angiograms. These images were generated from the 3D angiographic images of nine normal coronary arterial trees generated with the Dynamic Spatial Reconstructor (DSR) fast CT scanner. Using our image processing programs, the image of the coronary artery lumen was locally 'narrowed' by an amount and length and at a location determined by a random look-up table. Two independent, blinded, experienced angiographers viewed the holographic displays of these angiograms and recorded their confidence about the presence, location, and severity of the stenoses. This procedure evaluates the sensitivity and specificity of the detection of coronary artery stenoses as a function of the severity, size, and location along the arteries.

THE 70 MONTH SWIFT-BAT ALL-SKY HARD X-RAY SURVEY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baumgartner, W. H.; Tueller, J.; Markwardt, C. B.

2013-08-15

We present the catalog of sources detected in 70 months of observations with the Burst Alert Telescope (BAT) hard X-ray detector on the Swift gamma-ray burst observatory. The Swift-BAT 70 month survey has detected 1171 hard X-ray sources (more than twice as many sources as the previous 22 month survey) in the 14-195 keV band down to a significance level of 4.8{sigma}, associated with 1210 counterparts. The 70 month Swift-BAT survey is the most sensitive and uniform hard X-ray all-sky survey and reaches a flux level of 1.03 Multiplication-Sign 10{sup -11} erg s{sup -1} cm{sup -2} over 50% of themore » sky and 1.34 Multiplication-Sign 10{sup -11} erg s{sup -1} cm{sup -2} over 90% of the sky. The majority of new sources in the 70 month survey continue to be active galactic nuclei, with over 700 in the catalog. As part of this new edition of the Swift-BAT catalog, we also make available eight-channel spectra and monthly sampled light curves for each object detected in the survey in the online journal and at the Swift-BAT 70 month Web site.« less

Estimation of Confidence Intervals for Multiplication and Efficiency

DOE Office of Scientific and Technical Information (OSTI.GOV)

Verbeke, J

2009-07-17

Helium-3 tubes are used to detect thermal neutrons by charge collection using the {sup 3}He(n,p) reaction. By analyzing the time sequence of neutrons detected by these tubes, one can determine important features about the constitution of a measured object: Some materials such as Cf-252 emit several neutrons simultaneously, while others such as uranium and plutonium isotopes multiply the number of neutrons to form bursts. This translates into unmistakable signatures. To determine the type of materials measured, one compares the measured count distribution with the one generated by a theoretical fission chain model. When the neutron background is negligible, the theoreticalmore » count distributions can be completely characterized by a pair of parameters, the multiplication M and the detection efficiency {var_epsilon}. While the optimal pair of M and {var_epsilon} can be determined by existing codes such as BigFit, the uncertainty on these parameters has not yet been fully studied. The purpose of this work is to precisely compute the uncertainties on the parameters M and {var_epsilon}, given the uncertainties in the count distribution. By considering different lengths of time tagged data, we will determine how the uncertainties on M and {var_epsilon} vary with the different count distributions.« less

Structural Health Monitoring and Impact Detection Using Neural Networks for Damage Characterization

NASA Technical Reports Server (NTRS)

Ross, Richard W.

2006-01-01

Detection of damage due to foreign object impact is an important factor in the development of new aerospace vehicles. Acoustic waves generated on impact can be detected using a set of piezoelectric transducers, and the location of impact can be determined by triangulation based on the differences in the arrival time of the waves at each of the sensors. These sensors generate electrical signals in response to mechanical motion resulting from the impact as well as from natural vibrations. Due to electrical noise and mechanical vibration, accurately determining these time differentials can be challenging, and even small measurement inaccuracies can lead to significant errors in the computed damage location. Wavelet transforms are used to analyze the signals at multiple levels of detail, allowing the signals resulting from the impact to be isolated from ambient electromechanical noise. Data extracted from these transformed signals are input to an artificial neural network to aid in identifying the moment of impact from the transformed signals. By distinguishing which of the signal components are resultant from the impact and which are characteristic of noise and normal aerodynamic loads, the time differentials as well as the location of damage can be accurately assessed. The combination of wavelet transformations and neural network processing results in an efficient and accurate approach for passive in-flight detection of foreign object damage.

Delving into α-stable distribution in noise suppression for seizure detection from scalp EEG

NASA Astrophysics Data System (ADS)

Wang, Yueming; Qi, Yu; Wang, Yiwen; Lei, Zhen; Zheng, Xiaoxiang; Pan, Gang

2016-10-01

Objective. There is serious noise in EEG caused by eye blink and muscle activities. The noise exhibits similar morphologies to epileptic seizure signals, leading to relatively high false alarms in most existing seizure detection methods. The objective in this paper is to develop an effective noise suppression method in seizure detection and explore the reason why it works. Approach. Based on a state-space model containing a non-linear observation function and multiple features as the observations, this paper delves deeply into the effect of the α-stable distribution in the noise suppression for seizure detection from scalp EEG. Compared with the Gaussian distribution, the α-stable distribution is asymmetric and has relatively heavy tails. These properties make it more powerful in modeling impulsive noise in EEG, which usually can not be handled by the Gaussian distribution. Specially, we give a detailed analysis in the state estimation process to show the reason why the α-stable distribution can suppress the impulsive noise. Main results. To justify each component in our model, we compare our method with 4 different models with different settings on a collected 331-hour epileptic EEG data. To show the superiority of our method, we compare it with the existing approaches on both our 331-hour data and 892-hour public data. The results demonstrate that our method is most effective in both the detection rate and the false alarm. Significance. This is the first attempt to incorporate the α-stable distribution to a state-space model for noise suppression in seizure detection and achieves the state-of-the-art performance.

An AdaBoost Based Approach to Automatic Classification and Detection of Buildings Footprints, Vegetation Areas and Roads from Satellite Images

NASA Astrophysics Data System (ADS)

Gonulalan, Cansu

In recent years, there has been an increasing demand for applications to monitor the targets related to land-use, using remote sensing images. Advances in remote sensing satellites give rise to the research in this area. Many applications ranging from urban growth planning to homeland security have already used the algorithms for automated object recognition from remote sensing imagery. However, they have still problems such as low accuracy on detection of targets, specific algorithms for a specific area etc. In this thesis, we focus on an automatic approach to classify and detect building foot-prints, road networks and vegetation areas. The automatic interpretation of visual data is a comprehensive task in computer vision field. The machine learning approaches improve the capability of classification in an intelligent way. We propose a method, which has high accuracy on detection and classification. The multi class classification is developed for detecting multiple objects. We present an AdaBoost-based approach along with the supervised learning algorithm. The combi- nation of AdaBoost with "Attentional Cascade" is adopted from Viola and Jones [1]. This combination decreases the computation time and gives opportunity to real time applications. For the feature extraction step, our contribution is to combine Haar-like features that include corner, rectangle and Gabor. Among all features, AdaBoost selects only critical features and generates in extremely efficient cascade structured classifier. Finally, we present and evaluate our experimental results. The overall system is tested and high performance of detection is achieved. The precision rate of the final multi-class classifier is over 98%.

Nationwide Hybrid Change Detection of Buildings

NASA Astrophysics Data System (ADS)

Hron, V.; Halounova, L.

2016-06-01

The Fundamental Base of Geographic Data of the Czech Republic (hereinafter FBGD) is a national 2D geodatabase at a 1:10,000 scale with more than 100 geographic objects. This paper describes the design of the permanent updating mechanism of buildings in FBGD. The proposed procedure belongs to the category of hybrid change detection (HCD) techniques which combine pixel-based and object-based evaluation. The main sources of information for HCD are cadastral information and bi-temporal vertical digital aerial photographs. These photographs have great information potential because they contain multispectral, position and also elevation information. Elevation information represents a digital surface model (DSM) which can be obtained using the image matching technique. Pixel-based evaluation of bi-temporal DSMs enables fast localization of places with potential building changes. These coarse results are subsequently classified through the object-based image analysis (OBIA) using spectral, textural and contextual features and GIS tools. The advantage of the two-stage evaluation is the pre-selection of locations where image segmentation (a computationally demanding part of OBIA) is performed. It is not necessary to apply image segmentation to the entire scene, but only to the surroundings of detected changes, which contributes to significantly faster processing and lower hardware requirements. The created technology is based on open-source software solutions that allow easy portability on multiple computers and parallelization of processing. This leads to significant savings of financial resources which can be expended on the further development of FBGD.

A sparse representation-based approach for copy-move image forgery detection in smooth regions

NASA Astrophysics Data System (ADS)

Abdessamad, Jalila; ElAdel, Asma; Zaied, Mourad

2017-03-01

Copy-move image forgery is the act of cloning a restricted region in the image and pasting it once or multiple times within that same image. This procedure intends to cover a certain feature, probably a person or an object, in the processed image or emphasize it through duplication. Consequences of this malicious operation can be unexpectedly harmful. Hence, the present paper proposes a new approach that automatically detects Copy-move Forgery (CMF). In particular, this work broaches a widely common open issue in CMF research literature that is detecting CMF within smooth areas. Indeed, the proposed approach represents the image blocks as a sparse linear combination of pre-learned bases (a mixture of texture and color-wise small patches) which allows a robust description of smooth patches. The reported experimental results demonstrate the effectiveness of the proposed approach in identifying the forged regions in CM attacks.

NELIOTA: First temperature measurement of lunar impact flashes

NASA Astrophysics Data System (ADS)

Bonanos, A. Z.; Avdellidou, C.; Liakos, A.; Xilouris, E. M.; Dapergolas, A.; Koschny, D.; Bellas-Velidis, I.; Boumis, P.; Charmandaris, V.; Fytsilis, A.; Maroussis, A.

2018-04-01

We report the first scientific results from the NELIOTA (NEO Lunar Impacts and Optical TrAnsients) project, which has recently begun lunar monitoring observations with the 1.2-m Kryoneri telescope. NELIOTA aims to detect faint impact flashes produced by near-Earth meteoroids and asteroids and thereby help constrain the size-frequency distribution of near-Earth objects in the decimeter to meter range. The NELIOTA setup, consisting of two fast-frame cameras observing simultaneously in the R and I bands, enables - for the first time - direct analytical calculation of the flash temperatures. We present the first ten flashes detected, for which we find temperatures in the range 1600 to 3100 K, in agreement with theoretical values. Two of these flashes were detected on multiple frames in both filters and therefore yield the first measurements of the temperature drop for lunar flashes. In addition, we compute the impactor masses, which range between 100 g and 50 kg.

Apparatus and method using a holographic optical element for converting a spectral distribution to image points

NASA Technical Reports Server (NTRS)

McGill, Matthew J. (Inventor); Scott, Vibart S. (Inventor); Marzouk, Marzouk (Inventor)

2001-01-01

A holographic optical element transforms a spectral distribution of light to image points. The element comprises areas, each of which acts as a separate lens to image the light incident in its area to an image point. Each area contains the recorded hologram of a point source object. The image points can be made to lie in a line in the same focal plane so as to align with a linear array detector. A version of the element has been developed that has concentric equal areas to match the circular fringe pattern of a Fabry-Perot interferometer. The element has high transmission efficiency, and when coupled with high quantum efficiency solid state detectors, provides an efficient photon-collecting detection system. The element may be used as part of the detection system in a direct detection Doppler lidar system or multiple field of view lidar system.

Transparent, flexible surface enhanced Raman scattering substrates based on Ag-coated structured PET (polyethylene terephthalate) for in-situ detection

NASA Astrophysics Data System (ADS)

Zuo, Zewen; Zhu, Kai; Gu, Chuan; Wen, Yibing; Cui, Guanglei; Qu, Jun

2016-08-01

Transparent, flexible surface-enhanced Raman scattering (SERS) substrates were fabricated by metalization of structured polyethylene terephthalate (PET) sheets. The resultant Ag-coated structured PET SERS substrates were revealed to be highly sensitive with good reproducibility and stability, an enhancement factor of 3 × 106 was acquired, which can be attributed mainly to the presence of plentiful multiple-type hot spots within the quasi-three-dimensional surface of the structured PET obtained by oxygen plasma etching. In addition, detections of model molecules on fruit skin were also carried out, demonstrating the great potential of the Ag-coated structured PET in in-situ detection of analyte on irregular objects. Importantly, the technique used for the preparation of such substrate is completely compatible with well-established silicon device technologies, and large-area fabrication with low cost can be readily realized.

ALMA Observations of Starless Core Substructure in Ophiuchus

NASA Astrophysics Data System (ADS)

Kirk, H.; Dunham, M. M.; Di Francesco, J.; Johnstone, D.; Offner, S. S. R.; Sadavoy, S. I.; Tobin, J. J.; Arce, H. G.; Bourke, T. L.; Mairs, S.; Myers, P. C.; Pineda, J. E.; Schnee, S.; Shirley, Y. L.

2017-04-01

Compact substructure is expected to arise in a starless core as mass becomes concentrated in the central region likely to form a protostar. Additionally, multiple peaks may form if fragmentation occurs. We present Atacama Large Millimeter/submillimeter Array (ALMA) Cycle 2 observations of 60 starless and protostellar cores in the Ophiuchus molecular cloud. We detect eight compact substructures which are > 15\\prime\\prime from the nearest Spitzer young stellar object. Only one of these has strong evidence for being truly starless after considering ancillary data, e.g., from Herschel and X-ray telescopes. An additional extended emission structure has tentative evidence for starlessness. The number of our detections is consistent with estimates from a combination of synthetic observations of numerical simulations and analytical arguments. This result suggests that a similar ALMA study in the Chamaeleon I cloud, which detected no compact substructure in starless cores, may be due to the peculiar evolutionary state of cores in that cloud.

Reduced electrical bandwidth receivers for direct detection 4-ary PPM optical communication intersatellite links

NASA Technical Reports Server (NTRS)

Davidson, Frederic M.; Sun, Xiaoli

1993-01-01

One of the major sources of noise in a direct detection optical communication receiver is the shot noise due to the quantum nature of the photodetector. The shot noise is signal dependent and is neither Gaussian nor wide sense stationary. When a photomultiplier tube (PMT) or an avalanche photodiode (APD) is used, there is also a multiplicative excess noise due to the randomness of the internal photodetector gain. Generally speaking, the radio frequency (RF) communication theory cannot be applied to direct detection optical communication systems because noise in RF communication systems is usually additive and Gaussian. A receiver structure which is mathematically optimal for signal dependent shot noise is derived. Several suboptimal receiver structures are discussed and compared with the optimal receiver. The objective is to find a receiver structure which is easy to implement and gives close to optimal performance.

ICCD: interactive continuous collision detection between deformable models using connectivity-based culling.

PubMed

Tang, Min; Curtis, Sean; Yoon, Sung-Eui; Manocha, Dinesh

2009-01-01

We present an interactive algorithm for continuous collision detection between deformable models. We introduce multiple techniques to improve the culling efficiency and the overall performance of continuous collision detection. First, we present a novel formulation for continuous normal cones and use these normal cones to efficiently cull large regions of the mesh as part of self-collision tests. Second, we introduce the concept of "procedural representative triangles" to remove all redundant elementary tests between nonadjacent triangles. Finally, we exploit the mesh connectivity and introduce the concept of "orphan sets" to eliminate redundant elementary tests between adjacent triangle primitives. In practice, we can reduce the number of elementary tests by two orders of magnitude. These culling techniques have been combined with bounding volume hierarchies and can result in one order of magnitude performance improvement as compared to prior collision detection algorithms for deformable models. We highlight the performance of our algorithm on several benchmarks, including cloth simulations, N-body simulations, and breaking objects.

Multiple utility constrained multi-objective programs using Bayesian theory

NASA Astrophysics Data System (ADS)

Abbasian, Pooneh; Mahdavi-Amiri, Nezam; Fazlollahtabar, Hamed

2018-03-01

A utility function is an important tool for representing a DM's preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model.

Tracking moving targets behind a scattering medium via speckle correlation.

PubMed

Guo, Chengfei; Liu, Jietao; Wu, Tengfei; Zhu, Lei; Shao, Xiaopeng

2018-02-01

Tracking moving targets behind a scattering medium is a challenge, and it has many important applications in various fields. Owing to the multiple scattering, instead of the object image, only a random speckle pattern can be received on the camera when light is passing through highly scattering layers. Significantly, an important feature of a speckle pattern has been found, and it showed the target information can be derived from the speckle correlation. In this work, inspired by the notions used in computer vision and deformation detection, by specific simulations and experiments, we demonstrate a simple object tracking method, in which by using the speckle correlation, the movement of a hidden object can be tracked in the lateral direction and axial direction. In addition, the rotation state of the moving target can also be recognized by utilizing the autocorrelation of a speckle. This work will be beneficial for biomedical applications in the fields of quantitative analysis of the working mechanisms of a micro-object and the acquisition of dynamical information of the micro-object motion.

Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images

NASA Astrophysics Data System (ADS)

Ardila, Juan P.; Bijker, Wietske; Tolpekin, Valentyn A.; Stein, Alfred

2012-04-01

Municipalities need accurate and updated inventories of urban vegetation in order to manage green resources and estimate their return on investment in urban forestry activities. Earlier studies have shown that semi-automatic tree detection using remote sensing is a challenging task. This study aims to develop a reproducible geographic object-based image analysis (GEOBIA) methodology to locate and delineate tree crowns in urban areas using high resolution imagery. We propose a GEOBIA approach that considers the spectral, spatial and contextual characteristics of tree objects in the urban space. The study presents classification rules that exploit object features at multiple segmentation scales modifying the labeling and shape of image-objects. The GEOBIA methodology was implemented on QuickBird images acquired over the cities of Enschede and Delft (The Netherlands), resulting in an identification rate of 70% and 82% respectively. False negative errors concentrated on small trees and false positive errors in private gardens. The quality of crown boundaries was acceptable, with an overall delineation error <0.24 outside of gardens and backyards.

Object activation in semantic memory from visual multimodal feature input.

PubMed

Kraut, Michael A; Kremen, Sarah; Moo, Lauren R; Segal, Jessica B; Calhoun, Vincent; Hart, John

2002-01-01

The human brain's representation of objects has been proposed to exist as a network of coactivated neural regions present in multiple cognitive systems. However, it is not known if there is a region specific to the process of activating an integrated object representation in semantic memory from multimodal feature stimuli (e.g., picture-word). A previous study using word-word feature pairs as stimulus input showed that the left thalamus is integrally involved in object activation (Kraut, Kremen, Segal, et al., this issue). In the present study, participants were presented picture-word pairs that are features of objects, with the task being to decide if together they "activated" an object not explicitly presented (e.g., picture of a candle and the word "icing" activate the internal representation of a "cake"). For picture-word pairs that combine to elicit an object, signal change was detected in the ventral temporo-occipital regions, pre-SMA, left primary somatomotor cortex, both caudate nuclei, and the dorsal thalami bilaterally. These findings suggest that the left thalamus is engaged for either picture or word stimuli, but the right thalamus appears to be involved when picture stimuli are also presented with words in semantic object activation tasks. The somatomotor signal changes are likely secondary to activation of the semantic object representations from multimodal visual stimuli.

Multiple Chemical Sensitivity

PubMed Central

Rossi, Sabrina; Pitidis, Alessio

2018-01-01

Objective: Systematic bibliography analysis of about the last 17 years on multiple chemical sensitivity (MCS) was carried out in order to detect new diagnostic and epidemiological evidence. The MCS is a complex syndrome that manifests as a result of exposure to a low level of various common contaminants. The etiology, diagnosis, and treatment are still debated among researchers. Method: Querying PubMed, Web of Science, Scopus, Cochrane library, both using some specific MESH terms combined with MESH subheadings and through free search, even by Google. Results: The studies were analyzed by verifying 1) the typology of study design; 2) criteria for case definition; 3) presence of attendances in the emergency departments and hospital admissions, and 4) analysis of the risk factors. Outlook: With this review, we give some general considerations and hypothesis for possible future research. PMID:29111991

Systems and Methods for Imaging of Falling Objects

NASA Technical Reports Server (NTRS)

Fallgatter, Cale (Inventor); Garrett, Tim (Inventor)

2014-01-01

Imaging of falling objects is described. Multiple images of a falling object can be captured substantially simultaneously using multiple cameras located at multiple angles around the falling object. An epipolar geometry of the captured images can be determined. The images can be rectified to parallelize epipolar lines of the epipolar geometry. Correspondence points between the images can be identified. At least a portion of the falling object can be digitally reconstructed using the identified correspondence points to create a digital reconstruction.

IGRINS NEAR-IR HIGH-RESOLUTION SPECTROSCOPY OF MULTIPLE JETS AROUND LkHα 234

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oh, Heeyoung; Yuk, In-Soo; Park, Byeong-Gon

2016-02-01

We present the results of high-resolution near-IR spectroscopy toward the multiple outflows around the Herbig Be star LkHα 234 using the Immersion Grating Infrared Spectrograph. Previous studies indicate that the region around LkHα 234 is complex, with several embedded young stellar objects and the outflows associated with them. In simultaneous H- and K-band spectra from HH 167, we detected 5 [Fe ii] and 14 H{sub 2} emission lines. We revealed a new [Fe ii] jet driven by radio continuum source VLA 3B. Position–velocity diagrams of the H{sub 2} 1−0 S(1) λ2.122 μm line show multiple velocity peaks. The kinematics maymore » be explained by a geometrical bow shock model. We detected a component of H{sub 2} emission at the systemic velocity (V{sub LSR} = −10.2 km s{sup −1}) along the whole slit in all slit positions, which may arise from the ambient photodissociation region. Low-velocity gas dominates the molecular hydrogen emission from knots A and B in HH 167, which is close to the systemic velocity; [Fe ii] emission lines are detected farther from the systemic velocity, at V{sub LSR} = −100–−130 km s{sup −1}. We infer that the H{sub 2} emission arises from shocked gas entrained by a high-velocity outflow. Population diagrams of H{sub 2} lines imply that the gas is thermalized at a temperature of 2500–3000 K and the emission results from shock excitation.« less

Rapid response radiation sensors for homeland security applications

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sanjoy; Maurer, Richard; Guss, Paul

2014-09-01

The National Security Technologies, LLC, Remote Sensing Laboratory is developing a rapid response radiation detection system for homeland security field applications. The intelligence-driven system is deployed only when non-radiological information about the target is verifiable. The survey area is often limited, so the detection range is small; in most cases covering a distance of 10 meters or less suffices. Definitive response is required in no more than 3 seconds and should minimize false negative alarms, but can err on the side of positive false alarms. The detection system is rapidly reconfigurable in terms of size, shape, and outer appearance; it is a plug-and-play system. Multiple radiation detection components (viz., two or more sodium iodide scintillators) are used to independently "over-determine" the existence of the threat object. Rapid response electronic dose rate meters are also included in the equipment suite. Carefully studied threat signatures are the basis of the decision making. The use of Rad-Detect predictive modeling provides information on the nature of the threat object. Rad-Detect provides accurate dose rate from heavily shielded large sources; for example those lost in Mexico were Category 1 radiation sources (~3,000 Ci of 60Co), the most dangerous of five categories defined by the International Atomic Energy Agency. Taken out of their shielding containers, Category 1 sources can kill anyone who is exposed to them at close range for a few minutes to an hour. Whenever possible sub-second data acquisition will be attempted, and, when deployed, the system will be characterized for false alarm rates. Although the radiation detection materials selected are fast (viz., faster scintillators), their speed is secondary to sensitivity, which is of primary importance. Results from these efforts will be discussed and demonstrated.

Automatic multiple zebrafish larvae tracking in unconstrained microscopic video conditions.

PubMed

Wang, Xiaoying; Cheng, Eva; Burnett, Ian S; Huang, Yushi; Wlodkowic, Donald

2017-12-14

The accurate tracking of zebrafish larvae movement is fundamental to research in many biomedical, pharmaceutical, and behavioral science applications. However, the locomotive characteristics of zebrafish larvae are significantly different from adult zebrafish, where existing adult zebrafish tracking systems cannot reliably track zebrafish larvae. Further, the far smaller size differentiation between larvae and the container render the detection of water impurities inevitable, which further affects the tracking of zebrafish larvae or require very strict video imaging conditions that typically result in unreliable tracking results for realistic experimental conditions. This paper investigates the adaptation of advanced computer vision segmentation techniques and multiple object tracking algorithms to develop an accurate, efficient and reliable multiple zebrafish larvae tracking system. The proposed system has been tested on a set of single and multiple adult and larvae zebrafish videos in a wide variety of (complex) video conditions, including shadowing, labels, water bubbles and background artifacts. Compared with existing state-of-the-art and commercial multiple organism tracking systems, the proposed system improves the tracking accuracy by up to 31.57% in unconstrained video imaging conditions. To facilitate the evaluation on zebrafish segmentation and tracking research, a dataset with annotated ground truth is also presented. The software is also publicly accessible.

Gravitational lensing by an ensemble of isothermal galaxies

NASA Technical Reports Server (NTRS)

Katz, Neal; Paczynski, Bohdan

1987-01-01

Calculation of 28,000 models of gravitational lensing of a distant quasar by an ensemble of randomly placed galaxies, each having a singular isothermal mass distribuiton, is reported. The average surface mass density was 0.2 of the critical value in all models. It is found that the surface mass density averaged over the area of the smallest circle that encompasses the multiple images is 0.82, only slightly smaller than expected from a simple analytical model of Turner et al. (1984). The probability of getting multiple images is also as large as expected analytically. Gravitational lensing is dominated by the matter in the beam; i.e., by the beam convergence. The cases where the multiple imaging is due to asymmetry in mass distribution (i.e., due to shear) are very rare. Therefore, the observed gravitational-lens candidates for which no lensing object has been detected between the images cannot be a result of asymmetric mass distribution outside the images, at least in a model with randomly distributed galaxies. A surprisingly large number of large separations between the multiple images is found: up to 25 percent of multiple images have their angular separation 2 to 4 times larger than expected in a simple analytical model.

Automated Recognition of 3D Features in GPIR Images

NASA Technical Reports Server (NTRS)

Park, Han; Stough, Timothy; Fijany, Amir

2007-01-01

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

THE SECOND CATALOG OF ACTIVE GALACTIC NUCLEI DETECTED BY THE FERMI LARGE AREA TELESCOPE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ackermann, M.; Ajello, M.; Allafort, A.

The second catalog of active galactic nuclei (AGNs) detected by the Fermi Large Area Telescope (LAT) in two years of scientific operation is presented. The second LAT AGN catalog (2LAC) includes 1017 {gamma}-ray sources located at high Galactic latitudes (|b| > 10 Degree-Sign ) that are detected with a test statistic (TS) greater than 25 and associated statistically with AGNs. However, some of these are affected by analysis issues and some are associated with multiple AGNs. Consequently, we define a Clean Sample which includes 886 AGNs, comprising 395 BL Lacertae objects (BL Lac objects), 310 flat-spectrum radio quasars (FSRQs), 157more » candidate blazars of unknown type (i.e., with broadband blazar characteristics but with no optical spectral measurement yet), 8 misaligned AGNs, 4 narrow-line Seyfert 1 (NLS1s), 10 AGNs of other types, and 2 starburst galaxies. Where possible, the blazars have been further classified based on their spectral energy distributions (SEDs) as archival radio, optical, and X-ray data permit. While almost all FSRQs have a synchrotron-peak frequency <10{sup 14} Hz, about half of the BL Lac objects have a synchrotron-peak frequency >10{sup 15} Hz. The 2LAC represents a significant improvement relative to the first LAT AGN catalog (1LAC), with 52% more associated sources. The full characterization of the newly detected sources will require more broadband data. Various properties, such as {gamma}-ray fluxes and photon power-law spectral indices, redshifts, {gamma}-ray luminosities, variability, and archival radio luminosities and their correlations are presented and discussed for the different blazar classes. The general trends observed in 1LAC are confirmed.« less

A Mobile Service Oriented Multiple Object Tracking Augmented Reality Architecture for Education and Learning Experiences

ERIC Educational Resources Information Center

Rattanarungrot, Sasithorn; White, Martin; Newbury, Paul

2014-01-01

This paper describes the design of our service-oriented architecture to support mobile multiple object tracking augmented reality applications applied to education and learning scenarios. The architecture is composed of a mobile multiple object tracking augmented reality client, a web service framework, and dynamic content providers. Tracking of…

A New Moving Object Detection Method Based on Frame-difference and Background Subtraction

NASA Astrophysics Data System (ADS)

Guo, Jiajia; Wang, Junping; Bai, Ruixue; Zhang, Yao; Li, Yong

2017-09-01

Although many methods of moving object detection have been proposed, moving object extraction is still the core in video surveillance. However, with the complex scene in real world, false detection, missed detection and deficiencies resulting from cavities inside the body still exist. In order to solve the problem of incomplete detection for moving objects, a new moving object detection method combined an improved frame-difference and Gaussian mixture background subtraction is proposed in this paper. To make the moving object detection more complete and accurate, the image repair and morphological processing techniques which are spatial compensations are applied in the proposed method. Experimental results show that our method can effectively eliminate ghosts and noise and fill the cavities of the moving object. Compared to other four moving object detection methods which are GMM, VIBE, frame-difference and a literature's method, the proposed method improve the efficiency and accuracy of the detection.

Spatio-temporal Hotelling observer for signal detection from image sequences

PubMed Central

Caucci, Luca; Barrett, Harrison H.; Rodríguez, Jeffrey J.

2010-01-01

Detection of signals in noisy images is necessary in many applications, including astronomy and medical imaging. The optimal linear observer for performing a detection task, called the Hotelling observer in the medical literature, can be regarded as a generalization of the familiar prewhitening matched filter. Performance on the detection task is limited by randomness in the image data, which stems from randomness in the object, randomness in the imaging system, and randomness in the detector outputs due to photon and readout noise, and the Hotelling observer accounts for all of these effects in an optimal way. If multiple temporal frames of images are acquired, the resulting data set is a spatio-temporal random process, and the Hotelling observer becomes a spatio-temporal linear operator. This paper discusses the theory of the spatio-temporal Hotelling observer and estimation of the required spatio-temporal covariance matrices. It also presents a parallel implementation of the observer on a cluster of Sony PLAYSTATION 3 gaming consoles. As an example, we consider the use of the spatio-temporal Hotelling observer for exoplanet detection. PMID:19550494

Spatio-temporal Hotelling observer for signal detection from image sequences.

PubMed

Caucci, Luca; Barrett, Harrison H; Rodriguez, Jeffrey J

2009-06-22

Detection of signals in noisy images is necessary in many applications, including astronomy and medical imaging. The optimal linear observer for performing a detection task, called the Hotelling observer in the medical literature, can be regarded as a generalization of the familiar prewhitening matched filter. Performance on the detection task is limited by randomness in the image data, which stems from randomness in the object, randomness in the imaging system, and randomness in the detector outputs due to photon and readout noise, and the Hotelling observer accounts for all of these effects in an optimal way. If multiple temporal frames of images are acquired, the resulting data set is a spatio-temporal random process, and the Hotelling observer becomes a spatio-temporal linear operator. This paper discusses the theory of the spatio-temporal Hotelling observer and estimation of the required spatio-temporal covariance matrices. It also presents a parallel implementation of the observer on a cluster of Sony PLAYSTATION 3 gaming consoles. As an example, we consider the use of the spatio-temporal Hotelling observer for exoplanet detection.

A targeted change-detection procedure by combining change vector analysis and post-classification approach

NASA Astrophysics Data System (ADS)

Ye, Su; Chen, Dongmei; Yu, Jie

2016-04-01

In remote sensing, conventional supervised change-detection methods usually require effective training data for multiple change types. This paper introduces a more flexible and efficient procedure that seeks to identify only the changes that users are interested in, here after referred to as "targeted change detection". Based on a one-class classifier "Support Vector Domain Description (SVDD)", a novel algorithm named "Three-layer SVDD Fusion (TLSF)" is developed specially for targeted change detection. The proposed algorithm combines one-class classification generated from change vector maps, as well as before- and after-change images in order to get a more reliable detecting result. In addition, this paper introduces a detailed workflow for implementing this algorithm. This workflow has been applied to two case studies with different practical monitoring objectives: urban expansion and forest fire assessment. The experiment results of these two case studies show that the overall accuracy of our proposed algorithm is superior (Kappa statistics are 86.3% and 87.8% for Case 1 and 2, respectively), compared to applying SVDD to change vector analysis and post-classification comparison.

ROKU: a novel method for identification of tissue-specific genes

PubMed Central

Kadota, Koji; Ye, Jiazhen; Nakai, Yuji; Terada, Tohru; Shimizu, Kentaro

2006-01-01

Background One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. Results We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. Conclusion ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes. PMID:16764735

Offer of primary care services and detection of tuberculosis incidence in Brazil

PubMed Central

Pelissari, Daniele Maria; Bartholomay, Patricia; Jacobs, Marina Gasino; Arakaki-Sanchez, Denise; dos Anjos, Davllyn Santos Oliveira; Costa, Mara Lucia dos Santos; Cavalcanti, Pauline Cristine da Silva; Diaz-Quijano, Fredi Alexander

2018-01-01

ABSTRACT OBJECTIVE To evaluate the association between the health services offered by primary care teams and the detection of new tuberculosis cases in Brazil. METHODS This was an ecological study covering all Brazilian municipalities that registered at least one new tuberculosis case (diagnosed between 2012 to 2014 and notified in the Information System of Notifiable Diseases) and with at least one primary care team evaluated by the second cycle of the National Program for Improving Access and Quality of Primary Care (PMAQ-AB). The variables of the PMAQ-AB were classified as proximal or distal, according to their relation with the tuberculosis diagnosis. Then, they were tested hierarchically in multiple models (adjusted by States) using negative binomial regression. RESULTS An increase of 10% in the primary health care coverage was associated with a decrease of 2.24% in the tuberculosis detection rate (95%CI -3.35– -1.11). Regarding the proximal variables in relation to diagnosis, in the multiple model, the detection of tuberculosis was associated with the proportion of teams that conduct contact investigation (increase in Incidence Rate Ratio [IRR] = 2.97%, 95%CI 2.41–3.53), carry out tuberculosis active case finding (increase in IRR = 2.17%, 95%CI 1.48–2.87), and request culture for mycobacteria (increase in IRR = 1.87%, 95%CI 0.98–2.76). CONCLUSIONS The variables related to the search actions were positively associated with the detection of new tuberculosis cases, which suggests a significant contribution to the strengthening of the sensitivity of the surveillance system. On the other hand, primary care coverage was inversely associated with the tuberculosis detection rate, which could represent the overall effect of the primary care on transmission control, probably from the identification and early treatment of cases. PMID:29791528

Fusion of multiple quadratic penalty function support vector machines (QPFSVM) for automated sea mine detection and classification

NASA Astrophysics Data System (ADS)

Dobeck, Gerald J.; Cobb, J. Tory

2002-08-01

The high-resolution sonar is one of the principal sensors used by the Navy to detect and classify sea mines in minehunting operations. For such sonar systems, substantial effort has been devoted to the development of automated detection and classification (D/C) algorithms. These have been spurred by several factors including (1) aids for operators to reduce work overload, (2) more optimal use of all available data, and (3) the introduction of unmanned minehunting systems. The environments where sea mines are typically laid (harbor areas, shipping lanes, and the littorals) give rise to many false alarms caused by natural, biologic, and man-made clutter. The objective of the automated D/C algorithms is to eliminate most of these false alarms while still maintaining a very high probability of mine detection and classification (PdPc). In recent years, the benefits of fusing the outputs of multiple D/C algorithms have been studied. We refer to this as Algorithm Fusion. The results have been remarkable, including reliable robustness to new environments. The Quadratic Penalty Function Support Vector Machine (QPFSVM) algorithm to aid in the automated detection and classification of sea mines is introduced in this paper. The QPFSVM algorithm is easy to train, simple to implement, and robust to feature space dimension. Outputs of successive SVM algorithms are cascaded in stages (fused) to improve the Probability of Classification (Pc) and reduce the number of false alarms. Even though our experience has been gained in the area of sea mine detection and classification, the principles described herein are general and can be applied to fusion of any D/C problem (e.g., automated medical diagnosis or automatic target recognition for ballistic missile defense).

Detection and measurement of surface contamination by multiple antineoplastic drugs using multiplex bead assay

PubMed Central

Smith, Jerome P; Sammons, Deborah L; Robertson, Shirley A; Pretty, Jack; Debord, D Gayle; Connor, Thomas H; Snawder, John

2015-01-01

Objectives Contamination of workplace surfaces by antineoplastic drugs presents an exposure risk for healthcare workers. Traditional instrumental methods to detect contamination such as liquid chromatography-tandem mass spectrometry (LC-MS/MS) are sensitive and accurate but expensive. Since immunochemical methods may be cheaper and faster than instrumental methods, we wanted to explore their use for routine drug residue detection for preventing worker exposure. Methods In this study we examined the feasibility of using fluorescence covalent microbead immunosorbent assay (FCMIA) for simultaneous detection and semi-quantitative measurement of three antineoplastic drugs (5-fluorouracil, paclitaxel, and doxorubicin). The concentration ranges for the assay were 0–1000 ng/ml for 5-fluorouracil, 0–100 ng/ml for paclitaxel, and 0–2 ng/ml for doxorubicin. The surface sampling technique involved wiping a loaded surface with a swab wetted with wash buffer, extracting the swab in storage/blocking buffer, and measuring drugs in the extract using FCMIA. Results There was no significant cross reactivity between these drugs at the ranges studied indicated by a lack of response in the assay to cross analytes. The limit of detection (LOD) for 5-fluorouracil on the surface studied was 0.93 ng/cm2 with a limit of quantitation (LOQ) of 2.8 ng/cm2, the LOD for paclitaxel was 0.57 ng/cm2 with an LOQ of 2.06 ng/cm2, and the LOD for doxorubicin was 0.0036 ng/cm2 with an LOQ of 0.013 ng/cm2. Conclusion The use of FCMIA with a simple sampling technique has potential for low cost simultaneous detection and semi-quantitative measurement of surface contamination from multiple antineoplastic drugs. PMID:25293722

The Kinematics of Multiple-peaked Lyα Emission in Star-forming Galaxies at z ~ 2-3

NASA Astrophysics Data System (ADS)

Kulas, Kristin R.; Shapley, Alice E.; Kollmeier, Juna A.; Zheng, Zheng; Steidel, Charles C.; Hainline, Kevin N.

2012-01-01

We present new results on the Lyα emission-line kinematics of 18 z ~ 2-3 star-forming galaxies with multiple-peaked Lyα profiles. With our large spectroscopic database of UV-selected star-forming galaxies at these redshifts, we have determined that ~30% of such objects with detectable Lyα emission display multiple-peaked emission profiles. These profiles provide additional constraints on the escape of Lyα photons due to the rich velocity structure in the emergent line. Despite recent advances in modeling the escape of Lyα from star-forming galaxies at high redshifts, comparisons between models and data are often missing crucial observational information. Using Keck II NIRSPEC spectra of Hα (z ~ 2) and [O III]λ5007 (z ~ 3), we have measured accurate systemic redshifts, rest-frame optical nebular velocity dispersions, and emission-line fluxes for the objects in the sample. In addition, rest-frame UV luminosities and colors provide estimates of star formation rates and the degree of dust extinction. In concert with the profile sub-structure, these measurements provide critical constraints on the geometry and kinematics of interstellar gas in high-redshift galaxies. Accurate systemic redshifts allow us to translate the multiple-peaked Lyα profiles into velocity space, revealing that the majority (11/18) display double-peaked emission straddling the velocity-field zero point with stronger red-side emission. Interstellar absorption-line kinematics suggest the presence of large-scale outflows for the majority of objects in our sample, with an average measured interstellar absorption velocity offset of langΔv absrang = -230 km s-1. A comparison of the interstellar absorption kinematics for objects with multiple- and single-peaked Lyα profiles indicate that the multiple-peaked objects are characterized by significantly narrower absorption line widths. We compare our data with the predictions of simple models for outflowing and infalling gas distributions around high-redshift galaxies. While popular "shell" models provide a qualitative match with many of the observations of Lyα emission, we find that in detail there are important discrepancies between the models and data, as well as problems with applying the framework of an expanding thin shell of gas to explain high-redshift galaxy spectra. Our data highlight these inconsistencies, as well as illuminating critical elements for success in future models of outflow and infall in high-redshift galaxies. Based, in part, on data obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and NASA, and was made possible by the generous financial support of the W. M. Keck Foundation.

Verification of road databases using multiple road models

NASA Astrophysics Data System (ADS)

Ziems, Marcel; Rottensteiner, Franz; Heipke, Christian

2017-08-01

In this paper a new approach for automatic road database verification based on remote sensing images is presented. In contrast to existing methods, the applicability of the new approach is not restricted to specific road types, context areas or geographic regions. This is achieved by combining several state-of-the-art road detection and road verification approaches that work well under different circumstances. Each one serves as an independent module representing a unique road model and a specific processing strategy. All modules provide independent solutions for the verification problem of each road object stored in the database in form of two probability distributions, the first one for the state of a database object (correct or incorrect), and a second one for the state of the underlying road model (applicable or not applicable). In accordance with the Dempster-Shafer Theory, both distributions are mapped to a new state space comprising the classes correct, incorrect and unknown. Statistical reasoning is applied to obtain the optimal state of a road object. A comparison with state-of-the-art road detection approaches using benchmark datasets shows that in general the proposed approach provides results with larger completeness. Additional experiments reveal that based on the proposed method a highly reliable semi-automatic approach for road data base verification can be designed.

Road detection and buried object detection in elevated EO/IR imagery

NASA Astrophysics Data System (ADS)

Kennedy, Levi; Kolba, Mark P.; Walters, Joshua R.

2012-06-01

To assist the warfighter in visually identifying potentially dangerous roadside objects, the U.S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD) has developed an elevated video sensor system testbed for data collection. This system provides color and mid-wave infrared (MWIR) imagery. Signal Innovations Group (SIG) has developed an automated processing capability that detects the road within the sensor field of view and identifies potentially threatening buried objects within the detected road. The road detection algorithm leverages system metadata to project the collected imagery onto a flat ground plane, allowing for more accurate detection of the road as well as the direct specification of realistic physical constraints in the shape of the detected road. Once the road has been detected in an image frame, a buried object detection algorithm is applied to search for threatening objects within the detected road space. The buried object detection algorithm leverages textural and pixel intensity-based features to detect potential anomalies and then classifies them as threatening or non-threatening objects. Both the road detection and the buried object detection algorithms have been developed to facilitate their implementation in real-time in the NVESD system.

Cortical Circuit for Binding Object Identity and Location During Multiple-Object Tracking

PubMed Central

Nummenmaa, Lauri; Oksama, Lauri; Glerean, Erico; Hyönä, Jukka

2017-01-01

Abstract Sustained multifocal attention for moving targets requires binding object identities with their locations. The brain mechanisms of identity-location binding during attentive tracking have remained unresolved. In 2 functional magnetic resonance imaging experiments, we measured participants’ hemodynamic activity during attentive tracking of multiple objects with equivalent (multiple-object tracking) versus distinct (multiple identity tracking, MIT) identities. Task load was manipulated parametrically. Both tasks activated large frontoparietal circuits. MIT led to significantly increased activity in frontoparietal and temporal systems subserving object recognition and working memory. These effects were replicated when eye movements were prohibited. MIT was associated with significantly increased functional connectivity between lateral temporal and frontal and parietal regions. We propose that coordinated activity of this network subserves identity-location binding during attentive tracking. PMID:27913430

Statistical Track-Before-Detect Methods Applied to Faint Optical Observations of Resident Space Objects

NASA Astrophysics Data System (ADS)

Fujimoto, K.; Yanagisawa, T.; Uetsuhara, M.

Automated detection and tracking of faint objects in optical, or bearing-only, sensor imagery is a topic of immense interest in space surveillance. Robust methods in this realm will lead to better space situational awareness (SSA) while reducing the cost of sensors and optics. They are especially relevant in the search for high area-to-mass ratio (HAMR) objects, as their apparent brightness can change significantly over time. A track-before-detect (TBD) approach has been shown to be suitable for faint, low signal-to-noise ratio (SNR) images of resident space objects (RSOs). TBD does not rely upon the extraction of feature points within the image based on some thresholding criteria, but rather directly takes as input the intensity information from the image file. Not only is all of the available information from the image used, TBD avoids the computational intractability of the conventional feature-based line detection (i.e., "string of pearls") approach to track detection for low SNR data. Implementation of TBD rooted in finite set statistics (FISST) theory has been proposed recently by Vo, et al. Compared to other TBD methods applied so far to SSA, such as the stacking method or multi-pass multi-period denoising, the FISST approach is statistically rigorous and has been shown to be more computationally efficient, thus paving the path toward on-line processing. In this paper, we intend to apply a multi-Bernoulli filter to actual CCD imagery of RSOs. The multi-Bernoulli filter can explicitly account for the birth and death of multiple targets in a measurement arc. TBD is achieved via a sequential Monte Carlo implementation. Preliminary results with simulated single-target data indicate that a Bernoulli filter can successfully track and detect objects with measurement SNR as low as 2.4. Although the advent of fast-cadence scientific CMOS sensors have made the automation of faint object detection a realistic goal, it is nonetheless a difficult goal, as measurements arcs in space surveillance are often both short and sparse. FISST methodologies have been applied to the general problem of SSA by many authors, but they generally focus on tracking scenarios with long arcs or assume that line detection is tractable. We will instead focus this work on estimating sensor-level kinematics of RSOs for low SNR too-short arc observations. Once said estimate is made available, track association and simultaneous initial orbit determination may be achieved via any number of proposed solutions to the too-short arc problem, such as those incorporating the admissible region. We show that the benefit of combining FISST-based TBD with too-short arc association goes both ways; i.e., the former provides consistent statistics regarding bearing-only measurements, whereas the latter makes better use of the precise dynamical models nominally applicable to RSOs in orbit determination.

A MULTIPLICITY CENSUS OF INTERMEDIATE-MASS STARS IN SCORPIUS-CENTAURUS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Janson, Markus; Lafreniere, David; Jayawardhana, Ray

2013-08-20

Stellar multiplicity properties have been studied for the lowest and the highest stellar masses, but intermediate-mass stars from F-type to late A-type have received relatively little attention. Here, we report on a Gemini/NICI snapshot imaging survey of 138 such stars in the young Scorpius-Centaurus (Sco-Cen) region, for the purpose of studying multiplicity with sensitivity down to planetary masses at wide separations. In addition to two brown dwarfs and a companion straddling the hydrogen-burning limit which we reported previously, here we present 26 new stellar companions and determine a multiplicity fraction within 0.''1-5.''0 of 21% {+-} 4%. Depending on the adoptedmore » semimajor axis distribution, our results imply a total multiplicity in the range of {approx}60%-80%, which further supports the known trend of a smooth continuous increase in the multiplicity fraction as a function of primary stellar mass. A surprising feature in the sample is a distinct lack of nearly equal-mass binaries, for which we discuss possible reasons. The survey yielded no additional companions below or near the deuterium-burning limit, implying that their frequency at >200 AU separations is not quite as high as might be inferred from previous detections of such objects within the Sco-Cen region.« less

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure and increase the robustness of the proposed algorithm. The proposed algorithm is validated with a publicly available 10-class object detection dataset.

On the use of multi-agent systems for the monitoring of industrial systems

NASA Astrophysics Data System (ADS)

Rezki, Nafissa; Kazar, Okba; Mouss, Leila Hayet; Kahloul, Laid; Rezki, Djamil

2016-03-01

The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a multi-agent system that combines multiple intelligences such as: multivariate control charts, neural networks, Bayesian networks and expert systems has became a necessity. The proposed system is evaluated in the monitoring of the complex process Tennessee Eastman process.

Tracking multiple objects is limited only by object spacing, not by speed, time, or capacity.

PubMed

Franconeri, S L; Jonathan, S V; Scimeca, J M

2010-07-01

In dealing with a dynamic world, people have the ability to maintain selective attention on a subset of moving objects in the environment. Performance in such multiple-object tracking is limited by three primary factors-the number of objects that one can track, the speed at which one can track them, and how close together they can be. We argue that this last limit, of object spacing, is the root cause of all performance constraints in multiple-object tracking. In two experiments, we found that as long as the distribution of object spacing is held constant, tracking performance is unaffected by large changes in object speed and tracking time. These results suggest that barring object-spacing constraints, people could reliably track an unlimited number of objects as fast as they could track a single object.

AKARI North Ecliptic Pole Deep Survey. Revision of the catalogue via a new image analysis

NASA Astrophysics Data System (ADS)

Murata, K.; Matsuhara, H.; Wada, T.; Arimatsu, K.; Oi, N.; Takagi, T.; Oyabu, S.; Goto, T.; Ohyama, Y.; Malkan, M.; Pearson, C.; Małek, K.; Solarz, A.

2013-11-01

Context. We present the revised near- to mid-infrared catalogue of the AKARI North Ecliptic Pole deep survey. The survey has the unique advantage of continuous filter coverage from 2 to 24 μm over nine photometric bands, but the initial version of the survey catalogue leaves room for improvement in the image analysis stage; the original images are strongly contaminated by the behaviour of the detector and the optical system. Aims: The purpose of this study is to devise new image analysis methods and to improve the detection limit and reliability of the source extraction. Methods: We removed the scattered light and stray light from the Earth limb, and corrected for artificial patterns in the images by creating appropriate templates. We also removed any artificial sources due to bright sources by using their properties or masked them out visually. In addition, for the mid-infrared source extraction, we created detection images by stacking all six bands. This reduced the sky noise and enabled us to detect fainter sources more reliably. For the near-infrared source catalogue, we considered only objects with counterparts from ground-based catalogues to avoid fake sources. For our ground-based catalogues, we used catalogues based on the CFHT/MegaCam z' band, CFHT/WIRCam Ks band and Subaru/Scam z' band. Objects with multiple counterparts were all listed in the catalogue with a merged flag for the AKARI flux. Results: The detection limits of all mid-infrared bands were improved by ~20%, and the total number of detected objects was increased by ~2000 compared with the previous version of the catalogue; it now has 9560 objects. The 5σ detection limits in our catalogue are 11, 9, 10, 30, 34, 57, 87, 93, and 256 μJy in the N2, N3, N4, S7, S9W, S11, L15, L18W, and L24 bands, respectively. The astrometric accuracies of these band detections are 0.48, 0.52, 0.55, 0.99, 0.95, 1.1, 1.2, 1.3, and 1.6 arcsec, respectively. The false-detection rate of all nine bands was decreased to less than 0.3%. In total, 27 770 objects are listed in the catalogue, 11 349 of which have mid-infrared fluxes. The catalogue is available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/559/A132 or at the ISAS/JAXA observers page, http://www.ir.isas.jaxa.jp/ASTRO-F/Observation/

Multiview human activity recognition system based on spatiotemporal template for video surveillance system

NASA Astrophysics Data System (ADS)

Kushwaha, Alok Kumar Singh; Srivastava, Rajeev

2015-09-01

An efficient view invariant framework for the recognition of human activities from an input video sequence is presented. The proposed framework is composed of three consecutive modules: (i) detect and locate people by background subtraction, (ii) view invariant spatiotemporal template creation for different activities, (iii) and finally, template matching is performed for view invariant activity recognition. The foreground objects present in a scene are extracted using change detection and background modeling. The view invariant templates are constructed using the motion history images and object shape information for different human activities in a video sequence. For matching the spatiotemporal templates for various activities, the moment invariants and Mahalanobis distance are used. The proposed approach is tested successfully on our own viewpoint dataset, KTH action recognition dataset, i3DPost multiview dataset, MSR viewpoint action dataset, VideoWeb multiview dataset, and WVU multiview human action recognition dataset. From the experimental results and analysis over the chosen datasets, it is observed that the proposed framework is robust, flexible, and efficient with respect to multiple views activity recognition, scale, and phase variations.

Automated matching of multiple terrestrial laser scans for stem mapping without the use of artificial references

NASA Astrophysics Data System (ADS)

Liu, Jingbin; Liang, Xinlian; Hyyppä, Juha; Yu, Xiaowei; Lehtomäki, Matti; Pyörälä, Jiri; Zhu, Lingli; Wang, Yunsheng; Chen, Ruizhi

2017-04-01

Terrestrial laser scanning has been widely used to analyze the 3D structure of a forest in detail and to generate data at the level of a reference plot for forest inventories without destructive measurements. Multi-scan terrestrial laser scanning is more commonly applied to collect plot-level data so that all of the stems can be detected and analyzed. However, it is necessary to match the point clouds of multiple scans to yield a point cloud with automated processing. Mismatches between datasets will lead to errors during the processing of multi-scan data. Classic registration methods based on flat surfaces cannot be directly applied in forest environments; therefore, artificial reference objects have conventionally been used to assist with scan matching. The use of artificial references requires additional labor and expertise, as well as greatly increasing the cost. In this study, we present an automated processing method for plot-level stem mapping that matches multiple scans without artificial references. In contrast to previous studies, the registration method developed in this study exploits the natural geometric characteristics among a set of tree stems in a plot and combines the point clouds of multiple scans into a unified coordinate system. Integrating multiple scans improves the overall performance of stem mapping in terms of the correctness of tree detection, as well as the bias and the root-mean-square errors of forest attributes such as diameter at breast height and tree height. In addition, the automated processing method makes stem mapping more reliable and consistent among plots, reduces the costs associated with plot-based stem mapping, and enhances the efficiency.

Multimodal UAV detection: study of various intrusion scenarios

NASA Astrophysics Data System (ADS)

Hengy, Sebastien; Laurenzis, Martin; Schertzer, Stéphane; Hommes, Alexander; Kloeppel, Franck; Shoykhetbrod, Alex; Geibig, Thomas; Johannes, Winfried; Rassy, Oussama; Christnacher, Frank

2017-10-01

Small unmanned aerial vehicles (UAVs) are becoming increasingly popular and affordable the last years for professional and private consumer market, with varied capacities and performances. Recent events showed that illicit or hostile uses constitute an emergent, quickly evolutionary threat. Recent developments in UAV technologies tend to bring autonomous, highly agile and capable unmanned aerial vehicles to the market. These UAVs can be used for spying operations as well as for transporting illicit or hazardous material (smuggling, flying improvised explosive devices). The scenario of interest concerns the protection of sensitive zones against the potential threat constituted by small drones. In the recent past, field trials were carried out to investigate the detection and tracking of multiple UAV flying at low altitude. Here, we present results which were achieved using a heterogeneous sensor network consisting of acoustic antennas, small FMCW RADAR systems and optical sensors. While acoustics and RADAR was applied to monitor a wide azimuthal area (360°), optical sensors were used for sequentially identification. The localization results have been compared to the ground truth data to estimate the efficiency of each detection system. Seven-microphone acoustic arrays allow single source localization. The mean azimuth and elevation estimation error has been measured equal to 1.5 and -2.5 degrees respectively. The FMCW radar allows tracking of multiple UAVs by estimating their range, azimuth and motion speed. Both technologies can be linked to the electro-optical system for final identification of the detected object.

Medical chart validation of an algorithm for identifying multiple sclerosis relapse in healthcare claims.

PubMed

Chastek, Benjamin J; Oleen-Burkey, Merrikay; Lopez-Bresnahan, Maria V

2010-01-01

Relapse is a common measure of disease activity in relapsing-remitting multiple sclerosis (MS). The objective of this study was to test the content validity of an operational algorithm for detecting relapse in claims data. A claims-based relapse detection algorithm was tested by comparing its detection rate over a 1-year period with relapses identified based on medical chart review. According to the algorithm, MS patients in a US healthcare claims database who had either (1) a primary claim for MS during hospitalization or (2) a corticosteroid claim following a MS-related outpatient visit were designated as having a relapse. Patient charts were examined for explicit indication of relapse or care suggestive of relapse. Positive and negative predictive values were calculated. Medical charts were reviewed for 300 MS patients, half of whom had a relapse according to the algorithm. The claims-based criteria correctly classified 67.3% of patients with relapses (positive predictive value) and 70.0% of patients without relapses (negative predictive value; kappa 0.373: p < 0.001). Alternative algorithms did not improve on the predictive value of the operational algorithm. Limitations of the algorithm include lack of differentiation between relapsing-remitting MS and other types, and that it does not incorporate measures of function and disability. The claims-based algorithm appeared to successfully detect moderate-to-severe MS relapse. This validated definition can be applied to future claims-based MS studies.

Signature detection and matching for document image retrieval.

PubMed

Zhu, Guangyu; Zheng, Yefeng; Doermann, David; Jaeger, Stefan

2009-11-01

As one of the most pervasive methods of individual identification and document authentication, signatures present convincing evidence and provide an important form of indexing for effective document image processing and retrieval in a broad range of applications. However, detection and segmentation of free-form objects such as signatures from clustered background is currently an open document analysis problem. In this paper, we focus on two fundamental problems in signature-based document image retrieval. First, we propose a novel multiscale approach to jointly detecting and segmenting signatures from document images. Rather than focusing on local features that typically have large variations, our approach captures the structural saliency using a signature production model and computes the dynamic curvature of 2D contour fragments over multiple scales. This detection framework is general and computationally tractable. Second, we treat the problem of signature retrieval in the unconstrained setting of translation, scale, and rotation invariant nonrigid shape matching. We propose two novel measures of shape dissimilarity based on anisotropic scaling and registration residual error and present a supervised learning framework for combining complementary shape information from different dissimilarity metrics using LDA. We quantitatively study state-of-the-art shape representations, shape matching algorithms, measures of dissimilarity, and the use of multiple instances as query in document image retrieval. We further demonstrate our matching techniques in offline signature verification. Extensive experiments using large real-world collections of English and Arabic machine-printed and handwritten documents demonstrate the excellent performance of our approaches.

Neural-Fuzzy model Based Steel Pipeline Multiple Cracks Classification

NASA Astrophysics Data System (ADS)

Elwalwal, Hatem Mostafa; Mahzan, Shahruddin Bin Hj.; Abdalla, Ahmed N.

2017-10-01

While pipes are cheaper than other means of transportation, this cost saving comes with a major price: pipes are subject to cracks, corrosion etc., which in turn can cause leakage and environmental damage. In this paper, Neural-Fuzzy model for multiple cracks classification based on Lamb Guide Wave. Simulation results for 42 sample were collected using ANSYS software. The current research object to carry on the numerical simulation and experimental study, aiming at finding an effective way to detection and the localization of cracks and holes defects in the main body of pipeline. Considering the damage form of multiple cracks and holes which may exist in pipeline, to determine the respective position in the steel pipe. In addition, the technique used in this research a guided lamb wave based structural health monitoring method whereas piezoelectric transducers will use as exciting and receiving sensors by Pitch-Catch method. Implementation of simple learning mechanism has been developed specially for the ANN for fuzzy the system represented.

Microfabricated capillary electrophoresis chip and method for simultaneously detecting multiple redox labels

DOEpatents

Mathies, Richard A.; Singhal, Pankaj; Xie, Jin; Glazer, Alexander N.

2002-01-01

This invention relates to a microfabricated capillary electrophoresis chip for detecting multiple redox-active labels simultaneously using a matrix coding scheme and to a method of selectively labeling analytes for simultaneous electrochemical detection of multiple label-analyte conjugates after electrophoretic or chromatographic separation.

Developing Inventory and Monitoring Programs Based on Multiple Objectives

Treesearch

Daniel L. Schmoldt; David L. Peterson; David G. Silsbee

1995-01-01

Resource inventory and monitoring (I&M) programs in national parks combine multiple objectives in order to create a plan of action over a finite time horizon. Because all program activities are constrained by time and money, it is critical to plan I&M activities that make the best use of available agency resources. However, multiple objectives complicate a...

Detection of the gravitational lens magnifying a type Ia supernova.

PubMed

Quimby, Robert M; Oguri, Masamune; More, Anupreeta; More, Surhud; Moriya, Takashi J; Werner, Marcus C; Tanaka, Masayuki; Folatelli, Gaston; Bersten, Melina C; Maeda, Keiichi; Nomoto, Ken'ichi

2014-04-25

Objects of known brightness, like type Ia supernovae (SNIa), can be used to measure distances. If a massive object warps spacetime to form multiple images of a background SNIa, a direct test of cosmic expansion is also possible. However, these lensing events must first be distinguished from other rare phenomena. Recently, a supernova was found to shine much brighter than normal for its distance, which resulted in a debate: Was it a new type of superluminous supernova or a normal SNIa magnified by a hidden gravitational lens? Here, we report that a spectrum obtained after the supernova faded away shows the presence of a foreground galaxy-the first found to strongly magnify a SNIa. We discuss how more lensed SNIa can be found than previously predicted.

Stability of Spinal Bone Lesions in Patients With Multiple Myeloma After Radiotherapy-A Retrospective Analysis of 130 Cases.

PubMed

Lang, Kristin; König, Laila; Bruckner, Thomas; Förster, Robert; Sprave, Tanja; Schlampp, Ingmar; Bostel, Tilman; Welte, Stefan; Nicolay, Nils H; Debus, Jürgen; Rief, Harald

2017-12-01

The objective of the present retrospective analysis was the response evaluation regarding bone density and stability of patients with osteolytic spinal bone lesions due to multiple myeloma after palliative radiotherapy (RT). Patients with multiple myeloma who had undergone spinal RT from March 2003 to May 2016 were analyzed before and 3 and 6 months after RT. Assessment of spinal stability and bone density was performed using the internationally recognized Taneichi scoring system and measurement of bone density using computed tomography imaging-based Hounsfield units. For statistical analysis, we used the Bowker test, McNemar test, and κ statistics to detect possible asymmetries in the distribution of the Taneichi score over time. We used the Student t test for comparison of the density values (Hounsfield units) before and after treatment. Toxicity was evaluated using the Common Terminology Criteria for Adverse Events, version 4.0. Additionally, overall survival was calculated using the Kaplan-Meier method. We evaluated 130 patients (69% male; 31% female) with multiple myeloma and a median age of 58 years. The median follow-up period was 41 months. Before treatment, 51% of the lesions were classified as unstable. At 3 and 6 months after RT, this rate had decreased to 41% (P = .0047) and 24% (P = .2393), respectively. The computed tomography measurements showed a significant increase in bone density at 3 and 6 months after RT. Acute RT-related grade 1 and 2 complications were detected in 34% of patients. Late side effects (grade 1-2) were detected in 23% of the patients. No severe grade 3 or 4 acute or late toxicities were identified. The median overall survival was 19.7 months for all patients and 6.6 months for patients with a Karnofsky performance score of ≤ 70%. To the best of our knowledge, ours is the first report to analyze the bone density and stability in patients with multiple myeloma after RT using a validated scoring system and computed tomography imaging. Palliative RT is an effective method resulting in a significant increase in bone density for local response and stability without severe RT-related toxicity. Furthermore, recalcification could already be detected at 3 months after treatment. Copyright © 2017 Elsevier Inc. All rights reserved.

Object memory and change detection: dissociation as a function of visual and conceptual similarity.

PubMed

Yeh, Yei-Yu; Yang, Cheng-Ta

2008-01-01

People often fail to detect a change between two visual scenes, a phenomenon referred to as change blindness. This study investigates how a post-change object's similarity to the pre-change object influences memory of the pre-change object and affects change detection. The results of Experiment 1 showed that similarity lowered detection sensitivity but did not affect the speed of identifying the pre-change object, suggesting that similarity between the pre- and post-change objects does not degrade the pre-change representation. Identification speed for the pre-change object was faster than naming the new object regardless of detection accuracy. Similarity also decreased detection sensitivity in Experiment 2 but improved the recognition of the pre-change object under both correct detection and detection failure. The similarity effect on recognition was greatly reduced when 20% of each pre-change stimulus was masked by random dots in Experiment 3. Together the results suggest that the level of pre-change representation under detection failure is equivalent to the level under correct detection and that the pre-change representation is almost complete. Similarity lowers detection sensitivity but improves explicit access in recognition. Dissociation arises between recognition and change detection as the two judgments rely on the match-to-mismatch signal and mismatch-to-match signal, respectively.

Handheld microwave bomb-detecting imaging system

NASA Astrophysics Data System (ADS)

Gorwara, Ashok; Molchanov, Pavlo

2017-05-01

Proposed novel imaging technique will provide all weather high-resolution imaging and recognition capability for RF/Microwave signals with good penetration through highly scattered media: fog, snow, dust, smoke, even foliage, camouflage, walls and ground. Image resolution in proposed imaging system is not limited by diffraction and will be determined by processor and sampling frequency. Proposed imaging system can simultaneously cover wide field of view, detect multiple targets and can be multi-frequency, multi-function. Directional antennas in imaging system can be close positioned and installed in cell phone size handheld device, on small aircraft or distributed around protected border or object. Non-scanning monopulse system allows dramatically decrease in transmitting power and at the same time provides increased imaging range by integrating 2-3 orders more signals than regular scanning imaging systems.

Incidents Prediction in Road Junctions Using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Hajji, Tarik; Alami Hassani, Aicha; Ouazzani Jamil, Mohammed

2018-05-01

The implementation of an incident detection system (IDS) is an indispensable operation in the analysis of the road traffics. However the IDS may, in no case, represent an alternative to the classical monitoring system controlled by the human eye. The aim of this work is to increase detection and prediction probability of incidents in camera-monitored areas. Knowing that, these areas are monitored by multiple cameras and few supervisors. Our solution is to use Artificial Neural Networks (ANN) to analyze moving objects trajectories on captured images. We first propose a modelling of the trajectories and their characteristics, after we develop a learning database for valid and invalid trajectories, and then we carry out a comparative study to find the artificial neural network architecture that maximizes the rate of valid and invalid trajectories recognition.

Data Fusion for a Vision-Radiological System for Source Tracking and Discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Enqvist, Andreas; Koppal, Sanjeev

2015-07-01

A multidisciplinary approach to allow the tracking of the movement of radioactive sources by fusing data from multiple radiological and visual sensors is under development. The goal is to improve the ability to detect, locate, track and identify nuclear/radiological threats. The key concept is that such widely available visual and depth sensors can impact radiological detection, since the intensity fall-off in the count rate can be correlated to movement in three dimensions. To enable this, we pose an important question; what is the right combination of sensing modalities and vision algorithms that can best compliment a radiological sensor, for themore » purpose of detection and tracking of radioactive material? Similarly what is the best radiation detection methods and unfolding algorithms suited for data fusion with tracking data? Data fusion of multi-sensor data for radiation detection have seen some interesting developments lately. Significant examples include intelligent radiation sensor systems (IRSS), which are based on larger numbers of distributed similar or identical radiation sensors coupled with position data for network capable to detect and locate radiation source. Other developments are gamma-ray imaging systems based on Compton scatter in segmented detector arrays. Similar developments using coded apertures or scatter cameras for neutrons have recently occurred. The main limitation of such systems is not so much in their capability but rather in their complexity and cost which is prohibitive for large scale deployment. Presented here is a fusion system based on simple, low-cost computer vision and radiological sensors for tracking of multiple objects and identifying potential radiological materials being transported or shipped. The main focus of this work is the development on two separate calibration algorithms for characterizing the fused sensor system. The deviation from a simple inverse square-root fall-off of radiation intensity is explored and accounted for. In particular, the computer vision system enables a map of distance-dependence of the sources being tracked. Infrared, laser or stereoscopic vision sensors are all options for computer-vision implementation depending on interior vs exterior deployment, resolution desired and other factors. Similarly the radiation sensors will be focused on gamma-ray or neutron detection due to the long travel length and ability to penetrate even moderate shielding. There is a significant difference between the vision sensors and radiation sensors in the way the 'source' or signals are generated. A vision sensor needs an external light-source to illuminate the object and then detects the re-emitted illumination (or lack thereof). However, for a radiation detector, the radioactive material is the source itself. The only exception to this is the field of active interrogations where radiation is beamed into a material to entice new/additional radiation emission beyond what the material would emit spontaneously. The aspect of the nuclear material being the source itself means that all other objects in the environment are 'illuminated' or irradiated by the source. Most radiation will readily penetrate regular material, scatter in new directions or be absorbed. Thus if a radiation source is located near a larger object that object will in turn scatter some radiation that was initially emitted in a direction other than the direction of the radiation detector, this can add to the count rate that is observed. The effect of these scatter is a deviation from the traditional distance dependence of the radiation signal and is a key challenge that needs a combined system calibration solution and algorithms. Thus both an algebraic approach as well as a statistical approach have been developed and independently evaluated to investigate the sensitivity to this deviation from the simplified radiation fall-off as a function of distance. The resulting calibrated system algorithms are used and demonstrated in various laboratory scenarios, and later in realistic tracking scenarios. The selection and testing of radiological and computer-vision sensors for the additional specific scenarios will be the subject of ongoing and future work. (authors)« less

Multi-scale occupancy estimation and modelling using multiple detection methods

USGS Publications Warehouse

Nichols, James D.; Bailey, Larissa L.; O'Connell, Allan F.; Talancy, Neil W.; Grant, Evan H. Campbell; Gilbert, Andrew T.; Annand, Elizabeth M.; Husband, Thomas P.; Hines, James E.

2008-01-01

Occupancy estimation and modelling based on detection–nondetection data provide an effective way of exploring change in a species’ distribution across time and space in cases where the species is not always detected with certainty. Today, many monitoring programmes target multiple species, or life stages within a species, requiring the use of multiple detection methods. When multiple methods or devices are used at the same sample sites, animals can be detected by more than one method.We develop occupancy models for multiple detection methods that permit simultaneous use of data from all methods for inference about method-specific detection probabilities. Moreover, the approach permits estimation of occupancy at two spatial scales: the larger scale corresponds to species’ use of a sample unit, whereas the smaller scale corresponds to presence of the species at the local sample station or site.We apply the models to data collected on two different vertebrate species: striped skunks Mephitis mephitis and red salamanders Pseudotriton ruber. For striped skunks, large-scale occupancy estimates were consistent between two sampling seasons. Small-scale occupancy probabilities were slightly lower in the late winter/spring when skunks tend to conserve energy, and movements are limited to males in search of females for breeding. There was strong evidence of method-specific detection probabilities for skunks. As anticipated, large- and small-scale occupancy areas completely overlapped for red salamanders. The analyses provided weak evidence of method-specific detection probabilities for this species.Synthesis and applications. Increasingly, many studies are utilizing multiple detection methods at sampling locations. The modelling approach presented here makes efficient use of detections from multiple methods to estimate occupancy probabilities at two spatial scales and to compare detection probabilities associated with different detection methods. The models can be viewed as another variation of Pollock's robust design and may be applicable to a wide variety of scenarios where species occur in an area but are not always near the sampled locations. The estimation approach is likely to be especially useful in multispecies conservation programmes by providing efficient estimates using multiple detection devices and by providing device-specific detection probability estimates for use in survey design.

On the detection of other planetary systems by astrometric techniques

NASA Technical Reports Server (NTRS)

Black, D. C.; Scargle, J. D.

1982-01-01

A quantitative method for astrometrically detecting perturbations induced in a star's motion by the presence of a planetary object is described. A periodogram is defined, wherein signals observed from a star show exactly periodic variations, which can be extracted from observational data using purely statistical methods. A detection threshold is defined for the frequency of occurrence of some detectable signal, e.g., the Nyquist frequency. Possible effects of a stellar orbital eccentricity and multiple companions are discussed, noting that assumption of a circular orbit assures the spectral purity of the signal described. The periodogram technique was applied to 12 yr of astrometric data from the U.S. Naval Observatory for three stars with low mass stellar companions. Periodic perturbations were confirmed. A comparison of the accuracy of different astrometric systems shows that the detection accuracy of a system is determined by the measurement accuracy and the number of observations, although the detection efficiency can be maximized by minimizing the number of data points for the case when observational errors are proportional to the square root of the number of data points. It is suggested that a space-based astrometric telescope is best suited to take advantage of the method.

Incorporation of operator knowledge for improved HMDS GPR classification

NASA Astrophysics Data System (ADS)

Kennedy, Levi; McClelland, Jessee R.; Walters, Joshua R.

2012-06-01

The Husky Mine Detection System (HMDS) detects and alerts operators to potential threats observed in groundpenetrating RADAR (GPR) data. In the current system architecture, the classifiers have been trained using available data from multiple training sites. Changes in target types, clutter types, and operational conditions may result in statistical differences between the training data and the testing data for the underlying features used by the classifier, potentially resulting in an increased false alarm rate or a lower probability of detection for the system. In the current mode of operation, the automated detection system alerts the human operator when a target-like object is detected. The operator then uses data visualization software, contextual information, and human intuition to decide whether the alarm presented is an actual target or a false alarm. When the statistics of the training data and the testing data are mismatched, the automated detection system can overwhelm the analyst with an excessive number of false alarms. This is evident in the performance of and the data collected from deployed systems. This work demonstrates that analyst feedback can be successfully used to re-train a classifier to account for variable testing data statistics not originally captured in the initial training data.

Multiple components of surround modulation in primary visual cortex: multiple neural circuits with multiple functions?

PubMed Central

Nurminen, Lauri; Angelucci, Alessandra

2014-01-01

The responses of neurons in primary visual cortex (V1) to stimulation of their receptive field (RF) are modulated by stimuli in the RF surround. This modulation is suppressive when the stimuli in the RF and surround are of similar orientation, but less suppressive or facilitatory when they are cross-oriented. Similarly, in human vision surround stimuli selectively suppress the perceived contrast of a central stimulus. Although the properties of surround modulation have been thoroughly characterized in many species, cortical areas and sensory modalities, its role in perception remains unknown. Here we argue that surround modulation in V1 consists of multiple components having different spatio-temporal and tuning properties, generated by different neural circuits and serving different visual functions. One component arises from LGN afferents, is fast, untuned for orientation, and spatially restricted to the surround region nearest to the RF (the near-surround); its function is to normalize V1 cell responses to local contrast. Intra-V1 horizontal connections contribute a slower, narrowly orientation-tuned component to near-surround modulation, whose function is to increase the coding efficiency of natural images in manner that leads to the extraction of object boundaries. The third component is generated by topdown feedback connections to V1, is fast, broadly orientation-tuned, and extends into the far-surround; its function is to enhance the salience of behaviorally relevant visual features. Far- and near-surround modulation, thus, act as parallel mechanisms: the former quickly detects and guides saccades/attention to salient visual scene locations, the latter segments object boundaries in the scene. PMID:25204770

Methods and apparatus using commutative error detection values for fault isolation in multiple node computers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Almasi, Gheorghe; Blumrich, Matthias Augustin; Chen, Dong

Methods and apparatus perform fault isolation in multiple node computing systems using commutative error detection values for--example, checksums--to identify and to isolate faulty nodes. When information associated with a reproducible portion of a computer program is injected into a network by a node, a commutative error detection value is calculated. At intervals, node fault detection apparatus associated with the multiple node computer system retrieve commutative error detection values associated with the node and stores them in memory. When the computer program is executed again by the multiple node computer system, new commutative error detection values are created and stored inmore » memory. The node fault detection apparatus identifies faulty nodes by comparing commutative error detection values associated with reproducible portions of the application program generated by a particular node from different runs of the application program. Differences in values indicate a possible faulty node.« less

Informed multi-objective decision-making in environmental management using Pareto optimality

Treesearch

Maureen C. Kennedy; E. David Ford; Peter Singleton; Mark Finney; James K. Agee

2008-01-01

Effective decisionmaking in environmental management requires the consideration of multiple objectives that may conflict. Common optimization methods use weights on the multiple objectives to aggregate them into a single value, neglecting valuable insight into the relationships among the objectives in the management problem.

Object detection in natural scenes: Independent effects of spatial and category-based attention.

PubMed

Stein, Timo; Peelen, Marius V

2017-04-01

Humans are remarkably efficient in detecting highly familiar object categories in natural scenes, with evidence suggesting that such object detection can be performed in the (near) absence of attention. Here we systematically explored the influences of both spatial attention and category-based attention on the accuracy of object detection in natural scenes. Manipulating both types of attention additionally allowed for addressing how these factors interact: whether the requirement for spatial attention depends on the extent to which observers are prepared to detect a specific object category-that is, on category-based attention. The results showed that the detection of targets from one category (animals or vehicles) was better than the detection of targets from two categories (animals and vehicles), demonstrating the beneficial effect of category-based attention. This effect did not depend on the semantic congruency of the target object and the background scene, indicating that observers attended to visual features diagnostic of the foreground target objects from the cued category. Importantly, in three experiments the detection of objects in scenes presented in the periphery was significantly impaired when observers simultaneously performed an attentionally demanding task at fixation, showing that spatial attention affects natural scene perception. In all experiments, the effects of category-based attention and spatial attention on object detection performance were additive rather than interactive. Finally, neither spatial nor category-based attention influenced metacognitive ability for object detection performance. These findings demonstrate that efficient object detection in natural scenes is independently facilitated by spatial and category-based attention.

Research on Daily Objects Detection Based on Deep Neural Network

NASA Astrophysics Data System (ADS)

Ding, Sheng; Zhao, Kun

2018-03-01

With the rapid development of deep learning, great breakthroughs have been made in the field of object detection. In this article, the deep learning algorithm is applied to the detection of daily objects, and some progress has been made in this direction. Compared with traditional object detection methods, the daily objects detection method based on deep learning is faster and more accurate. The main research work of this article: 1. collect a small data set of daily objects; 2. in the TensorFlow framework to build different models of object detection, and use this data set training model; 3. the training process and effect of the model are improved by fine-tuning the model parameters.

Simultaneous detection of multiple chemical residues in milk using broad-specifity antibodies in a hybrid immunosorbent assay

USDA-ARS?s Scientific Manuscript database

The wide array of applications using quantum dots (QDs) for detection of multiple analytes reflects the versatility of the technology. In this study, a novel immunoassay using 2 types of sensors (QDs and an enzyme) were simultaneously used for detecting multiple structurally different low-molecular...

Object Detection in Natural Backgrounds Predicted by Discrimination Performance and Models

NASA Technical Reports Server (NTRS)

Ahumada, A. J., Jr.; Watson, A. B.; Rohaly, A. M.; Null, Cynthia H. (Technical Monitor)

1995-01-01

In object detection, an observer looks for an object class member in a set of backgrounds. In discrimination, an observer tries to distinguish two images. Discrimination models predict the probability that an observer detects a difference between two images. We compare object detection and image discrimination with the same stimuli by: (1) making stimulus pairs of the same background with and without the target object and (2) either giving many consecutive trials with the same background (discrimination) or intermixing the stimuli (object detection). Six images of a vehicle in a natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Detection observers rated the images for vehicle presence. Discrimination observers rated the images for any difference from the background image. Estimated detectabilities of the vehicles were found by maximizing the likelihood of a Thurstone category scaling model. The pattern of estimated detectabilities is similar for discrimination and object detection, and is accurately predicted by a Cortex Transform discrimination model. Predictions of a Contrast- Sensitivity- Function filter model and a Root-Mean-Square difference metric based on the digital image values are less accurate. The discrimination detectabilities averaged about twice those of object detection.

Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection

DOEpatents

Paiton, Dylan M.; Kenyon, Garrett T.; Brumby, Steven P.; Schultz, Peter F.; George, John S.

2015-07-28

An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using a combinatorial algorithm.

Detecting impossible changes in infancy: a three-system account

PubMed Central

Wang, Su-hua; Baillargeon, Renée

2012-01-01

Can infants detect that an object has magically disappeared, broken apart or changed color while briefly hidden? Recent research suggests that infants detect some but not other ‘impossible’ changes; and that various contextual manipulations can induce infants to detect changes they would not otherwise detect. We present an account that includes three systems: a physical-reasoning, an object-tracking, and an object-representation system. What impossible changes infants detect depends on what object information is included in the physical-reasoning system; this information becomes subject to a principle of persistence, which states that objects can undergo no spontaneous or uncaused change. What contextual manipulations induce infants to detect impossible changes depends on complex interplays between the physical-reasoning system and the object-tracking and object-representation systems. PMID:18078778

Conception and realization of a semiconductor based 240 GHz full 3D MIMO imaging system

NASA Astrophysics Data System (ADS)

Weisenstein, Christian; Kahl, Matthias; Friederich, Fabian; Haring Bolívar, Peter

2017-02-01

Multiple-input multiple-output (MIMO) imaging systems in the terahertz frequency range have a high potential in the field of non-destructive testing (NDT). With such systems it is possible to detect defects in composite materials, for example cracks or delaminations in fiber composites. To investigate mass-produced products it is necessary to study the objects in close to real-time on a conveyor without affecting the production cycle time. In this work we present the conception and realization of a 3D MIMO imaging system for in-line investigation of composite materials and structures. To achieve a lateral resolution of 1 mm, in order to detect such small defects in composite materials with a moderate number of elements, precise sensor design is crucial. In our approach we use the effective aperture concept. The designed sparse array consists of 32 transmitters and 30 receivers based on planar semiconductor components. High range resolution is achieved by an operating frequency between 220 GHz and 260 GHz in a stepped frequency continuous wave (SFCW) setup. A matched filter approach is used to simulate the reconstructed 3D image through the array. This allows the evaluation of the designed array geometry in regard of resolution and side lobe level. In contrast to earlier demonstrations, in which synthetic reconstruction is only performed in a 2D plane, an optics-free full 3D recon- struction has been implemented in our concept. Based on this simulation we designed an array geometry that enables to resolve objects with a resolution smaller than 1mm and moderate side lobe level.

Detection of Tree Crowns Based on Reclassification Using Aerial Images and LIDAR Data

NASA Astrophysics Data System (ADS)

Talebi, S.; Zarea, A.; Sadeghian, S.; Arefi, H.

2013-09-01

Tree detection using aerial sensors in early decades was focused by many researchers in different fields including Remote Sensing and Photogrammetry. This paper is intended to detect trees in complex city areas using aerial imagery and laser scanning data. Our methodology is a hierarchal unsupervised method consists of some primitive operations. This method could be divided into three sections, in which, first section uses aerial imagery and both second and third sections use laser scanners data. In the first section a vegetation cover mask is created in both sunny and shadowed areas. In the second section Rate of Slope Change (RSC) is used to eliminate grasses. In the third section a Digital Terrain Model (DTM) is obtained from LiDAR data. By using DTM and Digital Surface Model (DSM) we would get to Normalized Digital Surface Model (nDSM). Then objects which are lower than a specific height are eliminated. Now there are three result layers from three sections. At the end multiplication operation is used to get final result layer. This layer will be smoothed by morphological operations. The result layer is sent to WG III/4 to evaluate. The evaluation result shows that our method has a good rank in comparing to other participants' methods in ISPRS WG III/4, when assessed in terms of 5 indices including area base completeness, area base correctness, object base completeness, object base correctness and boundary RMS. With regarding of being unsupervised and automatic, this method is improvable and could be integrate with other methods to get best results.

Large-scale classification of traffic signs under real-world conditions

NASA Astrophysics Data System (ADS)

Hazelhoff, Lykele; Creusen, Ivo; van de Wouw, Dennis; de With, Peter H. N.

2012-02-01

Traffic sign inventories are important to governmental agencies as they facilitate evaluation of traffic sign locations and are beneficial for road and sign maintenance. These inventories can be created (semi-)automatically based on street-level panoramic images. In these images, object detection is employed to detect the signs in each image, followed by a classification stage to retrieve the specific sign type. Classification of traffic signs is a complicated matter, since sign types are very similar with only minor differences within the sign, a high number of different signs is involved and multiple distortions occur, including variations in capturing conditions, occlusions, viewpoints and sign deformations. Therefore, we propose a method for robust classification of traffic signs, based on the Bag of Words approach for generic object classification. We extend the approach with a flexible, modular codebook to model the specific features of each sign type independently, in order to emphasize at the inter-sign differences instead of the parts common for all sign types. Additionally, this allows us to model and label the present false detections. Furthermore, analysis of the classification output provides the unreliable results. This classification system has been extensively tested for three different sign classes, covering 60 different sign types in total. These three data sets contain the sign detection results on street-level panoramic images, extracted from a country-wide database. The introduction of the modular codebook shows a significant improvement for all three sets, where the system is able to classify about 98% of the reliable results correctly.

Detection of Hydroxyapatite in Calcified Cardiovascular Tissues

PubMed Central

Lee, Jae Sam; Morrisett, Joel D.; Tung, Ching-Hsuan

2012-01-01

Objective The objective of this study is to develop a method for selective detection of the calcific (hydroxyapatite) component in human aortic smooth muscle cells in vitro and in calcified cardiovascular tissues ex vivo. This method uses a novel optical molecular imaging contrast dye, Cy-HABP-19, to target calcified cells and tissues. Methods A peptide that mimics the binding affinity of osteocalcin was used to label hydroxyapatite in vitro and ex vivo. Morphological changes in vascular smooth muscle cells were evaluated at an early stage of the mineralization process induced by extrinsic stimuli, osteogenic factors and a magnetic suspension cell culture. Hydroxyapatite components were detected in monolayers of these cells in the presence of osteogenic factors and a magnetic suspension environment. Results Atherosclerotic plaque contains multiple components including lipidic, fibrotic, thrombotic, and calcific materials. Using optical imaging and the Cy-HABP-19 molecular imaging probe, we demonstrated that hydroxyapatite components could be selectively distinguished from various calcium salts in human aortic smooth muscle cells in vitro and in calcified cardiovascular tissues, carotid endarterectomy samples and aortic valves, ex vivo. Conclusion Hydroxyapatite deposits in cardiovascular tissues were selectively detected in the early stage of the calcification process using our Cy-HABP-19 probe. This new probe makes it possible to study the earliest events associated with vascular hydroxyapatite deposition at the cellular and molecular levels. This target-selective molecular imaging probe approach holds high potential for revealing early pathophysiological changes, leading to progression, regression, or stabilization of cardiovascular diseases. PMID:22877867

IMPROVED CAPABILITIES FOR SITING WIND FARMS AND MITIGATING IMPACTS ON RADAR OBSERVATIONS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chiswell, S.

2010-01-15

The development of efficient wind energy production involves challenges in technology and interoperability with other systems critical to the national mission. Wind turbines impact radar measurements as a result of their large reflectivity cross section as well as through the Doppler phase shift of their rotating blades. Wind farms can interfere with operational radar in multiple contexts, with degradation impacts on: weather detection such as tornado location, wind shear, and precipitation monitoring; tracking of airplanes where air traffic control software can lose the tracks of aircraft; and in identification of other low flying targets where a wind farm located closemore » to a border might create a dead zone for detecting intruding objects. Objects in the path of an electromagnetic wave affect its propagation characteristics. This includes actual blockage of wave propagation by large individual objects and interference in wave continuity due to diffraction of the beam by individual or multiple objects. As an evolving industry, and the fastest growing segment of the energy sector, wind power is poised to make significant contributions in future energy generation requirements. The ability to develop comprehensive strategies for designing wind turbine locations that are mutually beneficial to both the wind industry that is dependent on production, and radar sites which the nation relies on, is critical to establishing reliable and secure wind energy. The mission needs of the Department of Homeland Security (DHS), Department of Defense (DOD), Federal Aviation Administration (FAA), and National Oceanographic and Atmospheric Administration (NOAA) dictate that the nation's radar systems remain uninhibited, to the maximum extent possible, by man-made obstructions; however, wind turbines can and do impact the surveillance footprint for monitoring airspace both for national defense as well as critical weather conditions which can impact life and property. As a result, a number of potential wind power locations have been contested on the basis of radar line of site. Radar line of site is dependent on local topography, and varies with atmospheric refractive index which is affected by weather and geographic conditions.« less

Antimicrobial residues in animal waste and water resources proximal to large-scale swine and poultry feeding operations

USGS Publications Warehouse

Campagnolo, E.R.; Johnson, K.R.; Karpati, A.; Rubin, C.S.; Kolpin, D.W.; Meyer, M.T.; Esteban, J. Emilio; Currier, R.W.; Smith, K.; Thu, K.M.; McGeehin, M.

2002-01-01

Expansion and intensification of large-scale animal feeding operations (AFOs) in the United States has resulted in concern about environmental contamination and its potential public health impacts. The objective of this investigation was to obtain background data on a broad profile of antimicrobial residues in animal wastes and surface water and groundwater proximal to large-scale swine and poultry operations. The samples were measured for antimicrobial compounds using both radioimmunoassay and liquid chromatography/electrospray ionization-mass spectrometry (LC/ESI-MS) techniques. Multiple classes of antimicrobial compounds (commonly at concentrations of >100 μg/l) were detected in swine waste storage lagoons. In addition, multiple classes of antimicrobial compounds were detected in surface and groundwater samples collected proximal to the swine and poultry farms. This information indicates that animal waste used as fertilizer for crops may serve as a source of antimicrobial residues for the environment. Further research is required to determine if the levels of antimicrobials detected in this study are of consequence to human and/or environmental ecosystems. A comparison of the radioimmunoassay and LC/ESI-MS analytical methods documented that radioimmunoassay techniques were only appropriate for measuring residues in animal waste samples likely to contain high levels of antimicrobials. More sensitive LC/ESI-MS techniques are required in environmental samples, where low levels of antimicrobial residues are more likely.

Tracker Toolkit

NASA Technical Reports Server (NTRS)

Lewis, Steven J.; Palacios, David M.

2013-01-01

This software can track multiple moving objects within a video stream simultaneously, use visual features to aid in the tracking, and initiate tracks based on object detection in a subregion. A simple programmatic interface allows plugging into larger image chain modeling suites. It extracts unique visual features for aid in tracking and later analysis, and includes sub-functionality for extracting visual features about an object identified within an image frame. Tracker Toolkit utilizes a feature extraction algorithm to tag each object with metadata features about its size, shape, color, and movement. Its functionality is independent of the scale of objects within a scene. The only assumption made on the tracked objects is that they move. There are no constraints on size within the scene, shape, or type of movement. The Tracker Toolkit is also capable of following an arbitrary number of objects in the same scene, identifying and propagating the track of each object from frame to frame. Target objects may be specified for tracking beforehand, or may be dynamically discovered within a tripwire region. Initialization of the Tracker Toolkit algorithm includes two steps: Initializing the data structures for tracked target objects, including targets preselected for tracking; and initializing the tripwire region. If no tripwire region is desired, this step is skipped. The tripwire region is an area within the frames that is always checked for new objects, and all new objects discovered within the region will be tracked until lost (by leaving the frame, stopping, or blending in to the background).

Rapid gist perception of meaningful real-life scenes: Exploring individual and gender differences in multiple categorization tasks

PubMed Central

Vanmarcke, Steven; Wagemans, Johan

2015-01-01

In everyday life, we are generally able to dynamically understand and adapt to socially (ir)elevant encounters, and to make appropriate decisions about these. All of this requires an impressive ability to directly filter and obtain the most informative aspects of a complex visual scene. Such rapid gist perception can be assessed in multiple ways. In the ultrafast categorization paradigm developed by Simon Thorpe et al. (1996), participants get a clear categorization task in advance and succeed at detecting the target object of interest (animal) almost perfectly (even with 20 ms exposures). Since this pioneering work, follow-up studies consistently reported population-level reaction time differences on different categorization tasks, indicating a superordinate advantage (animal versus dog) and effects of perceptual similarity (animals versus vehicles) and object category size (natural versus animal versus dog). In this study, we replicated and extended these separate findings by using a systematic collection of different categorization tasks (varying in presentation time, task demands, and stimuli) and focusing on individual differences in terms of e.g., gender and intelligence. In addition to replicating the main findings from the literature, we find subtle, yet consistent gender differences (women faster than men). PMID:26034569

Unifying Inference of Meso-Scale Structures in Networks.

PubMed

Tunç, Birkan; Verma, Ragini

2015-01-01

Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities) of the brain, as well as its auxiliary characteristics (core-periphery).

THE ALFALFA H I ABSORPTION PILOT SURVEY: A WIDE-AREA BLIND DAMPED Ly{alpha} SYSTEM SURVEY OF THE LOCAL UNIVERSE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Darling, Jeremy; Macdonald, Erin P.; Haynes, Martha P.

2011-11-20

We present the results of a pilot survey for neutral hydrogen (H I) 21 cm absorption in the Arecibo Legacy Fast Arecibo L-Band Feed Array (ALFALFA) Survey. This project is a wide-area 'blind' search for H I absorption in the local universe, spanning -650 km s{sup -1} < cz < 17, 500 km s{sup -1} and covering 517.0 deg{sup 2} (7% of the full ALFALFA survey). The survey is sensitive to H I absorption lines stronger than 7.7 mJy (8983 radio sources) and is 90% complete for lines stronger than 11.0 mJy (7296 sources). The total redshift interval sensitive tomore » all damped Ly{alpha} (DLA) systems (N{sub H{sub i}}{>=}2 Multiplication-Sign 10{sup 20} cm{sup -2}) is {Delta}z = 7.0 (129 objects, assuming T{sub s} = 100 K and covering fraction unity); for super-DLAs (N{sub H{sub i}}{>=}2 Multiplication-Sign 10{sup 21} cm{sup -2}) it is {Delta}z = 128.2 (2353 objects). We re-detect the intrinsic H I absorption line in UGC 6081 but detect no intervening absorption line systems. We compute a 95% confidence upper limit on the column density frequency distribution function f(N{sub H{sub i}},X) spanning four orders of magnitude in column density, 10{sup 19} (T{sub s} /100 K) (1/f) cm{sup -2}

Detection of multiple blood feeding in Aedes aegypti (Diptera: Culicidae) during a single gonotrophic cycle using a histologic technique.

PubMed

Scott, T W; Clark, G G; Lorenz, L H; Amerasinghe, P H; Reiter, P; Edman, J D

1993-01-01

We evaluated a histologic technique for its usefulness in detecting multiple blood feeding by Aedes aegypti (L.) in a single gonotrophic cycle. To standardize the procedure, we carried out a laboratory study in which 166 mosquitoes imbibed two blood meals at known intervals. Eighty percent (78/98) of the multiple meals were detected when the interval between meals was from 1 to < to = 24 h and the time from the second meal to fixation ranged from 0 to 12 hr. At intervals outside this range, only 34% (23/68) of the multiple meals were detected. Overall, 61% (101/166) of the double meals were detected. Examination of 96 engorged Ae. aegypti collected by aspiration from inside houses in San Juan, Puerto Rico, indicated that 50% had imbibed multiple meals. Most wild-caught mosquitoes took their last meal the day before capture, and most multiple feeders fed twice on consecutive days. A dark line of digested blood, or heme, around the first meal and a physical separation between meals were the most useful histologic parameters for detecting multiple feeding in wild Ae. aegypti. An association of multiple feeding with advanced stages of oocyte development suggests that, at the time of collection, most Ae. aegypti from the study site had fed twice in each gonotrophic cycle. We conclude that, although it is labor intensive, histologic examination is an appropriate technique for a longitudinal, community-wide survey of multiple feeding by Ae. aegypti.

Parallel Object Activation and Attentional Gating of Information: Evidence from Eye Movements in the Multiple Object Naming Paradigm

ERIC Educational Resources Information Center

Schotter, Elizabeth R.; Ferreira, Victor S.; Rayner, Keith

2013-01-01

Do we access information from any object we can see, or do we access information only from objects that we intend to name? In 3 experiments using a modified multiple object naming paradigm, subjects were required to name several objects in succession when previews appeared briefly and simultaneously in the same location as the target as well as at…

The relationship between change detection and recognition of centrally attended objects in motion pictures.

PubMed

Angelone, Bonnie L; Levin, Daniel T; Simons, Daniel J

2003-01-01

Observers typically detect changes to central objects more readily than changes to marginal objects, but they sometimes miss changes to central, attended objects as well. However, even if observers do not report such changes, they may be able to recognize the changed object. In three experiments we explored change detection and recognition memory for several types of changes to central objects in motion pictures. Observers who failed to detect a change still performed at above chance levels on a recognition task in almost all conditions. In addition, observers who detected the change were no more accurate in their recognition than those who did not detect the change. Despite large differences in the detectability of changes across conditions, those observers who missed the change did not vary in their ability to recognize the changing object.

Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paiton, Dylan M.; Kenyon, Garrett T.; Brumby, Steven P.

An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using amore » combinatorial algorithm.« less

Laser-ultrasound spectroscopy apparatus and method with detection of shear resonances for measuring anisotropy, thickness, and other properties

DOEpatents

Levesque, Daniel; Moreau, Andre; Dubois, Marc; Monchalin, Jean-Pierre; Bussiere, Jean; Lord, Martin; Padioleau, Christian

2000-01-01

Apparatus and method for detecting shear resonances includes structure and steps for applying a radiation pulse from a pulsed source of radiation to an object to generate elastic waves therein, optically detecting the elastic waves generated in the object, and analyzing the elastic waves optically detected in the object. These shear resonances, alone or in combination with other information, may be used in the present invention to improve thickness measurement accuracy and to determine geometrical, microstructural, and physical properties of the object. At least one shear resonance in the object is detected with the elastic waves optically detected in the object. Preferably, laser-ultrasound spectroscopy is utilized to detect the shear resonances.

Acoustic and electromagnetic wave interaction in the detection and identification of buried objects

NASA Astrophysics Data System (ADS)

Lawrence, Daniel Edward

2002-09-01

In order to facilitate the development of a hybrid acoustic and electromagnetic (EM) system for buried object detection, a number of analytical solutions and a novel numerical technique are developed to analyze the complex interaction between acoustic and EM scattering. The essence of the interaction lies in the fact that identifiable acoustic properties of an object, such as acoustic resonances, can be observed in the scattered EM Doppler spectrum. Using a perturbation approach, analytical solutions are derived for the EM scattering from infinitely long circular cylinders, both metallic and dielectric, under acoustic vibration in a homogeneous background medium. Results indicate that both the shape variation and dielectric constant contribute to the scattered EM Doppler spectrum. To model the effect of a cylinder beneath an acoustically excited half-space, a new analytical solution is presented for EM scattering from a cylinder beneath a slightly rough surface. The solution is achieved by using plane-wave expansion of the fields and an iterative technique to account for the multiple interactions between the cylinder and rough surface. Following a similar procedure, a novel solution for elastic-wave scattering from a solid cylinder embedded in a solid half-space is developed and used to calculate the surface displacement. Simulations indicate that only a finite range of spatial surface frequencies, corresponding to surface roughness on the order of the EM wavelength; affect the EM scattering from buried objects and suggest that object detection can be improved if the acoustic excitation induces surface roughness outside this range. To extend the study to non-canonical scenarios, a novel numerical approach is introduced in which time-varying impedance boundary conditions (IBCs) are used in conjunction with the method of moments (MoM) to model the EM scattering from vibrating metallic objects of arbitrary shape. It is shown that the standard IBC provides a first order solution for TM polarization, but a second order IBC is needed for TE polarization. The crucial factor in the calculation of the potentially small Doppler components is that the time-varying nature of the cylinder boundary, contained within the surface impedance expressions, can be isolated from the unperturbed terms in the scattered field.

New Hypervelocity Terminal Intercept Guidance Systems for Deflecting/Disrupting Hazardous Asteroids

NASA Astrophysics Data System (ADS)

Lyzhoft, Joshua Richard

Computational modeling and simulations of visual and infrared (IR) sensors are investigated for a new hypervelocity terminal guidance system of intercepting small asteroids (50 to 150 meters in diameter). Computational software tools for signal-to-noise ratio estimation of visual and IR sensors, estimation of minimum and maximum ranges of target detection, and GPU (Graphics Processing Units)-accelerated simulations of the IR-based terminal intercept guidance systems are developed. Scaled polyhedron models of known objects, such as the Rosetta mission's Comet 67P/C-G, NASA's OSIRIS-REx Bennu, and asteroid 433 Eros, are utilized in developing a GPU-based simulation tool for the IR-based terminal intercept guidance systems. A parallelized-ray tracing algorithm for simulating realistic surface-to-surface shadowing of irregular-shaped asteroids or comets is developed. Polyhedron solid-angle approximation is also considered. Using these computational models, digital image processing is investigated to determine single or multiple impact locations to assess the technical feasibility of new planetary defense mission concepts of utilizing a Hypervelocity Asteroid Intercept Vehicle (HAIV) or a Multiple Kinetic-energy Interceptor Vehicle (MKIV). Study results indicate that the IR-based guidance system outperforms the visual-based system in asteroid detection and tracking. When using an IR sensor, predicting impact locations from filtered images resulted in less jittery spacecraft control accelerations than conducting missions with a visual sensor. Infrared sensors have also the possibility to detect asteroids at greater distances, and if properly used, can aid in terminal phase guidance for proper impact location determination for the MKIV system. Emerging new topics of the Minimum Orbit Intersection Distance (MOID) estimation and the Full-Two-Body Problem (F2BP) formulation are also investigated to assess a potential near-Earth object collision risk and the proximity gravity effects of an irregular-shaped binary-asteroid target on a standoff nuclear explosion mission.

Human endogenous retrovirus type W envelope expression in blood and brain cells provides new insights into multiple sclerosis disease

PubMed Central

Germi, Raphaëlle; Bernard, Corinne; Garcia-Montojo, Marta; Deluen, Cécile; Farinelli, Laurent; Faucard, Raphaël; Veas, Francisco; Stefas, Ilias; Fabriek, Babs O; Van-Horssen, Jack; Van-der-Valk, Paul; Gerdil, Claire; Mancuso, Roberta; Saresella, Marina; Clerici, Mario; Marcel, Sébastien; Creange, Alain; Cavaretta, Rosella; Caputo, Domenico; Arru, Giannina; Morand, Patrice; Lang, Alois B; Sotgiu, Stefano; Ruprecht, Klemens; Rieckmann, Peter; Villoslada, Pablo; Chofflon, Michel; Boucraut, Jose; Pelletier, Jean; Hartung, Hans-Peter

2012-01-01

Background: The envelope protein from multiple sclerosis (MS) associated retroviral element (MSRV), a member of the Human Endogenous Retroviral family ‘W’ (HERV-W), induces dysimmunity and inflammation. Objective: The objective of this study was to confirm and specify the association between HERV-W/MSRV envelope (Env) expression and MS. Methods: 103 MS, 199 healthy controls (HC) and controls with other neurological diseases (28), chronic infections (30) or autoimmunity (30) were analysed with an immunoassay detecting Env in serum. Env RNA or DNA copy numbers in peripheral blood mononuclear cells (PBMC) were determined by a quantitative polymerase chain reaction (PCR). Env was detected by immunohistology in the brains of patients with MS with three specific monoclonals. Results: Env antigen was detected in a serum of 73% of patients with MS with similar prevalence in all clinical forms, and not in chronic infection, systemic lupus, most other neurological diseases and healthy donors (p<0.01). Cases with chronic inflammatory demyelinating polyneuropathy (5/8) and rare HC (4/103) were positive. RNA expression in PBMC and DNA copy numbers were significantly elevated in patients with MS versus HC (p<0.001). In patients with MS, DNA copy numbers were significantly increased in chronic progressive MS (secondary progressive MS vs relapsing–remitting MS (RRMS) p<0.001; primary progressive MS vs RRMS –<0.02). Env protein was evidenced in macrophages within MS brain lesions with particular concentrations around vascular elements. Conclusion: The association between MS disease and the MSRV-type HERV-W element now appears quite strong, as evidenced ex-vivo from serum and PBMC with post-mortem confirmation in brain lesions. Chronic progressive MS, RRMS and clinically isolated syndrome show different ELISA (Enzyme-Linked Immunosorbent Assay) and/or PCR profiles suggestive of an increase with disease evolution, and amplicon sequencing confirms the association with particular HERV-W elements. PMID:22457345

Spatio-Semantic Comparison of Large 3d City Models in Citygml Using a Graph Database

NASA Astrophysics Data System (ADS)

Nguyen, S. H.; Yao, Z.; Kolbe, T. H.

2017-10-01

A city may have multiple CityGML documents recorded at different times or surveyed by different users. To analyse the city's evolution over a given period of time, as well as to update or edit the city model without negating modifications made by other users, it is of utmost importance to first compare, detect and locate spatio-semantic changes between CityGML datasets. This is however difficult due to the fact that CityGML elements belong to a complex hierarchical structure containing multi-level deep associations, which can basically be considered as a graph. Moreover, CityGML allows multiple syntactic ways to define an object leading to syntactic ambiguities in the exchange format. Furthermore, CityGML is capable of including not only 3D urban objects' graphical appearances but also their semantic properties. Since to date, no known algorithm is capable of detecting spatio-semantic changes in CityGML documents, a frequent approach is to replace the older models completely with the newer ones, which not only costs computational resources, but also loses track of collaborative and chronological changes. Thus, this research proposes an approach capable of comparing two arbitrarily large-sized CityGML documents on both semantic and geometric level. Detected deviations are then attached to their respective sources and can easily be retrieved on demand. As a result, updating a 3D city model using this approach is much more efficient as only real changes are committed. To achieve this, the research employs a graph database as the main data structure for storing and processing CityGML datasets in three major steps: mapping, matching and updating. The mapping process transforms input CityGML documents into respective graph representations. The matching process compares these graphs and attaches edit operations on the fly. Found changes can then be executed using the Web Feature Service (WFS), the standard interface for updating geographical features across the web.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harp, G. R.; Richards, Jon; Tarter, Jill C.

We report radio SETI observations on a large number of known exoplanets and other nearby star systems using the Allen Telescope Array (ATA). Observations were made over about 19000 hr from 2009 May to 2015 December. This search focused on narrowband radio signals from a set totaling 9293 stars, including 2015 exoplanet stars and Kepler objects of interest and an additional 65 whose planets may be close to their habitable zones. The ATA observations were made using multiple synthesized beams and an anticoincidence filter to help identify terrestrial radio interference. Stars were observed over frequencies from 1 to 9 GHzmore » in multiple bands that avoid strong terrestrial communication frequencies. Data were processed in near-real time for narrowband (0.7–100 Hz) continuous and pulsed signals with transmitter/receiver relative accelerations from −0.3 to 0.3 m s{sup −2}. A total of 1.9 × 10{sup 8} unique signals requiring immediate follow-up were detected in observations covering more than 8 × 10{sup 6} star-MHz. We detected no persistent signals from extraterrestrial technology exceeding our frequency-dependent sensitivity threshold of 180–310 × 10{sup −26} W m{sup −2}.« less

Determining the 95% limit of detection for waterborne pathogen analyses from primary concentration to qPCR.

PubMed

Stokdyk, Joel P; Firnstahl, Aaron D; Spencer, Susan K; Burch, Tucker R; Borchardt, Mark A

2016-06-01

The limit of detection (LOD) for qPCR-based analyses is not consistently defined or determined in studies on waterborne pathogens. Moreover, the LODs reported often reflect the qPCR assay alone rather than the entire sample process. Our objective was to develop an approach to determine the 95% LOD (lowest concentration at which 95% of positive samples are detected) for the entire process of waterborne pathogen detection. We began by spiking the lowest concentration that was consistently positive at the qPCR step (based on its standard curve) into each procedural step working backwards (i.e., extraction, secondary concentration, primary concentration), which established a concentration that was detectable following losses of the pathogen from processing. Using the fraction of positive replicates (n = 10) at this concentration, we selected and analyzed a second, and then third, concentration. If the fraction of positive replicates equaled 1 or 0 for two concentrations, we selected another. We calculated the LOD using probit analysis. To demonstrate our approach we determined the 95% LOD for Salmonella enterica serovar Typhimurium, adenovirus 41, and vaccine-derived poliovirus Sabin 3, which were 11, 12, and 6 genomic copies (gc) per reaction (rxn), respectively (equivalent to 1.3, 1.5, and 4.0 gc L(-1) assuming the 1500 L tap-water sample volume prescribed in EPA Method 1615). This approach limited the number of analyses required and was amenable to testing multiple genetic targets simultaneously (i.e., spiking a single sample with multiple microorganisms). An LOD determined this way can facilitate study design, guide the number of required technical replicates, aid method evaluation, and inform data interpretation. Published by Elsevier Ltd.

Determining the 95% limit of detection for waterborne pathogen analyses from primary concentration to qPCR

USGS Publications Warehouse

Stokdyk, Joel P.; Firnstahl, Aaron; Spencer, Susan K.; Burch, Tucker R; Borchardt, Mark A.

2016-01-01

The limit of detection (LOD) for qPCR-based analyses is not consistently defined or determined in studies on waterborne pathogens. Moreover, the LODs reported often reflect the qPCR assay alone rather than the entire sample process. Our objective was to develop an approach to determine the 95% LOD (lowest concentration at which 95% of positive samples are detected) for the entire process of waterborne pathogen detection. We began by spiking the lowest concentration that was consistently positive at the qPCR step (based on its standard curve) into each procedural step working backwards (i.e., extraction, secondary concentration, primary concentration), which established a concentration that was detectable following losses of the pathogen from processing. Using the fraction of positive replicates (n = 10) at this concentration, we selected and analyzed a second, and then third, concentration. If the fraction of positive replicates equaled 1 or 0 for two concentrations, we selected another. We calculated the LOD using probit analysis. To demonstrate our approach we determined the 95% LOD for Salmonella enterica serovar Typhimurium, adenovirus 41, and vaccine-derived poliovirus Sabin 3, which were 11, 12, and 6 genomic copies (gc) per reaction (rxn), respectively (equivalent to 1.3, 1.5, and 4.0 gc L−1 assuming the 1500 L tap-water sample volume prescribed in EPA Method 1615). This approach limited the number of analyses required and was amenable to testing multiple genetic targets simultaneously (i.e., spiking a single sample with multiple microorganisms). An LOD determined this way can facilitate study design, guide the number of required technical replicates, aid method evaluation, and inform data interpretation.

A survey on object detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Cheng, Gong; Han, Junwei

2016-07-01

Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

A stereo-vision hazard-detection algorithm to increase planetary lander autonomy

NASA Astrophysics Data System (ADS)

Woicke, Svenja; Mooij, Erwin

2016-05-01

For future landings on any celestial body, increasing the lander autonomy as well as decreasing risk are primary objectives. Both risk reduction and an increase in autonomy can be achieved by including hazard detection and avoidance in the guidance, navigation, and control loop. One of the main challenges in hazard detection and avoidance is the reconstruction of accurate elevation models, as well as slope and roughness maps. Multiple methods for acquiring the inputs for hazard maps are available. The main distinction can be made between active and passive methods. Passive methods (cameras) have budgetary advantages compared to active sensors (radar, light detection and ranging). However, it is necessary to proof that these methods deliver sufficiently good maps. Therefore, this paper discusses hazard detection using stereo vision. To facilitate a successful landing not more than 1% wrong detections (hazards that are not identified) are allowed. Based on a sensitivity analysis it was found that using a stereo set-up at a baseline of ≤ 2 m is feasible at altitudes of ≤ 200 m defining false positives of less than 1%. It was thus shown that stereo-based hazard detection is an effective means to decrease the landing risk and increase the lander autonomy. In conclusion, the proposed algorithm is a promising candidate for future landers.

Techniques for assessing relative values for multiple objective management on private forests

Treesearch

Donald F. Dennis; Thomas H. Stevens; David B. Kittredge; Mark G. Rickenbach

2003-01-01

Decision models for assessing multiple objective management of private lands will require estimates of the relative values of various nonmarket outputs or objectives that have become increasingly important. In this study, conjoint techniques are used to assess the relative values and acceptable trade-offs (marginal rates of substitution) among various objectives...

The near-infrared counterpart of a variable galactic plane radio source

NASA Technical Reports Server (NTRS)

Margon, Bruce; Phillips, Andrew C.; Ciardullo, Robin; Jacoby, George H.

1992-01-01

A near-infrared counterpart to the highly variable, unresolved galactic plane radio source GT 0116 + 622 is identified. This source is of particular interest, as it has been previously suggested to be the counterpart of the gamma-ray source Cas gamma-l. The present NIR and red images detect a faint, spatially extended (3 arcsec FWHM), very red object coincident with the radio position. There is complex spatial structure which may be due in part to an unrelated superposed foreground object. Observations on multiple nights show no evidence for flux variability, despite the high amplitude variability on a time-scale of days reported for the radio source. The data are consistent with an interpretation of GT 0116 + 622 as an unusually variable, obscured active galaxy at a distance of several hundred megaparsecs, although more exotic, and in particular galactic, interpretations cannot yet be ruled out. If the object is extragalactic, the previously suggested identification with the gamma-ray source would seem unlikely.

Spatiotemporal motion boundary detection and motion boundary velocity estimation for tracking moving objects with a moving camera: a level sets PDEs approach with concurrent camera motion compensation.

PubMed

Feghali, Rosario; Mitiche, Amar

2004-11-01

The purpose of this study is to investigate a method of tracking moving objects with a moving camera. This method estimates simultaneously the motion induced by camera movement. The problem is formulated as a Bayesian motion-based partitioning problem in the spatiotemporal domain of the image quence. An energy functional is derived from the Bayesian formulation. The Euler-Lagrange descent equations determine imultaneously an estimate of the image motion field induced by camera motion and an estimate of the spatiotemporal motion undary surface. The Euler-Lagrange equation corresponding to the surface is expressed as a level-set partial differential equation for topology independence and numerically stable implementation. The method can be initialized simply and can track multiple objects with nonsimultaneous motions. Velocities on motion boundaries can be estimated from geometrical properties of the motion boundary. Several examples of experimental verification are given using synthetic and real-image sequences.

Distributed Representation of Visual Objects by Single Neurons in the Human Brain

PubMed Central

Valdez, André B.; Papesh, Megan H.; Treiman, David M.; Smith, Kris A.; Goldinger, Stephen D.

2015-01-01

It remains unclear how single neurons in the human brain represent whole-object visual stimuli. While recordings in both human and nonhuman primates have shown distributed representations of objects (many neurons encoding multiple objects), recordings of single neurons in the human medial temporal lobe, taken as subjects' discriminated objects during multiple presentations, have shown gnostic representations (single neurons encoding one object). Because some studies suggest that repeated viewing may enhance neural selectivity for objects, we had human subjects discriminate objects in a single, more naturalistic viewing session. We found that, across 432 well isolated neurons recorded in the hippocampus and amygdala, the average fraction of objects encoded was 26%. We also found that more neurons encoded several objects versus only one object in the hippocampus (28 vs 18%, p < 0.001) and in the amygdala (30 vs 19%, p < 0.001). Thus, during realistic viewing experiences, typical neurons in the human medial temporal lobe code for a considerable range of objects, across multiple semantic categories. PMID:25834044

The Evolution of the Multiplicity of Embedded Protostars. I. Sample Properties and Binary Detections

NASA Astrophysics Data System (ADS)

Connelley, Michael S.; Reipurth, Bo; Tokunaga, Alan T.

2008-06-01

We present the observational results of a near-infrared survey of a large sample of Class I protostars designed to determine the Class I binary separation distribution from ~100 AU to ~5000 AU. We have selected targets from a new sample of 267 nearby candidate Class I objects. This sample is well understood, consists of mostly Class I young stellar objects (YSOs) within 1 kpc, has targets selected from the whole sky, and is not biased by previous studies of star formation. We have observed 189 Class I YSOs north of δ = -40° at the H, K, and L' bands, with a median angular resolution of 0farcs33 at L'. We determine our detection limit for close binary companions by observing artificial binaries. We choose a contrast limit and an outer detection limit to minimize contamination and to ensure that a candidate companion is gravitationally bound. Our survey uses observations at the L' rather than the K band for the detection of binary companions since there is less scattered light and better seeing at L'. This paper presents the positions of our targets, the near-IR photometry of sources detected in our fields at L', as well as the observed properties of the 89 detected companions (73 of which are newly discovered). Although we have chosen contrast and separation limits to minimize contamination, we expect that there are about six stars identified as binary companions that are due to contamination. Finder charts at L' for each field are shown to facilitate future studies of these objects. The Infrared Telescope Facility is operated by the University of Hawaii under Cooperative Agreement no. NCC 5-538 with the National Aeronautics and Space Administration, Science Mission Directorate, Planetary Astronomy Program. The United Kingdom Infrared Telescope is operated by the Joint Astronomy Centre on behalf of the Science and Technology Facilities Council of the U.K. Based in part on data collected at the Subaru Telescope, which is operated by the National Astronomical Observatory of Japan.

H2LIFT: global navigation simulation ship tracking and WMD detection in the maritime domain

NASA Astrophysics Data System (ADS)

Wyffels, Kevin

2007-04-01

This paper presents initial results for a tracking simulation of multiple maritime vehicles for use in a data fusion program detecting Weapons of Mass Destruction (WMD). This simulation supports a fusion algorithm (H2LIFT) for collecting and analyzing data providing a heuristic analysis tool for detecting weapons of mass destruction in the maritime domain. Tools required to develop a navigational simulation fitting a set of project objectives are introduced for integration into the H2LIFT algorithm. Emphasis is placed on the specific requirements of the H2LIFT project, however the basic equations, algorithms, and methodologies can be used as tools in a variety of scenario simulations. Discussion will be focused on track modeling (e.g. position tracking of ships), navigational techniques, WMD detection, and simulation of these models using Matlab and Simulink. Initial results provide absolute ship position data for a given multi-ship maritime scenario with random generation of a given ship containing a WMD. Required coordinate systems, conversions between coordinate systems, Earth modeling techniques, and navigational conventions and techniques are introduced for development of the simulations.

The Muon Portal Project: Design and construction of a scanning portal based on muon tomography

NASA Astrophysics Data System (ADS)

Antonuccio, V.; Bandieramonte, M.; Becciani, U.; Bonanno, D. L.; Bonanno, G.; Bongiovanni, D.; Fallica, P. G.; Garozzo, S.; Grillo, A.; La Rocca, P.; Leonora, E.; Longhitano, F.; Lo Presti, D.; Marano, D.; Parasole, O.; Pugliatti, C.; Randazzo, N.; Riggi, F.; Riggi, S.; Romeo, G.; Romeo, M.; Russo, G. V.; Santagati, G.; Timpanaro, M. C.; Valvo, G.

2017-02-01

Cosmic ray tomography is a technique which exploits the multiple Coulomb scattering of highly penetrating cosmic ray-produced muons to perform non-destructive inspection of high-Z materials without the use of artificial radiation. A muon tomography detection system can be used as a portal monitor at border crossing points for detecting illegal targeted objects. The Muon Portal Project is a joint initiative between Italian research and industrial partners, aimed at the construction of a real size detector prototype (6×3×7 m3) for the inspection of cargo containers by the muon scattering technique. The detector consists of four XY tracking planes, two placed above and two below the container to be inspected. After a research and development phase, which led to the choice and test of the individual components, the construction and installation of the detection modules is almost completed. In this paper the present status of the Project is reported, focusing on the design and construction phase, as well as on the preliminary results obtained with the first detection planes.

Time-of-flight-assisted Kinect camera-based people detection for intuitive human robot cooperation in the surgical operating room.

PubMed

Beyl, Tim; Nicolai, Philip; Comparetti, Mirko D; Raczkowsky, Jörg; De Momi, Elena; Wörn, Heinz

2016-07-01

Scene supervision is a major tool to make medical robots safer and more intuitive. The paper shows an approach to efficiently use 3D cameras within the surgical operating room to enable for safe human robot interaction and action perception. Additionally the presented approach aims to make 3D camera-based scene supervision more reliable and accurate. A camera system composed of multiple Kinect and time-of-flight cameras has been designed, implemented and calibrated. Calibration and object detection as well as people tracking methods have been designed and evaluated. The camera system shows a good registration accuracy of 0.05 m. The tracking of humans is reliable and accurate and has been evaluated in an experimental setup using operating clothing. The robot detection shows an error of around 0.04 m. The robustness and accuracy of the approach allow for an integration into modern operating room. The data output can be used directly for situation and workflow detection as well as collision avoidance.

Projection Mapping User Interface for Disabled People

PubMed Central

Simutis, Rimvydas; Maskeliūnas, Rytis

2018-01-01

Difficulty in communicating is one of the key challenges for people suffering from severe motor and speech disabilities. Often such person can communicate and interact with the environment only using assistive technologies. This paper presents a multifunctional user interface designed to improve communication efficiency and person independence. The main component of this interface is a projection mapping technique used to highlight objects in the environment. Projection mapping makes it possible to create a natural augmented reality information presentation method. The user interface combines a depth sensor and a projector to create camera-projector system. We provide a detailed description of camera-projector system calibration procedure. The described system performs tabletop object detection and automatic projection mapping. Multiple user input modalities have been integrated into the multifunctional user interface. Such system can be adapted to the needs of people with various disabilities. PMID:29686827

Wavelet-based adaptive thresholding method for image segmentation

NASA Astrophysics Data System (ADS)

Chen, Zikuan; Tao, Yang; Chen, Xin; Griffis, Carl

2001-05-01

A nonuniform background distribution may cause a global thresholding method to fail to segment objects. One solution is using a local thresholding method that adapts to local surroundings. In this paper, we propose a novel local thresholding method for image segmentation, using multiscale threshold functions obtained by wavelet synthesis with weighted detail coefficients. In particular, the coarse-to- fine synthesis with attenuated detail coefficients produces a threshold function corresponding to a high-frequency- reduced signal. This wavelet-based local thresholding method adapts to both local size and local surroundings, and its implementation can take advantage of the fast wavelet algorithm. We applied this technique to physical contaminant detection for poultry meat inspection using x-ray imaging. Experiments showed that inclusion objects in deboned poultry could be extracted at multiple resolutions despite their irregular sizes and uneven backgrounds.

Projection Mapping User Interface for Disabled People.

PubMed

Gelšvartas, Julius; Simutis, Rimvydas; Maskeliūnas, Rytis

2018-01-01

Difficulty in communicating is one of the key challenges for people suffering from severe motor and speech disabilities. Often such person can communicate and interact with the environment only using assistive technologies. This paper presents a multifunctional user interface designed to improve communication efficiency and person independence. The main component of this interface is a projection mapping technique used to highlight objects in the environment. Projection mapping makes it possible to create a natural augmented reality information presentation method. The user interface combines a depth sensor and a projector to create camera-projector system. We provide a detailed description of camera-projector system calibration procedure. The described system performs tabletop object detection and automatic projection mapping. Multiple user input modalities have been integrated into the multifunctional user interface. Such system can be adapted to the needs of people with various disabilities.

A-Track: Detecting Moving Objects in FITS images

NASA Astrophysics Data System (ADS)

Atay, T.; Kaplan, M.; Kilic, Y.; Karapinar, N.

2017-04-01

A-Track is a fast, open-source, cross-platform pipeline for detecting moving objects (asteroids and comets) in sequential telescope images in FITS format. The moving objects are detected using a modified line detection algorithm.

Sensor Tasking for Detection and Custody of HAMR Objects

NASA Astrophysics Data System (ADS)

Frueh, C.; Paul, S. M.; Fiedler, H.

High area-to-mass ratio objects (HAMR) are objects that are highly perturbed especially by non-conservative forces such as drag and solar radiation pressure. As a consequence, they are population different orbital regions than low area-to-mass ratio objects. This makes the objects hard to detect. After initial detection those objects are often lost, because standard follow-up times of thirty periods are not sufficient for redetection. This paper applies a sensor tasking and follow-up strategy to the problem of detecting and keeping custody of HAMR objects.

Detection of suboptimal effort with symbol span: development of a new embedded index.

PubMed

Young, J Christopher; Caron, Joshua E; Baughman, Brandon C; Sawyer, R John

2012-03-01

Developing embedded indicators of suboptimal effort on objective neurocognitive testing is essential for detecting increasingly sophisticated forms of symptom feigning. The current study explored whether Symbol Span, a novel Wechsler Memory Scale-fourth edition measure of supraspan visual attention, could be used to discriminate adequate effort from suboptimal effort. Archival data were collected from 136 veterans classified into Poor Effort (n = 42) and Good Effort (n = 94) groups based on symptom validity test (SVT) performance. The Poor Effort group had significantly lower raw scores (p < .001) and age-corrected scaled scores (p < .001) than the Good Effort group on the Symbol Span test. A raw score cutoff of <14 produced 83% specificity and 50% sensitivity for detection of Poor Effort. Similarly, sensitivity was 52% and specificity was 84% when employing a cutoff of <7 for Age-Corrected Scale Score. Collectively, present results suggest that Symbol Span can effectively differentiate veterans with multiple failures on established free-standing and embedded SVTs.

Analysis of Multiple Metabolites of Tocopherols and Tocotrienols in Mice and Humans

PubMed Central

Zhao, Yang; Lee, Mao-Jung; Cheung, Connie; Ju, Ji-Hyeung; Chen, Yu-Kuo; Liu, Ba; Hu, Long-Qin; Yang, Chung S.

2010-01-01

Tocopherols and tocotrienols, collectively known as vitamin E, are essential antioxidant nutrients. The biological fates and metabolite profiles of the different forms are not clearly understood. The objective of this study is to simultaneously analyze the metabolites of different tocopherols and tocotrienols in mouse and human samples. Using HPLC/electrochemical detection and mass spectrometry, 18 tocopherol-derived and 24 tocotrienol-derived side-chain degradation metabolites were identified in fecal samples. Short-chain degradation metabolites, in particular γ- and δ- carboxyethyl hydroxychromans (CEHCs) and carboxymethylbutyl hydroxychromans (CMBHCs) were detected in urine, serum and liver samples, with tocopherols additionally detected in serum and liver samples. The metabolite profiles of tocotrienols and tocopherols were similar, but new tocotrienol metabolites with double bonds were identified. This is the first comprehensive report describing simultaneous analysis of different side-chain metabolites of tocopherols and tocotrienols in mice and humans. Urinary metabolites may serve as useful biomarkers for nutritional assessment of vitamin E. PMID:20222730

Fusion of thermal- and visible-band video for abandoned object detection

NASA Astrophysics Data System (ADS)

Beyan, Cigdem; Yigit, Ahmet; Temizel, Alptekin

2011-07-01

Timely detection of packages that are left unattended in public spaces is a security concern, and rapid detection is important for prevention of potential threats. Because constant surveillance of such places is challenging and labor intensive, automated abandoned-object-detection systems aiding operators have started to be widely used. In many studies, stationary objects, such as people sitting on a bench, are also detected as suspicious objects due to abandoned items being defined as items newly added to the scene and remained stationary for a predefined time. Therefore, any stationary object results in an alarm causing a high number of false alarms. These false alarms could be prevented by classifying suspicious items as living and nonliving objects. In this study, a system for abandoned object detection that aids operators surveilling indoor environments such as airports, railway or metro stations, is proposed. By analysis of information from a thermal- and visible-band camera, people and the objects left behind can be detected and discriminated as living and nonliving, reducing the false-alarm rate. Experiments demonstrate that using data obtained from a thermal camera in addition to a visible-band camera also increases the true detection rate of abandoned objects.

Extreme gravity tests with gravitational waves from compact binary coalescences: (II) ringdown

NASA Astrophysics Data System (ADS)

Berti, Emanuele; Yagi, Kent; Yang, Huan; Yunes, Nicolás

2018-05-01

The LIGO/Virgo detections of binary black hole mergers marked a watershed moment in astronomy, ushering in the era of precision tests of Kerr dynamics. We review theoretical and experimental challenges that must be overcome to carry out black hole spectroscopy with present and future gravitational wave detectors. Among other topics, we discuss quasinormal mode excitation in binary mergers, astrophysical event rates, tests of black hole dynamics in modified theories of gravity, parameterized "post-Kerr" ringdown tests, exotic compact objects, and proposed data analysis methods to improve spectroscopic tests of Kerr dynamics by stacking multiple events.

Lineage mapper: A versatile cell and particle tracker

NASA Astrophysics Data System (ADS)

Chalfoun, Joe; Majurski, Michael; Dima, Alden; Halter, Michael; Bhadriraju, Kiran; Brady, Mary

2016-11-01

The ability to accurately track cells and particles from images is critical to many biomedical problems. To address this, we developed Lineage Mapper, an open-source tracker for time-lapse images of biological cells, colonies, and particles. Lineage Mapper tracks objects independently of the segmentation method, detects mitosis in confluence, separates cell clumps mistakenly segmented as a single cell, provides accuracy and scalability even on terabyte-sized datasets, and creates division and/or fusion lineages. Lineage Mapper has been tested and validated on multiple biological and simulated problems. The software is available in ImageJ and Matlab at isg.nist.gov.

Stellar interferometers and hypertelescopes: new insights on an angular spatial frequency approach to their non-invariant imaging

NASA Astrophysics Data System (ADS)

Dettwiller, L.; Lépine, T.

2017-12-01

A general and pure wave theory of image formation for all types of stellar interferometers, including hypertelescopes, is developed in the frame of Fresnel's paraxial approximations of diffraction. For a hypertelescope, we show that the severe lack of translation invariance leads to multiple and strong spatial frequency heterodyning, which codes the very high frequencies detected by the hypertelescope into medium spatial frequencies and introduces a moiré-type ambiguity for extended objects. This explains mathematically the disappointing appearance of poor resolution observed in some image simulations for hypertelescopes.

Pigmented skin lesion detection using random forest and wavelet-based texture

NASA Astrophysics Data System (ADS)

Hu, Ping; Yang, Tie-jun

2016-10-01

The incidence of cutaneous malignant melanoma, a disease of worldwide distribution and is the deadliest form of skin cancer, has been rapidly increasing over the last few decades. Because advanced cutaneous melanoma is still incurable, early detection is an important step toward a reduction in mortality. Dermoscopy photographs are commonly used in melanoma diagnosis and can capture detailed features of a lesion. A great variability exists in the visual appearance of pigmented skin lesions. Therefore, in order to minimize the diagnostic errors that result from the difficulty and subjectivity of visual interpretation, an automatic detection approach is required. The objectives of this paper were to propose a hybrid method using random forest and Gabor wavelet transformation to accurately differentiate which part belong to lesion area and the other is not in a dermoscopy photographs and analyze segmentation accuracy. A random forest classifier consisting of a set of decision trees was used for classification. Gabor wavelets transformation are the mathematical model of visual cortical cells of mammalian brain and an image can be decomposed into multiple scales and multiple orientations by using it. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain. Texture features based on Gabor wavelets transformation are found by the Gabor filtered image. Experiment results indicate the following: (1) the proposed algorithm based on random forest outperformed the-state-of-the-art in pigmented skin lesions detection (2) and the inclusion of Gabor wavelet transformation based texture features improved segmentation accuracy significantly.

Development and evaluation of modified envelope correlation method for deep tectonic tremor

NASA Astrophysics Data System (ADS)

Mizuno, N.; Ide, S.

2017-12-01

We develop a new location method for deep tectonic tremors, as an improvement of widely used envelope correlation method, and applied it to construct a tremor catalog in western Japan. Using the cross-correlation functions as objective functions and weighting components of data by the inverse of error variances, the envelope cross-correlation method is redefined as a maximum likelihood method. This method is also capable of multiple source detection, because when several events occur almost simultaneously, they appear as local maxima of likelihood.The average of weighted cross-correlation functions, defined as ACC, is a nonlinear function whose variable is a position of deep tectonic tremor. The optimization method has two steps. First, we fix the source depth to 30 km and use a grid search with 0.2 degree intervals to find the maxima of ACC, which are candidate event locations. Then, using each of the candidate locations as initial values, we apply a gradient method to determine horizontal and vertical components of a hypocenter. Sometimes, several source locations are determined in a time window of 5 minutes. We estimate the resolution, which is defined as a distance of sources to be detected separately by the location method, is about 100 km. The validity of this estimation is confirmed by a numerical test using synthetic waveforms. Applying to continuous seismograms in western Japan for over 10 years, the new method detected 27% more tremors than a previous method, owing to the multiple detection and improvement of accuracy by appropriate weighting scheme.