Autonomous Exploration for Gathering Increased Science

NASA Technical Reports Server (NTRS)

Bornstein, Benjamin J.; Castano, Rebecca; Estlin, Tara A.; Gaines, Daniel M.; Anderson, Robert C.; Thompson, David R.; DeGranville, Charles K.; Chien, Steve A.; Tang, Benyang; Burl, Michael C.;

Clustering analysis of moving target signatures

NASA Astrophysics Data System (ADS)

Martone, Anthony; Ranney, Kenneth; Innocenti, Roberto

2010-04-01

Previously, we developed a moving target indication (MTI) processing approach to detect and track slow-moving targets inside buildings, which successfully detected moving targets (MTs) from data collected by a low-frequency, ultra-wideband radar. Our MTI algorithms include change detection, automatic target detection (ATD), clustering, and tracking. The MTI algorithms can be implemented in a real-time or near-real-time system; however, a person-in-the-loop is needed to select input parameters for the clustering algorithm. Specifically, the number of clusters to input into the cluster algorithm is unknown and requires manual selection. A critical need exists to automate all aspects of the MTI processing formulation. In this paper, we investigate two techniques that automatically determine the number of clusters: the adaptive knee-point (KP) algorithm and the recursive pixel finding (RPF) algorithm. The KP algorithm is based on a well-known heuristic approach for determining the number of clusters. The RPF algorithm is analogous to the image processing, pixel labeling procedure. Both algorithms are used to analyze the false alarm and detection rates of three operational scenarios of personnel walking inside wood and cinderblock buildings.

Does training under consistent mapping conditions lead to automatic attention attraction to targets in search tasks?

PubMed

Lefebvre, Christine; Cousineau, Denis; Larochelle, Serge

2008-11-01

Schneider and Shiffrin (1977) proposed that training under consistent stimulus-response mapping (CM) leads to automatic target detection in search tasks. Other theories, such as Treisman and Gelade's (1980) feature integration theory, consider target-distractor discriminability as the main determinant of search performance. The first two experiments pit these two principles against each other. The results show that CM training is neither necessary nor sufficient to achieve optimal search performance. Two other experiments examine whether CM trained targets, presented as distractors in unattended display locations, attract attention away from current targets. The results are again found to vary with target-distractor similarity. Overall, the present study strongly suggests that CM training does not invariably lead to automatic attention attraction in search tasks.

Precision Targeting With a Tracking Adaptive Optics Scanning Laser Ophthalmoscope

DTIC Science & Technology

2006-01-01

automatic high- resolution mosaic generation, and automatic blink detection and tracking re-lock were also tested. The system has the potential to become an...structures can lead to earlier detection of retinal diseases such as age-related macular degeneration (AMD) and diabetic retinopathy (DR). Combined...optics systems sense perturbations in the detected wave-front and apply corrections to an optical element that flatten the wave-front and allow near

Scale invariant SURF detector and automatic clustering segmentation for infrared small targets detection

NASA Astrophysics Data System (ADS)

Zhang, Haiying; Bai, Jiaojiao; Li, Zhengjie; Liu, Yan; Liu, Kunhong

2017-06-01

The detection and discrimination of infrared small dim targets is a challenge in automatic target recognition (ATR), because there is no salient information of size, shape and texture. Many researchers focus on mining more discriminative information of targets in temporal-spatial. However, such information may not be available with the change of imaging environments, and the targets size and intensity keep changing in different imaging distance. So in this paper, we propose a novel research scheme using density-based clustering and backtracking strategy. In this scheme, the speeded up robust feature (SURF) detector is applied to capture candidate targets in single frame at first. And then, these points are mapped into one frame, so that target traces form a local aggregation pattern. In order to isolate the targets from noises, a newly proposed density-based clustering algorithm, fast search and find of density peak (FSFDP for short), is employed to cluster targets by the spatial intensive distribution. Two important factors of the algorithm, percent and γ , are exploited fully to determine the clustering scale automatically, so as to extract the trace with highest clutter suppression ratio. And at the final step, a backtracking algorithm is designed to detect and discriminate target trace as well as to eliminate clutter. The consistence and continuity of the short-time target trajectory in temporal-spatial is incorporated into the bounding function to speed up the pruning. Compared with several state-of-arts methods, our algorithm is more effective for the dim targets with lower signal-to clutter ratio (SCR). Furthermore, it avoids constructing the candidate target trajectory searching space, so its time complexity is limited to a polynomial level. The extensive experimental results show that it has superior performance in probability of detection (Pd) and false alarm suppressing rate aiming at variety of complex backgrounds.

Automatic visibility retrieval from thermal camera images

NASA Astrophysics Data System (ADS)

Dizerens, Céline; Ott, Beat; Wellig, Peter; Wunderle, Stefan

2017-10-01

This study presents an automatic visibility retrieval of a FLIR A320 Stationary Thermal Imager installed on a measurement tower on the mountain Lagern located in the Swiss Jura Mountains. Our visibility retrieval makes use of edges that are automatically detected from thermal camera images. Predefined target regions, such as mountain silhouettes or buildings with high thermal differences to the surroundings, are used to derive the maximum visibility distance that is detectable in the image. To allow a stable, automatic processing, our procedure additionally removes noise in the image and includes automatic image alignment to correct small shifts of the camera. We present a detailed analysis of visibility derived from more than 24000 thermal images of the years 2015 and 2016 by comparing them to (1) visibility derived from a panoramic camera image (VISrange), (2) measurements of a forward-scatter visibility meter (Vaisala FD12 working in the NIR spectra), and (3) modeled visibility values using the Thermal Range Model TRM4. Atmospheric conditions, mainly water vapor from European Center for Medium Weather Forecast (ECMWF), were considered to calculate the extinction coefficients using MODTRAN. The automatic visibility retrieval based on FLIR A320 images is often in good agreement with the retrieval from the systems working in different spectral ranges. However, some significant differences were detected as well, depending on weather conditions, thermal differences of the monitored landscape, and defined target size.

Tier-scalable reconnaissance: the future in autonomous C4ISR systems has arrived: progress towards an outdoor testbed

NASA Astrophysics Data System (ADS)

Fink, Wolfgang; Brooks, Alexander J.-W.; Tarbell, Mark A.; Dohm, James M.

2017-05-01

Autonomous reconnaissance missions are called for in extreme environments, as well as in potentially hazardous (e.g., the theatre, disaster-stricken areas, etc.) or inaccessible operational areas (e.g., planetary surfaces, space). Such future missions will require increasing degrees of operational autonomy, especially when following up on transient events. Operational autonomy encompasses: (1) Automatic characterization of operational areas from different vantages (i.e., spaceborne, airborne, surface, subsurface); (2) automatic sensor deployment and data gathering; (3) automatic feature extraction including anomaly detection and region-of-interest identification; (4) automatic target prediction and prioritization; (5) and subsequent automatic (re-)deployment and navigation of robotic agents. This paper reports on progress towards several aspects of autonomous C4ISR systems, including: Caltech-patented and NASA award-winning multi-tiered mission paradigm, robotic platform development (air, ground, water-based), robotic behavior motifs as the building blocks for autonomous tele-commanding, and autonomous decision making based on a Caltech-patented framework comprising sensor-data-fusion (feature-vectors), anomaly detection (clustering and principal component analysis), and target prioritization (hypothetical probing).

Reliable motion detection of small targets in video with low signal-to-clutter ratios

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

Nichols, S.A.; Naylor, R.B.

1995-07-01

Studies show that vigilance decreases rapidly after several minutes when human operators are required to search live video for infrequent intrusion detections. Therefore, there is a need for systems which can automatically detect targets in live video and reserve the operator`s attention for assessment only. Thus far, automated systems have not simultaneously provided adequate detection sensitivity, false alarm suppression, and ease of setup when used in external, unconstrained environments. This unsatisfactory performance can be exacerbated by poor video imagery with low contrast, high noise, dynamic clutter, image misregistration, and/or the presence of small, slow, or erratically moving targets. This papermore » describes a highly adaptive video motion detection and tracking algorithm which has been developed as part of Sandia`s Advanced Exterior Sensor (AES) program. The AES is a wide-area detection and assessment system for use in unconstrained exterior security applications. The AES detection and tracking algorithm provides good performance under stressing data and environmental conditions. Features of the algorithm include: reliable detection with negligible false alarm rate of variable velocity targets having low signal-to-clutter ratios; reliable tracking of targets that exhibit motion that is non-inertial, i.e., varies in direction and velocity; automatic adaptation to both infrared and visible imagery with variable quality; and suppression of false alarms caused by sensor flaws and/or cutouts.« less

An Underwater Target Detection System for Electro-Optical Imagery Data

DTIC Science & Technology

2010-06-01

detection and segmentation of underwater mine-like objects in the EO images captured with a CCD-based image sensor. The main focus of this research is to...develop a robust detection algorithm that can be used to detect low contrast and partial underwater objects from the EO imagery with low false alarm rate...underwater target detection I. INTRODUCTION Automatic detection and recognition of underwater objects from EO imagery poses a serious challenge due to poor

Truncated feature representation for automatic target detection using transformed data-based decomposition

NASA Astrophysics Data System (ADS)

Riasati, Vahid R.

2016-05-01

In this work, the data covariance matrix is diagonalized to provide an orthogonal bases set using the eigen vectors of the data. The eigen-vector decomposition of the data is transformed and filtered in the transform domain to truncate the data for robust features related to a specified set of targets. These truncated eigen features are then combined and reconstructed to utilize in a composite filter and consequently utilized for the automatic target detection of the same class of targets. The results associated with the testing of the current technique are evaluated using the peak-correlation and peak-correlation energy metrics and are presented in this work. The inverse transformed eigen-bases of the current technique may be thought of as an injected sparsity to minimize data in representing the skeletal data structure information associated with the set of targets under consideration.

Key features for ATA / ATR database design in missile systems

NASA Astrophysics Data System (ADS)

Özertem, Kemal Arda

2017-05-01

Automatic target acquisition (ATA) and automatic target recognition (ATR) are two vital tasks for missile systems, and having a robust detection and recognition algorithm is crucial for overall system performance. In order to have a robust target detection and recognition algorithm, an extensive image database is required. Automatic target recognition algorithms use the database of images in training and testing steps of algorithm. This directly affects the recognition performance, since the training accuracy is driven by the quality of the image database. In addition, the performance of an automatic target detection algorithm can be measured effectively by using an image database. There are two main ways for designing an ATA / ATR database. The first and easy way is by using a scene generator. A scene generator can model the objects by considering its material information, the atmospheric conditions, detector type and the territory. Designing image database by using a scene generator is inexpensive and it allows creating many different scenarios quickly and easily. However the major drawback of using a scene generator is its low fidelity, since the images are created virtually. The second and difficult way is designing it using real-world images. Designing image database with real-world images is a lot more costly and time consuming; however it offers high fidelity, which is critical for missile algorithms. In this paper, critical concepts in ATA / ATR database design with real-world images are discussed. Each concept is discussed in the perspective of ATA and ATR separately. For the implementation stage, some possible solutions and trade-offs for creating the database are proposed, and all proposed approaches are compared to each other with regards to their pros and cons.

Automatically Detecting Likely Edits in Clinical Notes Created Using Automatic Speech Recognition

PubMed Central

Lybarger, Kevin; Ostendorf, Mari; Yetisgen, Meliha

2017-01-01

The use of automatic speech recognition (ASR) to create clinical notes has the potential to reduce costs associated with note creation for electronic medical records, but at current system accuracy levels, post-editing by practitioners is needed to ensure note quality. Aiming to reduce the time required to edit ASR transcripts, this paper investigates novel methods for automatic detection of edit regions within the transcripts, including both putative ASR errors but also regions that are targets for cleanup or rephrasing. We create detection models using logistic regression and conditional random field models, exploring a variety of text-based features that consider the structure of clinical notes and exploit the medical context. Different medical text resources are used to improve feature extraction. Experimental results on a large corpus of practitioner-edited clinical notes show that 67% of sentence-level edits and 45% of word-level edits can be detected with a false detection rate of 15%. PMID:29854187

Attention to baseline: does orienting visuospatial attention really facilitate target detection?

PubMed

Albares, Marion; Criaud, Marion; Wardak, Claire; Nguyen, Song Chi Trung; Ben Hamed, Suliann; Boulinguez, Philippe

2011-08-01

Standard protocols testing the orientation of visuospatial attention usually present spatial cues before targets and compare valid-cue trials with invalid-cue trials. The valid/invalid contrast results in a relative behavioral or physiological difference that is generally interpreted as a benefit of attention orientation. However, growing evidence suggests that inhibitory control of response is closely involved in this kind of protocol that requires the subjects to withhold automatic responses to cues, probably biasing behavioral and physiological baselines. Here, we used two experiments to disentangle the inhibitory control of automatic responses from orienting of visuospatial attention in a saccadic reaction time task in humans, a variant of the classical cue-target detection task and a sustained visuospatial attentional task. Surprisingly, when referring to a simple target detection task in which there is no need to refrain from reacting to avoid inappropriate responses, we found no consistent evidence of facilitation of target detection at the attended location. Instead, we observed a cost at the unattended location. Departing from the classical view, our results suggest that reaction time measures of visuospatial attention probably relie on the attenuation of elementary processes involved in visual target detection and saccade initiation away from the attended location rather than on facilitation at the attended location. This highlights the need to use proper control conditions in experimental designs to disambiguate relative from absolute cueing benefits on target detection reaction times, both in psychophysical and neurophysiological studies.

Automatic detection of multiple UXO-like targets using magnetic anomaly inversion and self-adaptive fuzzy c-means clustering

NASA Astrophysics Data System (ADS)

Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoquan; Li, Zhining

2017-12-01

We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.

Sub-surface defects detection of by using active thermography and advanced image edge detection

NASA Astrophysics Data System (ADS)

Tse, Peter W.; Wang, Gaochao

2017-05-01

Active or pulsed thermography is a popular non-destructive testing (NDT) tool for inspecting the integrity and anomaly of industrial equipment. One of the recent research trends in using active thermography is to automate the process in detecting hidden defects. As of today, human effort has still been using to adjust the temperature intensity of the thermo camera in order to visually observe the difference in cooling rates caused by a normal target as compared to that by a sub-surface crack exists inside the target. To avoid the tedious human-visual inspection and minimize human induced error, this paper reports the design of an automatic method that is capable of detecting subsurface defects. The method used the technique of active thermography, edge detection in machine vision and smart algorithm. An infrared thermo-camera was used to capture a series of temporal pictures after slightly heating up the inspected target by flash lamps. Then the Canny edge detector was employed to automatically extract the defect related images from the captured pictures. The captured temporal pictures were preprocessed by a packet of Canny edge detector and then a smart algorithm was used to reconstruct the whole sequences of image signals. During the processes, noise and irrelevant backgrounds exist in the pictures were removed. Consequently, the contrast of the edges of defective areas had been highlighted. The designed automatic method was verified by real pipe specimens that contains sub-surface cracks. After applying such smart method, the edges of cracks can be revealed visually without the need of using manual adjustment on the setting of thermo-camera. With the help of this automatic method, the tedious process in manually adjusting the colour contract and the pixel intensity in order to reveal defects can be avoided.

ATR Performance Estimation Seed Program

DTIC Science & Technology

2015-09-28

to produce simulated MCM sonar data and demonstrate the impact of system, environmental, and target scattering effects on ATR detection...settings and achieving better understanding the relative impact of the factors influencing ATR performance. sonar, mine countermeasures, MCM , automatic...simulated MCM sonar data and demonstrate the impact of system, environmental, and target scattering effects on ATR detection/classification performance. The

Infrared dim target detection based on visual attention

NASA Astrophysics Data System (ADS)

Wang, Xin; Lv, Guofang; Xu, Lizhong

2012-11-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. Based on human visual attention mechanisms, an automatic detection algorithm for infrared dim target is presented. After analyzing the characteristics of infrared dim target images, the method firstly designs Difference of Gaussians (DoG) filters to compute the saliency map. Then the salient regions where the potential targets exist in are extracted by searching through the saliency map with a control mechanism of winner-take-all (WTA) competition and inhibition-of-return (IOR). At last, these regions are identified by the characteristics of the dim IR targets, so the true targets are detected, and the spurious objects are rejected. The experiments are performed for some real-life IR images, and the results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

Infrared small target detection with kernel Fukunaga Koontz transform

NASA Astrophysics Data System (ADS)

Liu, Rui-ming; Liu, Er-qi; Yang, Jie; Zhang, Tian-hao; Wang, Fang-lin

2007-09-01

The Fukunaga-Koontz transform (FKT) has been proposed for many years. It can be used to solve two-pattern classification problems successfully. However, there are few researchers who have definitely extended FKT to kernel FKT (KFKT). In this paper, we first complete this task. Then a method based on KFKT is developed to detect infrared small targets. KFKT is a supervised learning algorithm. How to construct training sets is very important. For automatically detecting targets, the synthetic target images and real background images are used to train KFKT. Because KFKT can represent the higher order statistical properties of images, we expect better detection performance of KFKT than that of FKT. The well-devised experiments verify that KFKT outperforms FKT in detecting infrared small targets.

OPTICAL correlation identification technology applied in underwater laser imaging target identification

NASA Astrophysics Data System (ADS)

Yao, Guang-tao; Zhang, Xiao-hui; Ge, Wei-long

2012-01-01

The underwater laser imaging detection is an effective method of detecting short distance target underwater as an important complement of sonar detection. With the development of underwater laser imaging technology and underwater vehicle technology, the underwater automatic target identification has gotten more and more attention, and is a research difficulty in the area of underwater optical imaging information processing. Today, underwater automatic target identification based on optical imaging is usually realized with the method of digital circuit software programming. The algorithm realization and control of this method is very flexible. However, the optical imaging information is 2D image even 3D image, the amount of imaging processing information is abundant, so the electronic hardware with pure digital algorithm will need long identification time and is hard to meet the demands of real-time identification. If adopt computer parallel processing, the identification speed can be improved, but it will increase complexity, size and power consumption. This paper attempts to apply optical correlation identification technology to realize underwater automatic target identification. The optics correlation identification technology utilizes the Fourier transform characteristic of Fourier lens which can accomplish Fourier transform of image information in the level of nanosecond, and optical space interconnection calculation has the features of parallel, high speed, large capacity and high resolution, combines the flexibility of calculation and control of digital circuit method to realize optoelectronic hybrid identification mode. We reduce theoretical formulation of correlation identification and analyze the principle of optical correlation identification, and write MATLAB simulation program. We adopt single frame image obtained in underwater range gating laser imaging to identify, and through identifying and locating the different positions of target, we can improve the speed and orientation efficiency of target identification effectively, and validate the feasibility of this method primarily.

Automatic attentional orienting to other people's gaze in schizophrenia.

PubMed

Langdon, Robyn; Seymour, Kiley; Williams, Tracey; Ward, Philip B

2017-08-01

Explicit tests of social cognition have revealed pervasive deficits in schizophrenia. Less is known of automatic social cognition in schizophrenia. We used a spatial orienting task to investigate automatic shifts of attention cued by another person's eye gaze in 29 patients and 28 controls. Central photographic images of a face with eyes shifted left or right, or looking straight ahead, preceded targets that appeared left or right of the cue. To examine automatic effects, cue direction was non-predictive of target location. Cue-target intervals were 100, 300, and 800 ms. In non-social control trials, arrows replaced eye-gaze cues. Both groups showed automatic attentional orienting indexed by faster reaction times (RTs) when arrows were congruent with target location across all cue-target intervals. Similar congruency effects were seen for eye-shift cues at 300 and 800 ms intervals, but patients showed significantly larger congruency effects at 800 ms, which were driven by delayed responses to incongruent target locations. At short 100-ms cue-target intervals, neither group showed faster RTs for congruent than for incongruent eye-shift cues, but patients were significantly slower to detect targets after direct-gaze cues. These findings conflict with previous studies using schematic line drawings of eye-shifts that have found automatic attentional orienting to be reduced in schizophrenia. Instead, our data indicate that patients display abnormalities in responding to gaze direction at various stages of gaze processing-reflected by a stronger preferential capture of attention by another person's direct eye contact at initial stages of gaze processing and difficulties disengaging from a gazed-at location once shared attention is established.

Applications of independent component analysis in SAR images

NASA Astrophysics Data System (ADS)

Huang, Shiqi; Cai, Xinhua; Hui, Weihua; Xu, Ping

2009-07-01

The detection of faint, small and hidden targets in synthetic aperture radar (SAR) image is still an issue for automatic target recognition (ATR) system. How to effectively separate these targets from the complex background is the aim of this paper. Independent component analysis (ICA) theory can enhance SAR image targets and improve signal clutter ratio (SCR), which benefits to detect and recognize faint targets. Therefore, this paper proposes a new SAR image target detection algorithm based on ICA. In experimental process, the fast ICA (FICA) algorithm is utilized. Finally, some real SAR image data is used to test the method. The experimental results verify that the algorithm is feasible, and it can improve the SCR of SAR image and increase the detection rate for the faint small targets.

Application of passive imaging polarimetry in the discrimination and detection of different color targets of identical shapes using color-blind imaging sensors

NASA Astrophysics Data System (ADS)

El-Saba, A. M.; Alam, M. S.; Surpanani, A.

2006-05-01

Important aspects of automatic pattern recognition systems are their ability to efficiently discriminate and detect proper targets with low false alarms. In this paper we extend the applications of passive imaging polarimetry to effectively discriminate and detect different color targets of identical shapes using color-blind imaging sensor. For this case of study we demonstrate that traditional color-blind polarization-insensitive imaging sensors that rely only on the spatial distribution of targets suffer from high false detection rates, especially in scenarios where multiple identical shape targets are present. On the other hand we show that color-blind polarization-sensitive imaging sensors can successfully and efficiently discriminate and detect true targets based on their color only. We highlight the main advantages of using our proposed polarization-encoded imaging sensor.

A long-term target detection approach in infrared image sequence

NASA Astrophysics Data System (ADS)

Li, Hang; Zhang, Qi; Wang, Xin; Hu, Chao

2016-10-01

An automatic target detection method used in long term infrared (IR) image sequence from a moving platform is proposed. Firstly, based on POME(the principle of maximum entropy), target candidates are iteratively segmented. Then the real target is captured via two different selection approaches. At the beginning of image sequence, the genuine target with litter texture is discriminated from other candidates by using contrast-based confidence measure. On the other hand, when the target becomes larger, we apply online EM method to estimate and update the distributions of target's size and position based on the prior detection results, and then recognize the genuine one which satisfies both the constraints of size and position. Experimental results demonstrate that the presented method is accurate, robust and efficient.

Automatic Detection and Positioning of Ground Control Points Using TerraSAR-X Multiaspect Acquisitions

NASA Astrophysics Data System (ADS)

Montazeri, Sina; Gisinger, Christoph; Eineder, Michael; Zhu, Xiao xiang

2018-05-01

Geodetic stereo Synthetic Aperture Radar (SAR) is capable of absolute three-dimensional localization of natural Persistent Scatterer (PS)s which allows for Ground Control Point (GCP) generation using only SAR data. The prerequisite for the method to achieve high precision results is the correct detection of common scatterers in SAR images acquired from different viewing geometries. In this contribution, we describe three strategies for automatic detection of identical targets in SAR images of urban areas taken from different orbit tracks. Moreover, a complete work-flow for automatic generation of large number of GCPs using SAR data is presented and its applicability is shown by exploiting TerraSAR-X (TS-X) high resolution spotlight images over the city of Oulu, Finland and a test site in Berlin, Germany.

Automatic detection of solar features in HSOS full-disk solar images using guided filter

NASA Astrophysics Data System (ADS)

Yuan, Fei; Lin, Jiaben; Guo, Jingjing; Wang, Gang; Tong, Liyue; Zhang, Xinwei; Wang, Bingxiang

2018-02-01

A procedure is introduced for the automatic detection of solar features using full-disk solar images from Huairou Solar Observing Station (HSOS), National Astronomical Observatories of China. In image preprocessing, median filter is applied to remove the noises. Guided filter is adopted to enhance the edges of solar features and restrain the solar limb darkening, which is first introduced into the astronomical target detection. Then specific features are detected by Otsu algorithm and further threshold processing technique. Compared with other automatic detection procedures, our procedure has some advantages such as real time and reliability as well as no need of local threshold. Also, it reduces the amount of computation largely, which is benefited from the efficient guided filter algorithm. The procedure has been tested on one month sequences (December 2013) of HSOS full-disk solar images and the result shows that the number of features detected by our procedure is well consistent with the manual one.

Automation in visual inspection tasks: X-ray luggage screening supported by a system of direct, indirect or adaptable cueing with low and high system reliability.

PubMed

Chavaillaz, Alain; Schwaninger, Adrian; Michel, Stefan; Sauer, Juergen

2018-05-25

The present study evaluated three automation modes for improving performance in an X-ray luggage screening task. 140 participants were asked to detect the presence of prohibited items in X-ray images of cabin luggage. Twenty participants conducted this task without automatic support (control group), whereas the others worked with either indirect cues (system indicated the target presence without specifying its location), or direct cues (system pointed out the exact target location) or adaptable automation (participants could freely choose between no cue, direct and indirect cues). Furthermore, automatic support reliability was manipulated (low vs. high). The results showed a clear advantage for direct cues regarding detection performance and response time. No benefits were observed for adaptable automation. Finally, high automation reliability led to better performance and higher operator trust. The findings overall confirmed that automatic support systems for luggage screening should be designed such that they provide direct, highly reliable cues.

Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images

NASA Astrophysics Data System (ADS)

Doshi, Trushali; Soraghan, John; Grose, Derek; MacKenzie, Kenneth; Petropoulakis, Lykourgos

2015-03-01

Detection of larynx cancer from medical imaging is important for the quantification and for the definition of target volumes in radiotherapy treatment planning (RTP). Magnetic resonance imaging (MRI) is being increasingly used in RTP due to its high resolution and excellent soft tissue contrast. Manually detecting larynx cancer from sequential MRI is time consuming and subjective. The large diversity of cancer in terms of geometry, non-distinct boundaries combined with the presence of normal anatomical regions close to the cancer regions necessitates the development of automatic and robust algorithms for this task. A new automatic algorithm for the detection of larynx cancer from 2D gadoliniumenhanced T1-weighted (T1+Gd) MRI to assist clinicians in RTP is presented. The algorithm employs edge detection using spatial neighborhood information of pixels and incorporates this information in a fuzzy c-means clustering process to robustly separate different tissues types. Furthermore, it utilizes the information of the expected cancerous location for cancer regions labeling. Comparison of this automatic detection system with manual clinical detection on real T1+Gd axial MRI slices of 2 patients (24 MRI slices) with visible larynx cancer yields an average dice similarity coefficient of 0.78+/-0.04 and average root mean square error of 1.82+/-0.28 mm. Preliminary results show that this fully automatic system can assist clinicians in RTP by obtaining quantifiable and non-subjective repeatable detection results in a particular time-efficient and unbiased fashion.

User acceptance of intelligent avionics: A study of automatic-aided target recognition

NASA Technical Reports Server (NTRS)

Becker, Curtis A.; Hayes, Brian C.; Gorman, Patrick C.

1991-01-01

User acceptance of new support systems typically was evaluated after the systems were specified, designed, and built. The current study attempts to assess user acceptance of an Automatic-Aided Target Recognition (ATR) system using an emulation of such a proposed system. The detection accuracy and false alarm level of the ATR system were varied systematically, and subjects rated the tactical value of systems exhibiting different performance levels. Both detection accuracy and false alarm level affected the subjects' ratings. The data from two experiments suggest a cut-off point in ATR performance below which the subjects saw little tactical value in the system. An ATR system seems to have obvious tactical value only if it functions at a correct detection rate of 0.7 or better with a false alarm level of 0.167 false alarms per square degree or fewer.

Results from Automated Cloud and Dust Devil Detection Onboard the MER

NASA Technical Reports Server (NTRS)

Chien, Steve; Castano, Rebecca; Bornstein, Benjamin; Fukunaga, Alex; Castano, Andres; Biesiadecki, Jeffrey; Greeley, Ron; Whelley, Patrick; Lemmon, Mark

2008-01-01

We describe a new capability to automatically detect dust devils and clouds in imagery onboard rovers, enabling downlink of just the images with the targets or only portions of the images containing the targets. Previously, the MER rovers conducted campaigns to image dust devils and clouds by commanding a set of images be collected at fixed times and downloading the entire image set. By increasing the efficiency of the campaigns, more campaigns can be executed. Software for these new capabilities was developed, tested, integrated, uploaded, and operationally checked out on both rovers as part of the R9.2 software upgrade. In April 2007 on Sol 1147 a dust devil was automatically detected onboard the Spirit rover for the first time. We discuss the operational usage of the capability and present initial dust devil results showing how this preliminary application has demonstrated the feasibility and potential benefits of the approach.

Automatic updating and 3D modeling of airport information from high resolution images using GIS and LIDAR data

NASA Astrophysics Data System (ADS)

Lv, Zheng; Sui, Haigang; Zhang, Xilin; Huang, Xianfeng

2007-11-01

As one of the most important geo-spatial objects and military establishment, airport is always a key target in fields of transportation and military affairs. Therefore, automatic recognition and extraction of airport from remote sensing images is very important and urgent for updating of civil aviation and military application. In this paper, a new multi-source data fusion approach on automatic airport information extraction, updating and 3D modeling is addressed. Corresponding key technologies including feature extraction of airport information based on a modified Ostu algorithm, automatic change detection based on new parallel lines-based buffer detection algorithm, 3D modeling based on gradual elimination of non-building points algorithm, 3D change detecting between old airport model and LIDAR data, typical CAD models imported and so on are discussed in detail. At last, based on these technologies, we develop a prototype system and the results show our method can achieve good effects.

Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller.

PubMed

Cruz, Aniana; Pires, Gabriel; Nunes, Urbano J

2018-01-01

Brain-computer interface (BCI) is a useful device for people with severe motor disabilities. However, due to its low speed and low reliability, BCI still has a very limited application in daily real-world tasks. This paper proposes a P300-based BCI speller combined with a double error-related potential (ErrP) detection to automatically correct erroneous decisions. This novel approach introduces a second error detection to infer whether wrong automatic correction also elicits a second ErrP. Thus, two single-trial responses, instead of one, contribute to the final selection, improving the reliability of error detection. Moreover, to increase error detection, the evoked potential detected as target by the P300 classifier is combined with the evoked error potential at a feature-level. Discriminable error and positive potentials (response to correct feedback) were clearly identified. The proposed approach was tested on nine healthy participants and one tetraplegic participant. The online average accuracy for the first and second ErrPs were 88.4% and 84.8%, respectively. With automatic correction, we achieved an improvement around 5% achieving 89.9% in spelling accuracy for an effective 2.92 symbols/min. The proposed approach revealed that double ErrP detection can improve the reliability and speed of BCI systems.

Marine Targets Detection in Pol-SAR Data

NASA Astrophysics Data System (ADS)

Chen, Peng; Yang, Jingsong

2016-08-01

In this poster, we present a new method of marine target detection in Pol-SAR data. One band SAR image, like HH, VV or VH, can be used to find marine target using a Contant False Alarm Ratio (CFAR) algorithm. But some false detection may happen, as the sidelobe of antenna, Azimuth ambiguity, strong speckle noise and so on in the single band SAR image. Pol-SAR image can get more information of targets. After decomposition and false color composite, the sidelobe of antenna and Azimuth ambiguity could be deleted. So, the method presented include three steps, decomposion, false color composite and supervised classification. The result of Radarsat-2 SAR image test indicates a good accuracy. The detection results are compared with Automatic Indentify Sistem (AIS) data, the accuracy of right detection is above 95% and false detection ratio is below 5%.

Musical space synesthesia: automatic, explicit and conceptual connections between musical stimuli and space.

PubMed

Akiva-Kabiri, Lilach; Linkovski, Omer; Gertner, Limor; Henik, Avishai

2014-08-01

In musical-space synesthesia, musical pitches are perceived as having a spatially defined array. Previous studies showed that symbolic inducers (e.g., numbers, months) can modulate response according to the inducer's relative position on the synesthetic spatial form. In the current study we tested two musical-space synesthetes and a group of matched controls on three different tasks: musical-space mapping, spatial cue detection and a spatial Stroop-like task. In the free mapping task, both synesthetes exhibited a diagonal organization of musical pitch tones rising from bottom left to the top right. This organization was found to be consistent over time. In the subsequent tasks, synesthetes were asked to ignore an auditory or visually presented musical pitch (irrelevant information) and respond to a visual target (i.e., an asterisk) on the screen (relevant information). Compatibility between musical pitch and the target's spatial location was manipulated to be compatible or incompatible with the synesthetes' spatial representations. In the spatial cue detection task participants had to press the space key immediately upon detecting the target. In the Stroop-like task, they had to reach the target by using a mouse cursor. In both tasks, synesthetes' performance was modulated by the compatibility between irrelevant and relevant spatial information. Specifically, the target's spatial location conflicted with the spatial information triggered by the irrelevant musical stimulus. These results reveal that for musical-space synesthetes, musical information automatically orients attention according to their specific spatial musical-forms. The present study demonstrates the genuineness of musical-space synesthesia by revealing its two hallmarks-automaticity and consistency. In addition, our results challenge previous findings regarding an implicit vertical representation for pitch tones in non-synesthete musicians. Copyright © 2014 Elsevier Inc. All rights reserved.

DBSCAN-based ROI extracted from SAR images and the discrimination of multi-feature ROI

NASA Astrophysics Data System (ADS)

He, Xin Yi; Zhao, Bo; Tan, Shu Run; Zhou, Xiao Yang; Jiang, Zhong Jin; Cui, Tie Jun

2009-10-01

The purpose of the paper is to extract the region of interest (ROI) from the coarse detected synthetic aperture radar (SAR) images and discriminate if the ROI contains a target or not, so as to eliminate the false alarm, and prepare for the target recognition. The automatic target clustering is one of the most difficult tasks in the SAR-image automatic target recognition system. The density-based spatial clustering of applications with noise (DBSCAN) relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN was first used in the SAR image processing, which has many excellent features: only two insensitivity parameters (radius of neighborhood and minimum number of points) are needed; clusters of arbitrary shapes which fit in with the coarse detected SAR images can be discovered; and the calculation time and memory can be reduced. In the multi-feature ROI discrimination scheme, we extract several target features which contain the geometry features such as the area discriminator and Radon-transform based target profile discriminator, the distribution characteristics such as the EFF discriminator, and the EM scattering property such as the PPR discriminator. The synthesized judgment effectively eliminates the false alarms.

Acute Alcohol Consumption Impairs Controlled but Not Automatic Processes in a Psychophysical Pointing Paradigm

PubMed Central

Johnston, Kevin; Timney, Brian; Goodale, Melvyn A.

2013-01-01

Numerous studies have investigated the effects of alcohol consumption on controlled and automatic cognitive processes. Such studies have shown that alcohol impairs performance on tasks requiring conscious, intentional control, while leaving automatic performance relatively intact. Here, we sought to extend these findings to aspects of visuomotor control by investigating the effects of alcohol in a visuomotor pointing paradigm that allowed us to separate the influence of controlled and automatic processes. Six male participants were assigned to an experimental “correction” condition in which they were instructed to point at a visual target as quickly and accurately as possible. On a small percentage of trials, the target “jumped” to a new location. On these trials, the participants’ task was to amend their movement such that they pointed to the new target location. A second group of 6 participants were assigned to a “countermanding” condition, in which they were instructed to terminate their movements upon detection of target “jumps”. In both the correction and countermanding conditions, participants served as their own controls, taking part in alcohol and no-alcohol conditions on separate days. Alcohol had no effect on participants’ ability to correct movements “in flight”, but impaired the ability to withhold such automatic corrections. Our data support the notion that alcohol selectively impairs controlled processes in the visuomotor domain. PMID:23861934

Figure-ground assignment in pigeons: evidence for a figural benefit.

PubMed

Lazareva, Olga E; Castro, Leyre; Vecera, Shaun P; Wasserman, Edward A

2006-07-01

Four pigeons discriminated whether a target spot appeared on a colored figural shape or on a differently colored background by first pecking the target and then reporting its location: on the figure or the background. We recorded three dependent variables: target detection time, choice response time, and choice accuracy. The birds were faster to detect the target, to report its location, and to learn the correct response on figure trials than on background trials. Later tests suggested that the pigeons might have attended to the figural region as a whole rather than using local properties in performing the figure-background discrimination. The location of the figural region did not affect figure-ground assignment. Finally, when 4 other pigeons had to detect and peck the target without making a choice report, no figural advantage emerged in target detection time, suggesting that the birds' attention may not have been automatically summoned to the figural region.

[Micron]ADS-B Detect and Avoid Flight Tests on Phantom 4 Unmanned Aircraft System

NASA Technical Reports Server (NTRS)

Arteaga, Ricardo; Dandachy, Mike; Truong, Hong; Aruljothi, Arun; Vedantam, Mihir; Epperson, Kraettli; McCartney, Reed

2018-01-01

Researchers at the National Aeronautics and Space Administration Armstrong Flight Research Center in Edwards, California and Vigilant Aerospace Systems collaborated for the flight-test demonstration of an Automatic Dependent Surveillance-Broadcast based collision avoidance technology on a small unmanned aircraft system equipped with the uAvionix Automatic Dependent Surveillance-Broadcast transponder. The purpose of the testing was to demonstrate that National Aeronautics and Space Administration / Vigilant software and algorithms, commercialized as the FlightHorizon UAS"TM", are compatible with uAvionix hardware systems and the DJI Phantom 4 small unmanned aircraft system. The testing and demonstrations were necessary for both parties to further develop and certify the technology in three key areas: flights beyond visual line of sight, collision avoidance, and autonomous operations. The National Aeronautics and Space Administration and Vigilant Aerospace Systems have developed and successfully flight-tested an Automatic Dependent Surveillance-Broadcast Detect and Avoid system on the Phantom 4 small unmanned aircraft system. The Automatic Dependent Surveillance-Broadcast Detect and Avoid system architecture is especially suited for small unmanned aircraft systems because it integrates: 1) miniaturized Automatic Dependent Surveillance-Broadcast hardware; 2) radio data-link communications; 3) software algorithms for real-time Automatic Dependent Surveillance-Broadcast data integration, conflict detection, and alerting; and 4) a synthetic vision display using a fully-integrated National Aeronautics and Space Administration geobrowser for three dimensional graphical representations for ownship and air traffic situational awareness. The flight-test objectives were to evaluate the performance of Automatic Dependent Surveillance-Broadcast Detect and Avoid collision avoidance technology as installed on two small unmanned aircraft systems. In December 2016, four flight tests were conducted at Edwards Air Force Base. Researchers in the ground control station looking at displays were able to verify the Automatic Dependent Surveillance-Broadcast target detection and collision avoidance resolutions.

A long-term target detection approach in infrared image sequence

NASA Astrophysics Data System (ADS)

Li, Hang; Zhang, Qi; Li, Yuanyuan; Wang, Liqiang

2015-12-01

An automatic target detection method used in long term infrared (IR) image sequence from a moving platform is proposed. Firstly, based on non-linear histogram equalization, target candidates are coarse-to-fine segmented by using two self-adapt thresholds generated in the intensity space. Then the real target is captured via two different selection approaches. At the beginning of image sequence, the genuine target with litter texture is discriminated from other candidates by using contrast-based confidence measure. On the other hand, when the target becomes larger, we apply online EM method to iteratively estimate and update the distributions of target's size and position based on the prior detection results, and then recognize the genuine one which satisfies both the constraints of size and position. Experimental results demonstrate that the presented method is accurate, robust and efficient.

[Study of automatic marine oil spills detection using imaging spectroscopy].

PubMed

Liu, De-Lian; Han, Liang; Zhang, Jian-Qi

2013-11-01

To reduce artificial auxiliary works in oil spills detection process, an automatic oil spill detection method based on adaptive matched filter is presented. Firstly, the characteristics of reflectance spectral signature of C-H bond in oil spill are analyzed. And an oil spill spectral signature extraction model is designed by using the spectral feature of C-H bond. It is then used to obtain the reference spectral signature for the following oil spill detection step. Secondly, the characteristics of reflectance spectral signature of sea water, clouds, and oil spill are compared. The bands which have large difference in reflectance spectral signatures of the sea water, clouds, and oil spill are selected. By using these bands, the sea water pixels are segmented. And the background parameters are then calculated. Finally, the classical adaptive matched filter from target detection algorithms is improved and introduced for oil spill detection. The proposed method is applied to the real airborne visible infrared imaging spectrometer (AVIRIS) hyperspectral image captured during the deepwater horizon oil spill in the Gulf of Mexico for oil spill detection. The results show that the proposed method has, high efficiency, does not need artificial auxiliary work, and can be used for automatic detection of marine oil spill.

Contrast, size, and orientation-invariant target detection in infrared imagery

NASA Astrophysics Data System (ADS)

Zhou, Yi-Tong; Crawshaw, Richard D.

1991-08-01

Automatic target detection in IR imagery is a very difficult task due to variations in target brightness, shape, size, and orientation. In this paper, the authors present a contrast, size, and orientation invariant algorithm based on Gabor functions for detecting targets from a single IR image frame. The algorithms consists of three steps. First, it locates potential targets by using low-resolution Gabor functions which resist noise and background clutter effects, then, it removes false targets and eliminates redundant target points based on a similarity measure. These two steps mimic human vision processing but are different from Zeevi's Foveating Vision System. Finally, it uses both low- and high-resolution Gabor functions to verify target existence. This algorithm has been successfully tested on several IR images that contain multiple examples of military vehicles with different size and brightness in various background scenes and orientations.

Top-attack modeling and automatic target detection using synthetic FLIR scenery

NASA Astrophysics Data System (ADS)

Weber, Bruce A.; Penn, Joseph A.

2004-09-01

A series of experiments have been performed to verify the utility of algorithmic tools for the modeling and analysis of cold-target signatures in synthetic, top-attack, FLIR video sequences. The tools include: MuSES/CREATION for the creation of synthetic imagery with targets, an ARL target detection algorithm to detect imbedded synthetic targets in scenes, and an ARL scoring algorithm, using Receiver-Operating-Characteristic (ROC) curve analysis, to evaluate detector performance. Cold-target detection variability was examined as a function of target emissivity, surrounding clutter type, and target placement in non-obscuring clutter locations. Detector metrics were also individually scored so as to characterize the effect of signature/clutter variations. Results show that using these tools, a detailed, physically meaningful, target detection analysis is possible and that scenario specific target detectors may be developed by selective choice and/or weighting of detector metrics. However, developing these tools into a reliable predictive capability will require the extension of these results to the modeling and analysis of a large number of data sets configured for a wide range of target and clutter conditions. Finally, these tools should also be useful for the comparison of competitive detection algorithms by providing well defined, and controllable target detection scenarios, as well as for the training and testing of expert human observers.

Attention capture by contour onsets and offsets: no special role for onsets.

PubMed

Watson, D G; Humphreys, G W

1995-07-01

In five experiments, we investigated the power of targets defined by the onset or offset of one of an object's parts (contour onsets and offsets) either to guide or to capture visual attention. In Experiment 1, search for a single contour onset target was compared with search for a single contour offset target against a static background of distractors; no difference was found between the efficiency with which each could be detected. In Experiment 2, onsets and offsets were compared for automatic attention capture, when both occurred simultaneously. Unlike in previous studies, the effects of overall luminance change, new-object creation, and number of onset and offset items were controlled. It was found that contour onset and offset items captured attention equally well. However, display size effects on both target types were also apparent. Such effects may have been due to competition for selection between multiple onset and offset stimuli. In Experiments 3 and 4, single onset and offset stimuli were presented simultaneously and pitted directly against one another among a background of static distractors. In Experiment 3, we examined "guided search," for a target that was formed either from an onset or from an offset among static items. In Experiment 4, the onsets and offsets were uncorrelated with the target location. Similar results occurred in both experiments: target onsets and offsets were detected more efficiently than static stimuli which needed serial search; there remained effects of display size on performance; but there was still no advantage for onsets. In Experiment 5, we examined automatic attention capture by single onset and offset stimuli presented individually among static distractors. Again, there was no advantage for onset over offset targets and a display size effect was also present. These results suggest that, both in isolation and in competition, onsets that do not form new objects neither guide nor gain automatic attention more efficiently than offsets. In addition, in contrast to previous studies in which onsets formed new objects, contour onsets and offsets did not reliably capture attention automatically.

An Experiment Quantifying The Effect Of Clutter On Target Detection

NASA Astrophysics Data System (ADS)

Weathersby, Marshall R.; Schmieder, David E.

1985-01-01

Experiments were conducted to determine the influence of background clutter on target detection criteria. The experiment consisted of placing observers in front of displayed images on a TV monitor. Observer ability to detect military targets embedded in simulated natural and manmade background clutter was measured when there was unlimited viewing time. Results were described in terms of detection probability versus target resolution for various signal to clutter ratios (SCR). The experiments were preceded by a search for a meaningful clutter definition. The selected definition was a statistical measure computed by averaging the standard deviation of contiguous scene cells over the whole scene. The cell size was comparable to the target size. Observer test results confirmed the expectation that the resolution required for a given detection probability was a continuum function of the clutter level. At the lower SCRs the resolution required for a high probability of detection was near 6 lines pairs per target (LP/TGT), while at the higher SCRs it was found that a resolution of less than 0.25 LP/TGT would yield a high probability of detection. These results are expected to aid in target acquisition performance modeling and to lead to improved specifications for imaging automatic target screeners.

Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map.

PubMed

Tan, Yihua; Li, Qingyun; Li, Yansheng; Tian, Jinwen

2015-09-11

This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient textural saliency map under the contextual cues of apron area. Firstly, the candidate regions with the possible existence of airport are detected from the apron area. Secondly, directional local gradient distribution detector is used to obtain a gradient textural saliency map in the favor of the candidate regions. In addition, the final targets will be detected by segmenting the saliency map using CFAR-type algorithm. The real high-resolution airborne SAR image data is used to verify the proposed algorithm. The results demonstrate that this algorithm can detect aircraft targets quickly and accurately, and decrease the false alarm rate.

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%.

RoboTAP: Target priorities for robotic microlensing observations

NASA Astrophysics Data System (ADS)

Hundertmark, M.; Street, R. A.; Tsapras, Y.; Bachelet, E.; Dominik, M.; Horne, K.; Bozza, V.; Bramich, D. M.; Cassan, A.; D'Ago, G.; Figuera Jaimes, R.; Kains, N.; Ranc, C.; Schmidt, R. W.; Snodgrass, C.; Wambsganss, J.; Steele, I. A.; Mao, S.; Ment, K.; Menzies, J.; Li, Z.; Cross, S.; Maoz, D.; Shvartzvald, Y.

2018-01-01

Context. The ability to automatically select scientifically-important transient events from an alert stream of many such events, and to conduct follow-up observations in response, will become increasingly important in astronomy. With wide-angle time domain surveys pushing to fainter limiting magnitudes, the capability to follow-up on transient alerts far exceeds our follow-up telescope resources, and effective target prioritization becomes essential. The RoboNet-II microlensing program is a pathfinder project, which has developed an automated target selection process (RoboTAP) for gravitational microlensing events, which are observed in real time using the Las Cumbres Observatory telescope network. Aims: Follow-up telescopes typically have a much smaller field of view compared to surveys, therefore the most promising microlensing events must be automatically selected at any given time from an annual sample exceeding 2000 events. The main challenge is to select between events with a high planet detection sensitivity, with the aim of detecting many planets and characterizing planetary anomalies. Methods: Our target selection algorithm is a hybrid system based on estimates of the planet detection zones around a microlens. It follows automatic anomaly alerts and respects the expected survey coverage of specific events. Results: We introduce the RoboTAP algorithm, whose purpose is to select and prioritize microlensing events with high sensitivity to planetary companions. In this work, we determine the planet sensitivity of the RoboNet follow-up program and provide a working example of how a broker can be designed for a real-life transient science program conducting follow-up observations in response to alerts; we explore the issues that will confront similar programs being developed for the Large Synoptic Survey Telescope (LSST) and other time domain surveys.

Figural properties are prioritized for search under conditions of uncertainty: Setting boundary conditions on claims that figures automatically attract attention.

PubMed

Peterson, Mary A; Mojica, Andrew J; Salvagio, Elizabeth; Kimchi, Ruth

2017-01-01

Nelson and Palmer (2007) concluded that figures/figural properties automatically attract attention, after they found that participants were faster to detect/discriminate targets appearing where a portion of a familiar object was suggested in an otherwise ambiguous display. We investigated whether these effects are truly automatic and whether they generalize to another figural property-convexity. We found that Nelson and Palmer's results do generalize to convexity, but only when participants are uncertain regarding when and where the target will appear. Dependence on uncertainty regarding target location/timing was also observed for familiarity. Thus, although we could replicate and extend Nelson and Palmer's results, our experiments showed that figures do not automatically draw attention. In addition, our research went beyond Nelson and Palmer's, in that we were able to separate figural properties from perceived figures. Because figural properties are regularities that predict where objects lie in the visual field, our results join other evidence that regularities in the environment can attract attention. More generally, our results are consistent with Bayesian theories in which priors are given more weight under conditions of uncertainty.

Detection performance in clutter with variable resolution

NASA Astrophysics Data System (ADS)

Schmieder, D. E.; Weathersby, M. R.

1983-07-01

Experiments were conducted to determine the influence of background clutter on target detection criteria. The experiment consisted of placing observers in front of displayed images on a TV monitor. Observer ability to detect military targets embedded in simulated natural and manmade background clutter was measured when there was unlimited viewing time. Results were described in terms of detection probability versus target resolution for various signal to clutter ratios (SCR). The experiments were preceded by a search for a meaningful clutter definition. The selected definition was a statistical measure computed by averaging the standard deviation of contiguous scene cells over the whole scene. The cell size was comparable to the target size. Observer test results confirmed the expectation that the resolution required for a given detection probability was a continuum function of the clutter level. At the lower SCRs the resolution required for a high probability of detection was near 6 line pairs per target (LP/TGT), while at the higher SCRs it was found that a resoluton of less than 0.25 LP/TGT would yield a high probability of detection. These results are expected to aid in target acquisition performance modeling and to lead to improved specifications for imaging automatic target screeners.

Ship detection in optical remote sensing images based on deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Yao, Yuan; Jiang, Zhiguo; Zhang, Haopeng; Zhao, Danpei; Cai, Bowen

2017-10-01

Automatic ship detection in optical remote sensing images has attracted wide attention for its broad applications. Major challenges for this task include the interference of cloud, wave, wake, and the high computational expenses. We propose a fast and robust ship detection algorithm to solve these issues. The framework for ship detection is designed based on deep convolutional neural networks (CNNs), which provide the accurate locations of ship targets in an efficient way. First, the deep CNN is designed to extract features. Then, a region proposal network (RPN) is applied to discriminate ship targets and regress the detection bounding boxes, in which the anchors are designed by intrinsic shape of ship targets. Experimental results on numerous panchromatic images demonstrate that, in comparison with other state-of-the-art ship detection methods, our method is more efficient and achieves higher detection accuracy and more precise bounding boxes in different complex backgrounds.

Morphological operators for enhanced polarimetric image target detection

NASA Astrophysics Data System (ADS)

Romano, João. M.; Rosario, Dalton S.

2015-09-01

We introduce an algorithm based on morphological filters with the Stokes parameters that augments the daytime and nighttime detection of weak-signal manmade objects immersed in a predominant natural background scene. The approach features a tailored sequence of signal-enhancing filters, consisting of core morphological operators (dilation, erosion) and higher level morphological operations (e.g., spatial gradient, opening, closing) to achieve a desired overarching goal. Using representative data from the SPICE database, the results show that the approach was able to automatically and persistently detect with a high confidence level the presence of three mobile military howitzer surrogates (targets) in natural clutter.

Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map

PubMed Central

Tan, Yihua; Li, Qingyun; Li, Yansheng; Tian, Jinwen

2015-01-01

This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient textural saliency map under the contextual cues of apron area. Firstly, the candidate regions with the possible existence of airport are detected from the apron area. Secondly, directional local gradient distribution detector is used to obtain a gradient textural saliency map in the favor of the candidate regions. In addition, the final targets will be detected by segmenting the saliency map using CFAR-type algorithm. The real high-resolution airborne SAR image data is used to verify the proposed algorithm. The results demonstrate that this algorithm can detect aircraft targets quickly and accurately, and decrease the false alarm rate. PMID:26378543

Automatic detection of sweep-meshable volumes

DOEpatents

Tautges,; Timothy J. , White; David, R [Pittsburgh, PA

2006-05-23

A method of and software for automatically determining whether a mesh can be generated by sweeping for a representation of a geometric solid comprising: classifying surface mesh schemes for surfaces of the representation locally using surface vertex types; grouping mappable and submappable surfaces of the representation into chains; computing volume edge types for the representation; recursively traversing surfaces of the representation and grouping the surfaces into source, target, and linking surface lists; and checking traversal direction when traversing onto linking surfaces.

a Target Aware Texture Mapping for Sculpture Heritage Modeling

NASA Astrophysics Data System (ADS)

Yang, C.; Zhang, F.; Huang, X.; Li, D.; Zhu, Y.

2017-08-01

In this paper, we proposed a target aware image to model registration method using silhouette as the matching clues. The target sculpture object in natural environment can be automatically detected from image with complex background with assistant of 3D geometric data. Then the silhouette can be automatically extracted and applied in image to model matching. Due to the user don't need to deliberately draw target area, the time consumption for precisely image to model matching operation can be greatly reduced. To enhance the function of this method, we also improved the silhouette matching algorithm to support conditional silhouette matching. Two experiments using a stone lion sculpture of Ming Dynasty and a potable relic in museum are given to evaluate the method we proposed. The method we proposed in this paper is extended and developed into a mature software applied in many culture heritage documentation projects.

Examining the robustness of automated aural classification of active sonar echoes.

PubMed

Murphy, Stefan M; Hines, Paul C

2014-02-01

Active sonar systems are used to detect underwater man-made objects of interest (targets) that are too quiet to be reliably detected with passive sonar. Performance of active sonar can be degraded by false alarms caused by echoes returned from geological seabed structures (clutter) in shallow regions. To reduce false alarms, a method of distinguishing target echoes from clutter echoes is required. Research has demonstrated that perceptual-based signal features similar to those employed in the human auditory system can be used to automatically discriminate between target and clutter echoes, thereby reducing the number of false alarms and improving sonar performance. An active sonar experiment on the Malta Plateau in the Mediterranean Sea was conducted during the Clutter07 sea trial and repeated during the Clutter09 sea trial. The dataset consists of more than 95,000 pulse-compressed echoes returned from two targets and many geological clutter objects. These echoes were processed using an automatic classifier that quantifies the timbre of each echo using a number of perceptual signal features. Using echoes from 2007, the aural classifier was trained to establish a boundary between targets and clutter in the feature space. Temporal robustness was then investigated by testing the classifier on echoes from the 2009 experiment.

Automated processing integrated with a microflow cytometer for pathogen detection in clinical matrices

PubMed Central

Golden, J.P.; Verbarg, J.; Howell, P.B.; Shriver-Lake, L.C.; Ligler, F.S.

2012-01-01

A spinning magnetic trap (MagTrap) for automated sample processing was integrated with a microflow cytometer capable of simultaneously detecting multiple targets to provide an automated sample-to-answer diagnosis in 40 min. After target capture on fluorescently coded magnetic microspheres, the magnetic trap automatically concentrated the fluorescently coded microspheres, separated the captured target from the sample matrix, and exposed the bound target sequentially to biotinylated tracer molecules and streptavidin-labeled phycoerythrin. The concentrated microspheres were then hydrodynamically focused in a microflow cytometer capable of 4-color analysis (two wavelengths for microsphere identification, one for light scatter to discriminate single microspheres and one for phycoerythrin bound to the target). A three-fold decrease in sample preparation time and an improved detection limit, independent of target preconcentration, was demonstrated for detection of Escherichia coli 0157:H7 using the MagTrap as compared to manual processing. Simultaneous analysis of positive and negative controls, along with the assay reagents specific for the target, was used to obtain dose–response curves, demonstrating the potential for quantification of pathogen load in buffer and serum. PMID:22960010

Automated processing integrated with a microflow cytometer for pathogen detection in clinical matrices.

PubMed

Golden, J P; Verbarg, J; Howell, P B; Shriver-Lake, L C; Ligler, F S

2013-02-15

A spinning magnetic trap (MagTrap) for automated sample processing was integrated with a microflow cytometer capable of simultaneously detecting multiple targets to provide an automated sample-to-answer diagnosis in 40 min. After target capture on fluorescently coded magnetic microspheres, the magnetic trap automatically concentrated the fluorescently coded microspheres, separated the captured target from the sample matrix, and exposed the bound target sequentially to biotinylated tracer molecules and streptavidin-labeled phycoerythrin. The concentrated microspheres were then hydrodynamically focused in a microflow cytometer capable of 4-color analysis (two wavelengths for microsphere identification, one for light scatter to discriminate single microspheres and one for phycoerythrin bound to the target). A three-fold decrease in sample preparation time and an improved detection limit, independent of target preconcentration, was demonstrated for detection of Escherichia coli 0157:H7 using the MagTrap as compared to manual processing. Simultaneous analysis of positive and negative controls, along with the assay reagents specific for the target, was used to obtain dose-response curves, demonstrating the potential for quantification of pathogen load in buffer and serum. Published by Elsevier B.V.

Sequential feature selection for detecting buried objects using forward looking ground penetrating radar

NASA Astrophysics Data System (ADS)

Shaw, Darren; Stone, Kevin; Ho, K. C.; Keller, James M.; Luke, Robert H.; Burns, Brian P.

2016-05-01

Forward looking ground penetrating radar (FLGPR) has the benefit of detecting objects at a significant standoff distance. The FLGPR signal is radiated over a large surface area and the radar signal return is often weak. Improving detection, especially for buried in road targets, while maintaining an acceptable false alarm rate remains to be a challenging task. Various kinds of features have been developed over the years to increase the FLGPR detection performance. This paper focuses on investigating the use of as many features as possible for detecting buried targets and uses the sequential feature selection technique to automatically choose the features that contribute most for improving performance. Experimental results using data collected at a government test site are presented.

A fuel-limited isothermal DNA machine for the sensitive detection of cellular deoxyribonucleoside triphosphates.

PubMed

Dong, Jiantong; Wu, Tongbo; Xiao, Yu; Xu, Lei; Fang, Simin; Zhao, Meiping

2016-09-29

A fuel-limited isothermal DNA machine has been built for the sensitive fluorescence detection of cellular deoxyribonucleoside triphosphates (dNTPs) at the fmol level, which greatly reduces the required sample cell number. Upon the input of the limiting target dNTP, the machine runs automatically at 37 °C without the need for higher temperature.

Change-Based Satellite Monitoring Using Broad Coverage and Targetable Sensing

NASA Technical Reports Server (NTRS)

Chien, Steve A.; Tran, Daniel Q.; Doubleday, Joshua R.; Doggett, Thomas

2013-01-01

A generic software framework analyzes data from broad coverage sweeps or general larger areas of interest. Change detection methods are used to extract subsets of directed swath areas that intersect areas of change. These areas are prioritized and allocated to targetable assets. This method is deployed in an automatic fashion, and has operated without human monitoring or intervention for sustained periods of time (months).

A new method of inshore ship detection in high-resolution optical remote sensing images

NASA Astrophysics Data System (ADS)

Hu, Qifeng; Du, Yaling; Jiang, Yunqiu; Ming, Delie

2015-10-01

Ship as an important military target and water transportation, of which the detection has great significance. In the military field, the automatic detection of ships can be used to monitor ship dynamic in the harbor and maritime of enemy, and then analyze the enemy naval power. In civilian field, the automatic detection of ships can be used in monitoring transportation of harbor and illegal behaviors such as illegal fishing, smuggling and pirates, etc. In recent years, research of ship detection is mainly concentrated in three categories: forward-looking infrared images, downward-looking SAR image, and optical remote sensing images with sea background. Little research has been done into ship detection of optical remote sensing images with harbor background, as the gray-scale and texture features of ships are similar to the coast in high-resolution optical remote sensing images. In this paper, we put forward an effective harbor ship target detection method. First of all, in order to overcome the shortage of the traditional difference method in obtaining histogram valley as the segmentation threshold, we propose an iterative histogram valley segmentation method which separates the harbor and ships from the water quite well. Secondly, as landing ships in optical remote sensing images usually lead to discontinuous harbor edges, we use Hough Transform method to extract harbor edges. First, lines are detected by Hough Transform. Then, lines that have similar slope are connected into a new line, thus we access continuous harbor edges. Secondary segmentation on the result of the land-and-sea separation, we eventually get the ships. At last, we calculate the aspect ratio of the ROIs, thereby remove those targets which are not ship. The experiment results show that our method has good robustness and can tolerate a certain degree of noise and occlusion.

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

PubMed Central

Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

2016-01-01

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE. PMID:27447635

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection.

PubMed

Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

2016-07-19

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE.

An Algorithm to Detect the Retinal Region of Interest

NASA Astrophysics Data System (ADS)

Şehirli, E.; Turan, M. K.; Demiral, E.

2017-11-01

Retina is one of the important layers of the eyes, which includes sensitive cells to colour and light and nerve fibers. Retina can be displayed by using some medical devices such as fundus camera, ophthalmoscope. Hence, some lesions like microaneurysm, haemorrhage, exudate with many diseases of the eye can be detected by looking at the images taken by devices. In computer vision and biomedical areas, studies to detect lesions of the eyes automatically have been done for a long time. In order to make automated detections, the concept of ROI may be utilized. ROI which stands for region of interest generally serves the purpose of focusing on particular targets. The main concentration of this paper is the algorithm to automatically detect retinal region of interest belonging to different retinal images on a software application. The algorithm consists of three stages such as pre-processing stage, detecting ROI on processed images and overlapping between input image and obtained ROI of the image.

Automatic lung nodule graph cuts segmentation with deep learning false positive reduction

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Huang, Xia; Tseng, Tzu-Liang Bill; Qian, Wei

2017-03-01

To automatic detect lung nodules from CT images, we designed a two stage computer aided detection (CAD) system. The first stage is graph cuts segmentation to identify and segment the nodule candidates, and the second stage is convolutional neural network for false positive reduction. The dataset contains 595 CT cases randomly selected from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) and the 305 pulmonary nodules achieved diagnosis consensus by all four experienced radiologists were our detection targets. Consider each slice as an individual sample, 2844 nodules were included in our database. The graph cuts segmentation was conducted in a two-dimension manner, 2733 lung nodule ROIs are successfully identified and segmented. With a false positive reduction by a seven-layer convolutional neural network, 2535 nodules remain detected while the false positive dropped to 31.6%. The average F-measure of segmented lung nodule tissue is 0.8501.

Research on regional intrusion prevention and control system based on target tracking

NASA Astrophysics Data System (ADS)

Liu, Yanfei; Wang, Jieling; Jiang, Ke; He, Yanhui; Wu, Zhilin

2017-08-01

In view of the fact that China’s border is very long and the border prevention and control measures are single, we designed a regional intrusion prevention and control system which based on target-tracking. The system consists of four parts: solar panel, radar, electro-optical equipment, unmanned aerial vehicle and intelligent tracking platform. The solar panel provides independent power for the entire system. The radar detects the target in real time and realizes the high precision positioning of suspicious targets, then through the linkage of electro-optical equipment, it can achieve full-time automatic precise tracking of targets. When the target appears within the range of detection, the drone will be launched to continue the tracking. The system is mainly to realize the full time, full coverage, whole process integration and active realtime control of the border area.

The analysis of selected orientation methods of architectural objects' scans

NASA Astrophysics Data System (ADS)

Markiewicz, Jakub S.; Kajdewicz, Irmina; Zawieska, Dorota

2015-05-01

The terrestrial laser scanning is commonly used in different areas, inter alia in modelling architectural objects. One of the most important part of TLS data processing is scans registration. It significantly affects the accuracy of generation of high resolution photogrammetric documentation. This process is time consuming, especially in case of a large number of scans. It is mostly based on an automatic detection and a semi-automatic measurement of control points placed on the object. In case of the complicated historical buildings, sometimes it is forbidden to place survey targets on an object or it may be difficult to distribute survey targets in the optimal way. Such problems encourage the search for the new methods of scan registration which enable to eliminate the step of placing survey targets on the object. In this paper the results of target-based registration method are presented The survey targets placed on the walls of historical chambers of the Museum of King Jan III's Palace at Wilanów and on the walls of ruins of the Bishops Castle in Iłża were used for scan orientation. Several variants of orientation were performed, taking into account different placement and different number of survey marks. Afterwards, during next research works, raster images were generated from scans and the SIFT and SURF algorithms for image processing were used to automatically search for corresponding natural points. The case of utilisation of automatically identified points for TLS data orientation was analysed. The results of both methods for TLS data registration were summarized and presented in numerical and graphical forms.

A saliency-based approach to detection of infrared target

NASA Astrophysics Data System (ADS)

Chen, Yanfei; Sang, Nong; Dan, Zhiping

2013-10-01

Automatic target detection in infrared images is a hot research field of national defense technology. We propose a new saliency-based infrared target detection model in this paper, which is based on the fact that human focus of attention is directed towards the relevant target to interpret the most promising information. For a given image, the convolution of the image log amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale is equivalent to an image saliency detector in the frequency domain. At the same time, orientation and shape features extracted are combined into a saliency map in the spatial domain. Our proposed model decides salient targets based on a final saliency map, which is generated by integration of the saliency maps in the frequency and spatial domain. At last, the size of each salient target is obtained by maximizing entropy of the final saliency map. Experimental results show that the proposed model can highlight both small and large salient regions in infrared image, as well as inhibit repeated distractors in cluttered image. In addition, its detecting efficiency has improved significantly.

Automatic detection of anatomical regions in frontal x-ray images: comparing convolutional neural networks to random forest

NASA Astrophysics Data System (ADS)

Olory Agomma, R.; Vázquez, C.; Cresson, T.; De Guise, J.

2018-02-01

Most algorithms to detect and identify anatomical structures in medical images require either to be initialized close to the target structure, or to know that the structure is present in the image, or to be trained on a homogeneous database (e.g. all full body or all lower limbs). Detecting these structures when there is no guarantee that the structure is present in the image, or when the image database is heterogeneous (mixed configurations), is a challenge for automatic algorithms. In this work we compared two state-of-the-art machine learning techniques in order to determine which one is the most appropriate for predicting targets locations based on image patches. By knowing the position of thirteen landmarks points, labelled by an expert in EOS frontal radiography, we learn the displacement between salient points detected in the image and these thirteen landmarks. The learning step is carried out with a machine learning approach by exploring two methods: Convolutional Neural Network (CNN) and Random Forest (RF). The automatic detection of the thirteen landmarks points in a new image is then obtained by averaging the positions of each one of these thirteen landmarks estimated from all the salient points in the new image. We respectively obtain for CNN and RF, an average prediction error (both mean and standard deviation in mm) of 29 +/-18 and 30 +/- 21 for the thirteen landmarks points, indicating the approximate location of anatomical regions. On the other hand, the learning time is 9 days for CNN versus 80 minutes for RF. We provide a comparison of the results between the two machine learning approaches.

The Advanced Linked Extended Reconnaissance & Targeting Technology Demonstration project

NASA Astrophysics Data System (ADS)

Edwards, Mark

2008-04-01

The Advanced Linked Extended Reconnaissance & Targeting (ALERT) Technology Demonstration (TD) project is addressing many operational needs of the future Canadian Army's Surveillance and Reconnaissance forces. Using the surveillance system of the Coyote reconnaissance vehicle as an experimental platform, the ALERT TD project aims to significantly enhance situational awareness by fusing multi-sensor and tactical data, developing automated processes, and integrating beyond line-of-sight sensing. The project is exploiting important advances made in computer processing capability, displays technology, digital communications, and sensor technology since the design of the original surveillance system. As the major research area within the project, concepts are discussed for displaying and fusing multi-sensor and tactical data within an Enhanced Operator Control Station (EOCS). The sensor data can originate from the Coyote's own visible-band and IR cameras, laser rangefinder, and ground-surveillance radar, as well as from beyond line-of-sight systems such as mini-UAVs and unattended ground sensors. Video-rate image processing has been developed to assist the operator to detect poorly visible targets. As a second major area of research, automatic target cueing capabilities have been added to the system. These include scene change detection, automatic target detection and aided target recognition algorithms processing both IR and visible-band images to draw the operator's attention to possible targets. The merits of incorporating scene change detection algorithms are also discussed. In the area of multi-sensor data fusion, up to Joint Defence Labs level 2 has been demonstrated. The human factors engineering aspects of the user interface in this complex environment are presented, drawing upon multiple user group sessions with military surveillance system operators. The paper concludes with Lessons Learned from the project. The ALERT system has been used in a number of C4ISR field trials, most recently at Exercise Empire Challenge in China Lake CA, and at Trial Quest in Norway. Those exercises provided further opportunities to investigate operator interactions. The paper concludes with recommendations for future work in operator interface design.

Strategies for automatic processing of large aftershock sequences

NASA Astrophysics Data System (ADS)

Kvaerna, T.; Gibbons, S. J.

2017-12-01

Aftershock sequences following major earthquakes present great challenges to seismic bulletin generation. The analyst resources needed to locate events increase with increased event numbers as the quality of underlying, fully automatic, event lists deteriorates. While current pipelines, designed a generation ago, are usually limited to single passes over the raw data, modern systems also allow multiple passes. Processing the raw data from each station currently generates parametric data streams that are later subject to phase-association algorithms which form event hypotheses. We consider a major earthquake scenario and propose to define a region of likely aftershock activity in which we will detect and accurately locate events using a separate, specially targeted, semi-automatic process. This effort may use either pattern detectors or more general algorithms that cover wider source regions without requiring waveform similarity. An iterative procedure to generate automatic bulletins would incorporate all the aftershock event hypotheses generated by the auxiliary process, and filter all phases from these events from the original detection lists prior to a new iteration of the global phase-association algorithm.

Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

NASA Astrophysics Data System (ADS)

Kim, D.; Youn, J.; Kim, C.

2017-08-01

As a malfunctioning PV (Photovoltaic) cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

Focused ultrasound: concept for automated transcutaneous control of hemorrhage in austere settings.

PubMed

Kucewicz, John C; Bailey, Michael R; Kaczkowski, Peter J; Carter, Stephen J

2009-04-01

High intensity focused ultrasound (HIFU) is being developed for a range of clinical applications. Of particular interest to NASA and the military is the use of HIFU for traumatic injuries because HIFU has the unique ability to transcutaneously stop bleeding. Automation of this technology would make possible its use in remote, austere settings by personnel not specialized in medical ultrasound. Here a system to automatically detect and target bleeding is tested and reported. The system uses Doppler ultrasound images from a clinical ultrasound scanner for bleeding detection and hardware for HIFU therapy. The system was tested using a moving string to simulate blood flow and targeting was visualized by Schlieren imaging to show the focusing of the HIFU acoustic waves. When instructed by the operator, a Doppler ultrasound image is acquired and processed to detect and localize the moving string, and the focus of the HIFU array is electronically adjusted to target the string. Precise and accurate targeting was verified in the Schlieren images. An automated system to detect and target simulated bleeding has been built and tested. The system could be combined with existing algorithms to detect, target, and treat clinical bleeding.

A method for real-time implementation of HOG feature extraction

NASA Astrophysics Data System (ADS)

Luo, Hai-bo; Yu, Xin-rong; Liu, Hong-mei; Ding, Qing-hai

2011-08-01

Histogram of oriented gradient (HOG) is an efficient feature extraction scheme, and HOG descriptors are feature descriptors which is widely used in computer vision and image processing for the purpose of biometrics, target tracking, automatic target detection(ATD) and automatic target recognition(ATR) etc. However, computation of HOG feature extraction is unsuitable for hardware implementation since it includes complicated operations. In this paper, the optimal design method and theory frame for real-time HOG feature extraction based on FPGA were proposed. The main principle is as follows: firstly, the parallel gradient computing unit circuit based on parallel pipeline structure was designed. Secondly, the calculation of arctangent and square root operation was simplified. Finally, a histogram generator based on parallel pipeline structure was designed to calculate the histogram of each sub-region. Experimental results showed that the HOG extraction can be implemented in a pixel period by these computing units.

Non-target evaluation of contaminants in honey bees and pollen samples by gas chromatography time-of-flight mass spectrometry.

PubMed

Hakme, E; Lozano, A; Gómez-Ramos, M M; Hernando, M D; Fernández-Alba, A R

2017-10-01

This work presents a non-targeted screening approach for the detection and quantitation of contaminants in bees and pollen, collected from the same hive, by GC-EI-ToF-MS. It consists of a spectral library datasets search using a compound database followed by a manual investigation and analytical standard confirmation together with semi-quantitation purposes. Over 20% of the compounds found automatically by the library search could not be confirmed manually. This number of false positive detections was mainly a consequence of an inadequate ion ratio criterion (±30%), not considered in the automatic searching procedure. Eight compounds were detected in bees and pollen. They include insecticides/acaricides (chlorpyrifos, coumaphos, fluvalinate-tau, chlorfenvinphos, pyridaben, and propyl cresol) at a concentration range of 1-1207 μg kg -1 , herbicides (oxyfluorfen) at a concentration range of 212-1773 μg kg -1 and a growth regulator hormone (methoprene). Some compounds were detected only in pollen; such as herbicides (clomazone), insecticides/acaricides and fungicides used to control Varroa mites as benzylbenzoate, bufencarb, allethrin, permethrin, eugenol and cyprodinil. Additional compounds were detected only in bees: flamprop-methyl, 2-methylphenol (2-49 μg kg -1 ) and naphthalene (1-23 μg kg -1 ). The proposed method presents important advantages as it can avoid the use of an unachievable number of analytical standards considered target compounds "a priori" but not present in the analyzed samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

Detecting targets hidden in random forests

NASA Astrophysics Data System (ADS)

Kouritzin, Michael A.; Luo, Dandan; Newton, Fraser; Wu, Biao

2009-05-01

Military tanks, cargo or troop carriers, missile carriers or rocket launchers often hide themselves from detection in the forests. This plagues the detection problem of locating these hidden targets. An electro-optic camera mounted on a surveillance aircraft or unmanned aerial vehicle is used to capture the images of the forests with possible hidden targets, e.g., rocket launchers. We consider random forests of longitudinal and latitudinal correlations. Specifically, foliage coverage is encoded with a binary representation (i.e., foliage or no foliage), and is correlated in adjacent regions. We address the detection problem of camouflaged targets hidden in random forests by building memory into the observations. In particular, we propose an efficient algorithm to generate random forests, ground, and camouflage of hidden targets with two dimensional correlations. The observations are a sequence of snapshots consisting of foliage-obscured ground or target. Theoretically, detection is possible because there are subtle differences in the correlations of the ground and camouflage of the rocket launcher. However, these differences are well beyond human perception. To detect the presence of hidden targets automatically, we develop a Markov representation for these sequences and modify the classical filtering equations to allow the Markov chain observation. Particle filters are used to estimate the position of the targets in combination with a novel random weighting technique. Furthermore, we give positive proof-of-concept simulations.

Automatic pose correction for image-guided nonhuman primate brain surgery planning

NASA Astrophysics Data System (ADS)

Ghafurian, Soheil; Chen, Antong; Hines, Catherine; Dogdas, Belma; Bone, Ashleigh; Lodge, Kenneth; O'Malley, Stacey; Winkelmann, Christopher T.; Bagchi, Ansuman; Lubbers, Laura S.; Uslaner, Jason M.; Johnson, Colena; Renger, John; Zariwala, Hatim A.

2016-03-01

Intracranial delivery of recombinant DNA and neurochemical analysis in nonhuman primate (NHP) requires precise targeting of various brain structures via imaging derived coordinates in stereotactic surgeries. To attain targeting precision, the surgical planning needs to be done on preoperative three dimensional (3D) CT and/or MR images, in which the animals head is fixed in a pose identical to the pose during the stereotactic surgery. The matching of the image to the pose in the stereotactic frame can be done manually by detecting key anatomical landmarks on the 3D MR and CT images such as ear canal and ear bar zero position. This is not only time intensive but also prone to error due to the varying initial poses in the images which affects both the landmark detection and rotation estimation. We have introduced a fast, reproducible, and semi-automatic method to detect the stereotactic coordinate system in the image and correct the pose. The method begins with a rigid registration of the subject images to an atlas and proceeds to detect the anatomical landmarks through a sequence of optimization, deformable and multimodal registration algorithms. The results showed similar precision (maximum difference of 1.71 in average in-plane rotation) to a manual pose correction.

Confidence level estimation in multi-target classification problems

NASA Astrophysics Data System (ADS)

Chang, Shi; Isaacs, Jason; Fu, Bo; Shin, Jaejeong; Zhu, Pingping; Ferrari, Silvia

2018-04-01

This paper presents an approach for estimating the confidence level in automatic multi-target classification performed by an imaging sensor on an unmanned vehicle. An automatic target recognition algorithm comprised of a deep convolutional neural network in series with a support vector machine classifier detects and classifies targets based on the image matrix. The joint posterior probability mass function of target class, features, and classification estimates is learned from labeled data, and recursively updated as additional images become available. Based on the learned joint probability mass function, the approach presented in this paper predicts the expected confidence level of future target classifications, prior to obtaining new images. The proposed approach is tested with a set of simulated sonar image data. The numerical results show that the estimated confidence level provides a close approximation to the actual confidence level value determined a posteriori, i.e. after the new image is obtained by the on-board sensor. Therefore, the expected confidence level function presented in this paper can be used to adaptively plan the path of the unmanned vehicle so as to optimize the expected confidence levels and ensure that all targets are classified with satisfactory confidence after the path is executed.

Polarimetric Imaging System for Automatic Target Detection and Recognition

DTIC Science & Technology

2000-03-01

technique shown in Figure 4(b) can also be used to integrate polarizer arrays with other types of imaging sensors, such as LWIR cameras and uncooled...vertical stripe pattern in this φ image is caused by nonuniformities in the particular polarizer array used. 2. CIRCULAR POLARIZATION IMAGING USING

Partial polygon pruning of hydrographic features in automated generalization

USGS Publications Warehouse

Stum, Alexander K.; Buttenfield, Barbara P.; Stanislawski, Larry V.

2017-01-01

This paper demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi-scale hydrographic database. Water features are known to be sensitive to scale change; and thus multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal features—such as long and sinuous reservoir arms, stream channels that are too narrow at the target scale, and islands that begin to coalesce—entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the U.S. Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This paper describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the United States.

BgCut: automatic ship detection from UAV images.

PubMed

Xu, Chao; Zhang, Dongping; Zhang, Zhengning; Feng, Zhiyong

2014-01-01

Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches.

BgCut: Automatic Ship Detection from UAV Images

PubMed Central

Zhang, Zhengning; Feng, Zhiyong

2014-01-01

Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches. PMID:24977182

Feature-aided multiple target tracking in the image plane

NASA Astrophysics Data System (ADS)

Brown, Andrew P.; Sullivan, Kevin J.; Miller, David J.

2006-05-01

Vast quantities of EO and IR data are collected on airborne platforms (manned and unmanned) and terrestrial platforms (including fixed installations, e.g., at street intersections), and can be exploited to aid in the global war on terrorism. However, intelligent preprocessing is required to enable operator efficiency and to provide commanders with actionable target information. To this end, we have developed an image plane tracker which automatically detects and tracks multiple targets in image sequences using both motion and feature information. The effects of platform and camera motion are compensated via image registration, and a novel change detection algorithm is applied for accurate moving target detection. The contiguous pixel blob on each moving target is segmented for use in target feature extraction and model learning. Feature-based target location measurements are used for tracking through move-stop-move maneuvers, close target spacing, and occlusion. Effective clutter suppression is achieved using joint probabilistic data association (JPDA), and confirmed target tracks are indicated for further processing or operator review. In this paper we describe the algorithms implemented in the image plane tracker and present performance results obtained with video clips from the DARPA VIVID program data collection and from a miniature unmanned aerial vehicle (UAV) flight.

Application of automatic threshold in dynamic target recognition with low contrast

NASA Astrophysics Data System (ADS)

Miao, Hua; Guo, Xiaoming; Chen, Yu

2014-11-01

Hybrid photoelectric joint transform correlator can realize automatic real-time recognition with high precision through the combination of optical devices and electronic devices. When recognizing targets with low contrast using photoelectric joint transform correlator, because of the difference of attitude, brightness and grayscale between target and template, only four to five frames of dynamic targets can be recognized without any processing. CCD camera is used to capture the dynamic target images and the capturing speed of CCD is 25 frames per second. Automatic threshold has many advantages like fast processing speed, effectively shielding noise interference, enhancing diffraction energy of useful information and better reserving outline of target and template, so this method plays a very important role in target recognition with optical correlation method. However, the automatic obtained threshold by program can not achieve the best recognition results for dynamic targets. The reason is that outline information is broken to some extent. Optimal threshold is obtained by manual intervention in most cases. Aiming at the characteristics of dynamic targets, the processing program of improved automatic threshold is finished by multiplying OTSU threshold of target and template by scale coefficient of the processed image, and combining with mathematical morphology. The optimal threshold can be achieved automatically by improved automatic threshold processing for dynamic low contrast target images. The recognition rate of dynamic targets is improved through decreased background noise effect and increased correlation information. A series of dynamic tank images with the speed about 70 km/h are adapted as target images. The 1st frame of this series of tanks can correlate only with the 3rd frame without any processing. Through OTSU threshold, the 80th frame can be recognized. By automatic threshold processing of the joint images, this number can be increased to 89 frames. Experimental results show that the improved automatic threshold processing has special application value for the recognition of dynamic target with low contrast.

Detection, recognition, identification, and tracking of military vehicles using biomimetic intelligence

NASA Astrophysics Data System (ADS)

Pace, Paul W.; Sutherland, John

2001-10-01

This project is aimed at analyzing EO/IR images to provide automatic target detection/recognition/identification (ATR/D/I) of militarily relevant land targets. An increase in performance was accomplished using a biomimetic intelligence system functioning on low-cost, commercially available processing chips. Biomimetic intelligence has demonstrated advanced capabilities in the areas of hand- printed character recognition, real-time detection/identification of multiple faces in full 3D perspectives in cluttered environments, advanced capabilities in classification of ground-based military vehicles from SAR, and real-time ATR/D/I of ground-based military vehicles from EO/IR/HRR data in cluttered environments. The investigation applied these tools to real data sets and examined the parameters such as the minimum resolution for target recognition, the effect of target size, rotation, line-of-sight changes, contrast, partial obscuring, background clutter etc. The results demonstrated a real-time ATR/D/I capability against a subset of militarily relevant land targets operating in a realistic scenario. Typical results on the initial EO/IR data indicate probabilities of correct classification of resolved targets to be greater than 95 percent.

Strategic deployment of orthographic knowledge in phoneme detection.

PubMed

Cutler, Anne; Treiman, Rebecca; van Ooijen, Brit

2010-01-01

The phoneme detection task is widely used in spoken-word recognition research. Alphabetically literate participants, however, are more used to explicit representations of letters than of phonemes. The present study explored whether phoneme detection is sensitive to how target phonemes are, or may be, orthographically realized. Listeners detected the target sounds [b, m, t, f, s, k] in word-initial position in sequences of isolated English words. Response times were faster to the targets [b, m, t], which have consistent word-initial spelling, than to the targets [f, s, k], which are inconsistently spelled, but only when spelling was rendered salient by the presence in the experiment of many irregularly spelled filler words. Within the inconsistent targets [f, s, k], there was no significant difference between responses to targets in words with more usual (foam, seed, cattle) versus less usual (phone, cede, kettle) spellings. Phoneme detection is thus not necessarily sensitive to orthographic effects; knowledge of spelling stored in the lexical representations of words does not automatically become available as word candidates are activated. However, salient orthographic manipulations in experimental input can induce such sensitivity. We attribute this to listeners' experience of the value of spelling in everyday situations that encourage phonemic decisions (such as learning new names).

Small-scale anomaly detection in panoramic imaging using neural models of low-level vision

NASA Astrophysics Data System (ADS)

Casey, Matthew C.; Hickman, Duncan L.; Pavlou, Athanasios; Sadler, James R. E.

2011-06-01

Our understanding of sensory processing in animals has reached the stage where we can exploit neurobiological principles in commercial systems. In human vision, one brain structure that offers insight into how we might detect anomalies in real-time imaging is the superior colliculus (SC). The SC is a small structure that rapidly orients our eyes to a movement, sound or touch that it detects, even when the stimulus may be on a small-scale; think of a camouflaged movement or the rustle of leaves. This automatic orientation allows us to prioritize the use of our eyes to raise awareness of a potential threat, such as a predator approaching stealthily. In this paper we describe the application of a neural network model of the SC to the detection of anomalies in panoramic imaging. The neural approach consists of a mosaic of topographic maps that are each trained using competitive Hebbian learning to rapidly detect image features of a pre-defined shape and scale. What makes this approach interesting is the ability of the competition between neurons to automatically filter noise, yet with the capability of generalizing the desired shape and scale. We will present the results of this technique applied to the real-time detection of obscured targets in visible-band panoramic CCTV images. Using background subtraction to highlight potential movement, the technique is able to correctly identify targets which span as little as 3 pixels wide while filtering small-scale noise.

Infrared variation reduction by simultaneous background suppression and target contrast enhancement for deep convolutional neural network-based automatic target recognition

NASA Astrophysics Data System (ADS)

Kim, Sungho

2017-06-01

Automatic target recognition (ATR) is a traditionally challenging problem in military applications because of the wide range of infrared (IR) image variations and the limited number of training images. IR variations are caused by various three-dimensional target poses, noncooperative weather conditions (fog and rain), and difficult target acquisition environments. Recently, deep convolutional neural network-based approaches for RGB images (RGB-CNN) showed breakthrough performance in computer vision problems, such as object detection and classification. The direct use of RGB-CNN to the IR ATR problem fails to work because of the IR database problems (limited database size and IR image variations). An IR variation-reduced deep CNN (IVR-CNN) to cope with the problems is presented. The problem of limited IR database size is solved by a commercial thermal simulator (OKTAL-SE). The second problem of IR variations is mitigated by the proposed shifted ramp function-based intensity transformation. This can suppress the background and enhance the target contrast simultaneously. The experimental results on the synthesized IR images generated by the thermal simulator (OKTAL-SE) validated the feasibility of IVR-CNN for military ATR applications.

Feature Extraction Using Attributed Scattering Center Models for Model-Based Automatic Target Recognition (ATR)

DTIC Science & Technology

2005-10-01

section of the coiled arm. Right: measured realized total gain for a square spiral in free space with inductive treatment. . . . . . . . 154 8.5 Initial...appreciable velocities can often be easily separated from clutter returns, slow moving targets of more moderate cross sections can be very difficult to detect...electromagnetic radiation and measuring the energy scattered back. The data obtained as a result of this process is a finite-extent, noisy set of

Optimization of OT-MACH Filter Generation for Target Recognition

NASA Technical Reports Server (NTRS)

Johnson, Oliver C.; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

2009-01-01

An automatic Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter generator for use in a gray-scale optical correlator (GOC) has been developed for improved target detection at JPL. While the OT-MACH filter has been shown to be an optimal filter for target detection, actually solving for the optimum is too computationally intensive for multiple targets. Instead, an adaptive step gradient descent method was tested to iteratively optimize the three OT-MACH parameters, alpha, beta, and gamma. The feedback for the gradient descent method was a composite of the performance measures, correlation peak height and peak to side lobe ratio. The automated method generated and tested multiple filters in order to approach the optimal filter quicker and more reliably than the current manual method. Initial usage and testing has shown preliminary success at finding an approximation of the optimal filter, in terms of alpha, beta, gamma values. This corresponded to a substantial improvement in detection performance where the true positive rate increased for the same average false positives per image.

Real Time Intelligent Target Detection and Analysis with Machine Vision

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Padgett, Curtis; Brown, Kenneth

2000-01-01

We present an algorithm for detecting a specified set of targets for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and nontarget objects in a scene by evaluating 40x40 image blocks belonging to an image. Each image block is first projected onto a set of templates specifically designed to separate images of targets embedded in a typical background scene from those background images without targets. These filters are found using directed principal component analysis which maximally separates the two groups. The projected images are then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Each projected image pattern is then fed into the associated cluster's trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for designing the templates, describe our modified clustering algorithm, and provide details on the neural network classifiers. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.

Performance Analysis of a Pole and Tree Trunk Detection Method for Mobile Laser Scanning Data

NASA Astrophysics Data System (ADS)

Lehtomäki, M.; Jaakkola, A.; Hyyppä, J.; Kukko, A.; Kaartinen, H.

2011-09-01

Dense point clouds can be collected efficiently from large areas using mobile laser scanning (MLS) technology. Accurate MLS data can be used for detailed 3D modelling of the road surface and objects around it. The 3D models can be utilised, for example, in street planning and maintenance and noise modelling. Utility poles, traffic signs, and lamp posts can be considered an important part of road infrastructure. Poles and trees stand out from the environment and should be included in realistic 3D models. Detection of narrow vertical objects, such as poles and tree trunks, from MLS data was studied. MLS produces huge amounts of data and, therefore, processing methods should be as automatic as possible and for the methods to be practical, the algorithms should run in an acceptable time. The automatic pole detection method tested in this study is based on first finding point clusters that are good candidates for poles and then separating poles and tree trunks from other clusters using features calculated from the clusters and by applying a mask that acts as a model of a pole. The method achieved detection rates of 77.7% and 69.7% in the field tests while 81.0% and 86.5% of the detected targets were correct. Pole-like targets that were surrounded by other objects, such as tree trunks that were inside branches, were the most difficult to detect. Most of the false detections came from wall structures, which could be corrected in further processing.

Real-time road detection in infrared imagery

NASA Astrophysics Data System (ADS)

Andre, Haritini E.; McCoy, Keith

1990-09-01

Automatic road detection is an important part in many scene recognition applications. The extraction of roads provides a means of navigation and position update for remotely piloted vehicles or autonomous vehicles. Roads supply strong contextual information which can be used to improve the performance of automatic target recognition (ATh) systems by directing the search for targets and adjusting target classification confidences. This paper will describe algorithmic techniques for labeling roads in high-resolution infrared imagery. In addition, realtime implementation of this structural approach using a processor array based on the Martin Marietta Geometric Arithmetic Parallel Processor (GAPPTh) chip will be addressed. The algorithm described is based on the hypothesis that a road consists of pairs of line segments separated by a distance "d" with opposite gradient directions (antiparallel). The general nature of the algorithm, in addition to its parallel implementation in a single instruction, multiple data (SIMD) machine, are improvements to existing work. The algorithm seeks to identify line segments meeting the road hypothesis in a manner that performs well, even when the side of the road is fragmented due to occlusion or intersections. The use of geometrical relationships between line segments is a powerful yet flexible method of road classification which is independent of orientation. In addition, this approach can be used to nominate other types of objects with minor parametric changes.

Through-barrier electromagnetic imaging with an atomic magnetometer.

PubMed

Deans, Cameron; Marmugi, Luca; Renzoni, Ferruccio

2017-07-24

We demonstrate the penetration of thick metallic and ferromagnetic barriers for imaging of conductive targets underneath. Our system is based on an 85 Rb radio-frequency atomic magnetometer operating in electromagnetic induction imaging modality in an unshielded environment. Detrimental effects, including unpredictable magnetic signatures from ferromagnetic screens and variations in the magnetic background, are automatically compensated by active compensation coils controlled by servo loops. We exploit the tunability and low-frequency sensitivity of the atomic magnetometer to directly image multiple conductive targets concealed by a 2.5 mm ferromagnetic steel shield and/or a 2.0 mm aluminium shield, in a single scan. The performance of the atomic magnetometer allows imaging without any prior knowledge of the barriers or the targets, and without the need of background subtraction. A dedicated edge detection algorithm allows automatic estimation of the targets' size within 3.3 mm and of their position within 2.4 mm. Our results prove the feasibility of a compact, sensitive and automated sensing platform for imaging of concealed objects in a range of applications, from security screening to search and rescue.

The Automatic Recognition of the Abnormal Sky-subtraction Spectra Based on Hadoop

NASA Astrophysics Data System (ADS)

An, An; Pan, Jingchang

2017-10-01

The skylines, superimposing on the target spectrum as a main noise, If the spectrum still contains a large number of high strength skylight residuals after sky-subtraction processing, it will not be conducive to the follow-up analysis of the target spectrum. At the same time, the LAMOST can observe a quantity of spectroscopic data in every night. We need an efficient platform to proceed the recognition of the larger numbers of abnormal sky-subtraction spectra quickly. Hadoop, as a distributed parallel data computing platform, can deal with large amounts of data effectively. In this paper, we conduct the continuum normalization firstly and then a simple and effective method will be presented to automatic recognize the abnormal sky-subtraction spectra based on Hadoop platform. Obtain through the experiment, the Hadoop platform can implement the recognition with more speed and efficiency, and the simple method can recognize the abnormal sky-subtraction spectra and find the abnormal skyline positions of different residual strength effectively, can be applied to the automatic detection of abnormal sky-subtraction of large number of spectra.

Testing Saliency Parameters for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Pandya, Sagar

2012-01-01

A bottom-up visual attention model (the saliency model) is tested to enhance the performance of Automated Target Recognition (ATR). JPL has developed an ATR system that identifies regions of interest (ROI) using a trained OT-MACH filter, and then classifies potential targets as true- or false-positives using machine-learning techniques. In this project, saliency is used as a pre-processing step to reduce the space for performing OT-MACH filtering. Saliency parameters, such as output level and orientation weight, are tuned to detect known target features. Preliminary results are promising and future work entails a rigrous and parameter-based search to gain maximum insight about this method.

Automatic Target Cueing (ATC) Task 1 Report - Literature Survey on ATC

DTIC Science & Technology

2013-10-30

xa s In st ru m en t D aV in ci c hi p C ++ O ut da te d in fo rm at io n as w eb pa ge w as la st u pd at ed in...techniques such as contrast/ edge enhancement to increase the detectability of targets in the urban terrain. [P-4] restores long-distance thermal...Range? Sensor Experimental Setup Results [P-3] Contrast enhancement Edge enhancement Multi-scale edge domain Still images Yes IR

Automatic tool alignment in a backscatter X-ray scanning system

DOEpatents

Garretson, Justin; Hobart, Clinton G.; Gladwell, Thomas S.; Monda, Mark J.

2015-11-17

Technologies pertaining to backscatter x-ray scanning systems are described herein. The backscatter x-ray scanning system includes an x-ray source, which directs collimated x-rays along a plurality of output vectors towards a target. A detector detects diffusely reflected x-rays subsequent to respective collimated x-rays impacting the target, and outputs signals indicative of parameters of the detected x-rays. An image processing system generates an x-ray image based upon parameters of the detected x-rays, wherein each pixel in the image corresponds to a respective output vector. A user selects a particular portion of the image, and a medical device is positioned such that its directional axis is coincident with the output vector corresponding to at least one pixel in the portion of the image.

Automatic tool alignment in a backscatter x-ray scanning system

DOEpatents

Garretson, Justin; Hobart, Clinton G.; Gladwell, Thomas S.; Monda, Mark J.

2015-06-16

Technologies pertaining to backscatter x-ray scanning systems are described herein. The backscatter x-ray scanning system includes an x-ray source, which directs collimated x-rays along a plurality of output vectors towards a target. A detector detects diffusely reflected x-rays subsequent to respective collimated x-rays impacting the target, and outputs signals indicative of parameters of the detected x-rays. An image processing system generates an x-ray image based upon parameters of the detected x-rays, wherein each pixel in the image corresponds to a respective output vector. A user selects a particular portion of the image, and a tool is positioned such that its directional axis is coincident with the output vector corresponding to at least one pixel in the portion of the image.

46 CFR 161.002-9 - Automatic fire detecting system, power supply.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 6 2011-10-01 2011-10-01 false Automatic fire detecting system, power supply. 161.002-9 Section 161.002-9 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) EQUIPMENT...-9 Automatic fire detecting system, power supply. The power supply for an automatic fire detecting...

46 CFR 161.002-9 - Automatic fire detecting system, power supply.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 6 2010-10-01 2010-10-01 false Automatic fire detecting system, power supply. 161.002-9 Section 161.002-9 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) EQUIPMENT...-9 Automatic fire detecting system, power supply. The power supply for an automatic fire detecting...

Advanced miniature processing handware for ATR applications

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin (Inventor); Daud, Taher (Inventor); Thakoor, Anikumar (Inventor)

2003-01-01

A Hybrid Optoelectronic Neural Object Recognition System (HONORS), is disclosed, comprising two major building blocks: (1) an advanced grayscale optical correlator (OC) and (2) a massively parallel three-dimensional neural-processor. The optical correlator, with its inherent advantages in parallel processing and shift invariance, is used for target of interest (TOI) detection and segmentation. The three-dimensional neural-processor, with its robust neural learning capability, is used for target classification and identification. The hybrid optoelectronic neural object recognition system, with its powerful combination of optical processing and neural networks, enables real-time, large frame, automatic target recognition (ATR).

Automated intelligent video surveillance system for ships

NASA Astrophysics Data System (ADS)

Wei, Hai; Nguyen, Hieu; Ramu, Prakash; Raju, Chaitanya; Liu, Xiaoqing; Yadegar, Jacob

2009-05-01

To protect naval and commercial ships from attack by terrorists and pirates, it is important to have automatic surveillance systems able to detect, identify, track and alert the crew on small watercrafts that might pursue malicious intentions, while ruling out non-threat entities. Radar systems have limitations on the minimum detectable range and lack high-level classification power. In this paper, we present an innovative Automated Intelligent Video Surveillance System for Ships (AIVS3) as a vision-based solution for ship security. Capitalizing on advanced computer vision algorithms and practical machine learning methodologies, the developed AIVS3 is not only capable of efficiently and robustly detecting, classifying, and tracking various maritime targets, but also able to fuse heterogeneous target information to interpret scene activities, associate targets with levels of threat, and issue the corresponding alerts/recommendations to the man-in- the-loop (MITL). AIVS3 has been tested in various maritime scenarios and shown accurate and effective threat detection performance. By reducing the reliance on human eyes to monitor cluttered scenes, AIVS3 will save the manpower while increasing the accuracy in detection and identification of asymmetric attacks for ship protection.

Clever eye algorithm for target detection of remote sensing imagery

NASA Astrophysics Data System (ADS)

Geng, Xiurui; Ji, Luyan; Sun, Kang

2016-04-01

Target detection algorithms for hyperspectral remote sensing imagery, such as the two most commonly used remote sensing detection algorithms, the constrained energy minimization (CEM) and matched filter (MF), can usually be attributed to the inner product between a weight filter (or detector) and a pixel vector. CEM and MF have the same expression except that MF requires data centralization first. However, this difference leads to a difference in the target detection results. That is to say, the selection of the data origin could directly affect the performance of the detector. Therefore, does there exist another data origin other than the zero and mean-vector points for a better target detection performance? This is a very meaningful issue in the field of target detection, but it has not been paid enough attention yet. In this study, we propose a novel objective function by introducing the data origin as another variable, and the solution of the function is corresponding to the data origin with the minimal output energy. The process of finding the optimal solution can be vividly regarded as a clever eye automatically searching the best observing position and direction in the feature space, which corresponds to the largest separation between the target and background. Therefore, this new algorithm is referred to as the clever eye algorithm (CE). Based on the Sherman-Morrison formula and the gradient ascent method, CE could derive the optimal target detection result in terms of energy. Experiments with both synthetic and real hyperspectral data have verified the effectiveness of our method.

Attentional Capture by Salient Color Singleton Distractors Is Modulated by Top-Down Dimensional Set

ERIC Educational Resources Information Center

Muller, Hermann J.; Geyer, Thomas; Zehetleitner, Michael; Krummenacher, Joseph

2009-01-01

Three experiments examined whether salient color singleton distractors automatically interfere with the detection singleton form targets in visual search (e.g., J. Theeuwes, 1992), or whether the degree of interference is top-down modulable. In Experiments 1 and 2, observers started with a pure block of trials, which contained either never a…

Perceptual Units Training for Improving Word Analysis Skills. Technical Report No. 1.

ERIC Educational Resources Information Center

Weaver, Phyllis A.; And Others

A training program was devised to develop automaticity of one subcomponent of reading--locating and disembedding multiletter units within words. The system involved the use of a training task that was implemented in a microcomputer-based game that required students to detect whether a target unit was presented within words that were shown in rapid…

Subliminal gaze cues increase preference levels for items in the gaze direction.

PubMed

Mitsuda, Takashi; Masaki, Syuta

2017-08-29

Another individual's gaze automatically shifts an observer's attention to a location. This reflexive response occurs even when the gaze is presented subliminally over a short period. Another's gaze also increases the preference level for items in the gaze direction; however, it was previously unclear if this effect occurs when the gaze is presented subliminally. This study showed that the preference levels for nonsense figures looked at by a subliminal gaze were significantly greater than those for items that were subliminally looked away from (Task 1). Targets that were looked at by a subliminal gaze were detected faster (Task 2); however, the participants were unable to detect the gaze direction (Task 3). These results indicate that another individual's gaze automatically increases the preference levels for items in the gaze direction without conscious awareness.

Intelligent Unmanned Monitoring of Remediated Sites

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

Emile Fiesler, Ph.D.

During this Phase I project, IOS demonstrated the feasibility of combining digital signal processing and neural network analysis to analyze spectral signals from pure samples of several typical contaminants. We fabricated and tested a prototype system by automatically analyzing Raman spectral data taken in the Vadose zone at the 321 M site in the M area of DOE's Savannah River Site in South Carolina. This test demonstration proved the ability of IOS's technology to detect the target contaminants, tetrachloroethylene (PCE) and trichloroethylene (TCE), in isolation, and to detect the spectra of these contaminants in real-world noisy samples taken from amore » mixture of materials obtained from this typical remediation target site.« less

Learning a detection map for a network of unattended ground sensors.

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

Nguyen, Hung D.; Koch, Mark William

2010-03-01

We have developed algorithms to automatically learn a detection map of a deployed sensor field for a virtual presence and extended defense (VPED) system without apriori knowledge of the local terrain. The VPED system is an unattended network of sensor pods, with each pod containing acoustic and seismic sensors. Each pod has the ability to detect and classify moving targets at a limited range. By using a network of pods we can form a virtual perimeter with each pod responsible for a certain section of the perimeter. The site's geography and soil conditions can affect the detection performance of themore » pods. Thus, a network in the field may not have the same performance as a network designed in the lab. To solve this problem we automatically estimate a network's detection performance as it is being installed at a site by a mobile deployment unit (MDU). The MDU will wear a GPS unit, so the system not only knows when it can detect the MDU, but also the MDU's location. In this paper, we demonstrate how to handle anisotropic sensor-configurations, geography, and soil conditions.« less

A new approach for SSVEP detection using PARAFAC and canonical correlation analysis.

PubMed

Tello, Richard; Pouryazdian, Saeed; Ferreira, Andre; Beheshti, Soosan; Krishnan, Sridhar; Bastos, Teodiano

2015-01-01

This paper presents a new way for automatic detection of SSVEPs through correlation analysis between tensor models. 3-way EEG tensor of channel × frequency × time is decomposed into constituting factor matrices using PARAFAC model. PARAFAC analysis of EEG tensor enables us to decompose multichannel EEG into constituting temporal, spectral and spatial signatures. SSVEPs characterized with localized spectral and spatial signatures are then detected exploiting a correlation analysis between extracted signatures of the EEG tensor and the corresponding simulated signatures of all target SSVEP signals. The SSVEP that has the highest correlation is selected as the intended target. Two flickers blinking at 8 and 13 Hz were used as visual stimuli and the detection was performed based on data packets of 1 second without overlapping. Five subjects participated in the experiments and the highest classification rate of 83.34% was achieved, leading to the Information Transfer Rate (ITR) of 21.01 bits/min.

Automatic categorization of anatomical landmark-local appearances based on diffeomorphic demons and spectral clustering for constructing detector ensembles.

PubMed

Hanaoka, Shouhei; Masutani, Yoshitaka; Nemoto, Mitsutaka; Nomura, Yukihiro; Yoshikawa, Takeharu; Hayashi, Naoto; Ohtomo, Kuni

2012-01-01

A method for categorizing landmark-local appearances extracted from computed tomography (CT) datasets is presented. Anatomical landmarks in the human body inevitably have inter-individual variations that cause difficulty in automatic landmark detection processes. The goal of this study is to categorize subjects (i.e., training datasets) according to local shape variations of such a landmark so that each subgroup has less shape variation and thus the machine learning of each landmark detector is much easier. The similarity between each subject pair is measured based on the non-rigid registration result between them. These similarities are used by the spectral clustering process. After the clustering, all training datasets in each cluster, as well as synthesized intermediate images calculated from all subject-pairs in the cluster, are used to train the corresponding subgroup detector. All of these trained detectors compose a detector ensemble to detect the target landmark. Evaluation with clinical CT datasets showed great improvement in the detection performance.

Optical Fiber On-Line Detection System for Non-Touch Monitoring Roller Shape

NASA Astrophysics Data System (ADS)

Guo, Y.; Wang, Y. T.

2006-10-01

Basing on the principle of reflective displacement fiber-optic sensor, a high accuracy non-touch on-line optical fiber measurement system for roller shape is presented. The principle and composition of the detection system and the operation process are expatiated also. By using a novel probe of three optical fibers in equal transverse space, the effects of fluctuations in the light source, reflective changing of target surface and the intensity losses in the fiber lines are automatically compensated. Meantime, an optical fiber sensor model of correcting static error based on BP artificial neural network (ANN) is set up. Also by using interpolation method and value filtering to process the signals, effectively reduce the influence of random noise and the vibration of the roller bearing. So enhance the accuracy and resolution remarkably. Experiment proves that the accuracy of the system reach to the demand of practical production process, it provides a new method for the high speed, accurate and automatic on line detection of the mill roller shape.

Distributed pheromone-based swarming control of unmanned air and ground vehicles for RSTA

NASA Astrophysics Data System (ADS)

Sauter, John A.; Mathews, Robert S.; Yinger, Andrew; Robinson, Joshua S.; Moody, John; Riddle, Stephanie

2008-04-01

The use of unmanned vehicles in Reconnaissance, Surveillance, and Target Acquisition (RSTA) applications has received considerable attention recently. Cooperating land and air vehicles can support multiple sensor modalities providing pervasive and ubiquitous broad area sensor coverage. However coordination of multiple air and land vehicles serving different mission objectives in a dynamic and complex environment is a challenging problem. Swarm intelligence algorithms, inspired by the mechanisms used in natural systems to coordinate the activities of many entities provide a promising alternative to traditional command and control approaches. This paper describes recent advances in a fully distributed digital pheromone algorithm that has demonstrated its effectiveness in managing the complexity of swarming unmanned systems. The results of a recent demonstration at NASA's Wallops Island of multiple Aerosonde Unmanned Air Vehicles (UAVs) and Pioneer Unmanned Ground Vehicles (UGVs) cooperating in a coordinated RSTA application are discussed. The vehicles were autonomously controlled by the onboard digital pheromone responding to the needs of the automatic target recognition algorithms. UAVs and UGVs controlled by the same pheromone algorithm self-organized to perform total area surveillance, automatic target detection, sensor cueing, and automatic target recognition with no central processing or control and minimal operator input. Complete autonomy adds several safety and fault tolerance requirements which were integrated into the basic pheromone framework. The adaptive algorithms demonstrated the ability to handle some unplanned hardware failures during the demonstration without any human intervention. The paper describes lessons learned and the next steps for this promising technology.

Automatic Robotic Steering of Flexible Needles from 3D Ultrasound Images in Phantoms and Ex Vivo Biological Tissue.

PubMed

Mignon, Paul; Poignet, Philippe; Troccaz, Jocelyne

2018-05-29

Robotic control of needle bending aims at increasing the precision of percutaneous procedures. Ultrasound feedback is preferable for its clinical ease of use, cost and compactness but raises needle detection issues. In this paper, we propose a complete system dedicated to robotized guidance of a flexible needle under 3D ultrasound imaging. This system includes a medical robot dedicated to transperineal needle positioning and insertion, a rapid path planning for needle steering using bevel-tip needle natural curvature in tissue, and an ultrasound-based automatic needle detection algorithm. Since ultrasound-based automatic needle steering is often made difficult by the needle localization in biological tissue, we quantify the benefit of using flexible echogenic needles for robotized guidance under 3D ultrasound. The "echogenic" term refers to the etching of microstructures on the needle shaft. We prove that these structures improve needle visibility and detection robustness in ultrasound images. We finally present promising results when reaching targets using needle steering. The experiments were conducted with various needles in different media (synthetic phantoms and ex vivo biological tissue). For instance, with nitinol needles the mean accuracy is 1.2 mm (respectively 3.8 mm) in phantoms (resp. biological tissue).

The Electronic McPhail Trap

PubMed Central

Potamitis, Ilyas; Rigakis, Iraklis; Fysarakis, Konstantinos

2014-01-01

Certain insects affect cultivations in a detrimental way. A notable case is the olive fruit fly (Bactrocera oleae (Rossi)), that in Europe alone causes billions of euros in crop-loss/per year. Pests can be controlled with aerial and ground bait pesticide sprays, the efficiency of which depends on knowing the time and location of insect infestations as early as possible. The inspection of traps is currently carried out manually. Automatic monitoring traps can enhance efficient monitoring of flying pests by identifying and counting targeted pests as they enter the trap. This work deals with the hardware setup of an insect trap with an embedded optoelectronic sensor that automatically records insects as they fly in the trap. The sensor responsible for detecting the insect is an array of phototransistors receiving light from an infrared LED. The wing-beat recording is based on the interruption of the emitted light due to the partial occlusion from insect's wings as they fly in the trap. We show that the recordings are of high quality paving the way for automatic recognition and transmission of insect detections from the field to a smartphone. This work emphasizes the hardware implementation of the sensor and the detection/counting module giving all necessary implementation details needed to construct it. PMID:25429412

Reward-associated features capture attention in the absence of awareness: Evidence from object-substitution masking.

PubMed

Harris, Joseph A; Donohue, Sarah E; Schoenfeld, Mircea A; Hopf, Jens-Max; Heinze, Hans-Jochen; Woldorff, Marty G

2016-08-15

Reward-associated visual features have been shown to capture visual attention, evidenced in faster and more accurate behavioral performance, as well as in neural responses reflecting lateralized shifts of visual attention to those features. Specifically, the contralateral N2pc event-related-potential (ERP) component that reflects attentional shifting exhibits increased amplitude in response to task-relevant targets containing a reward-associated feature. In the present study, we examined the automaticity of such reward-association effects using object-substitution masking (OSM) in conjunction with MEG measures of visual attentional shifts. In OSM, a visual-search array is presented, with the target item to be detected indicated by a surrounding mask (here, four surrounding squares). Delaying the offset of the target-surrounding four-dot mask relative to the offset of the rest of the target/distracter array disrupts the viewer's awareness of the target (masked condition), whereas simultaneous offsets do not (unmasked condition). Here we manipulated whether the color of the OSM target was or was not of a previously reward-associated color. By tracking reward-associated enhancements of behavior and the N2pc in response to masked targets containing a previously rewarded or unrewarded feature, the automaticity of attentional capture by reward could be probed. We found an enhanced N2pc response to targets containing a previously reward-associated color feature. Moreover, this enhancement of the N2pc by reward did not differ between masking conditions, nor did it differ as a function of the apparent visibility of the target within the masked condition. Overall, these results underscore the automaticity of attentional capture by reward-associated features, and demonstrate the ability of feature-based reward associations to shape attentional capture and allocation outside of perceptual awareness. Copyright © 2016 Elsevier Inc. All rights reserved.

Detection of facilities in satellite imagery using semi-supervised image classification and auxiliary contextual observables

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

Harvey, Neal R; Ruggiero, Christy E; Pawley, Norma H

2009-01-01

Detecting complex targets, such as facilities, in commercially available satellite imagery is a difficult problem that human analysts try to solve by applying world knowledge. Often there are known observables that can be extracted by pixel-level feature detectors that can assist in the facility detection process. Individually, each of these observables is not sufficient for an accurate and reliable detection, but in combination, these auxiliary observables may provide sufficient context for detection by a machine learning algorithm. We describe an approach for automatic detection of facilities that uses an automated feature extraction algorithm to extract auxiliary observables, and a semi-supervisedmore » assisted target recognition algorithm to then identify facilities of interest. We illustrate the approach using an example of finding schools in Quickbird image data of Albuquerque, New Mexico. We use Los Alamos National Laboratory's Genie Pro automated feature extraction algorithm to find a set of auxiliary features that should be useful in the search for schools, such as parking lots, large buildings, sports fields and residential areas and then combine these features using Genie Pro's assisted target recognition algorithm to learn a classifier that finds schools in the image data.« less

Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines

NASA Astrophysics Data System (ADS)

Gibbons, Steven J.; Kværna, Tormod; Harris, David B.; Dodge, Douglas A.

2016-04-01

Aftershock sequences following very large earthquakes present enormous challenges to near-realtime generation of seismic bulletins. The increase in analyst resources needed to relocate an inflated number of events is compounded by failures of phase association algorithms and a significant deterioration in the quality of underlying fully automatic event bulletins. Current processing pipelines were designed a generation ago and, due to computational limitations of the time, are usually limited to single passes over the raw data. With current processing capability, multiple passes over the data are feasible. Processing the raw data at each station currently generates parametric data streams which are then scanned by a phase association algorithm to form event hypotheses. We consider the scenario where a large earthquake has occurred and propose to define a region of likely aftershock activity in which events are detected and accurately located using a separate specially targeted semi-automatic process. This effort may focus on so-called pattern detectors, but here we demonstrate a more general grid search algorithm which may cover wider source regions without requiring waveform similarity. Given many well-located aftershocks within our source region, we may remove all associated phases from the original detection lists prior to a new iteration of the phase association algorithm. We provide a proof-of-concept example for the 2015 Gorkha sequence, Nepal, recorded on seismic arrays of the International Monitoring System. Even with very conservative conditions for defining event hypotheses within the aftershock source region, we can automatically remove over half of the original detections which could have been generated by Nepal earthquakes and reduce the likelihood of false associations and spurious event hypotheses. Further reductions in the number of detections in the parametric data streams are likely using correlation and subspace detectors and/or empirical matched field processing.

46 CFR 78.47-13 - Fire detecting and manual alarm, automatic sprinkler, and smoke detecting alarm bells.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., and smoke detecting alarm bells. 78.47-13 Section 78.47-13 Shipping COAST GUARD, DEPARTMENT OF.... § 78.47-13 Fire detecting and manual alarm, automatic sprinkler, and smoke detecting alarm bells. (a) The fire detecting and manual alarm automatic sprinklers, and smoke detecting alarm bells in the...

46 CFR 78.47-13 - Fire detecting and manual alarm, automatic sprinkler, and smoke detecting alarm bells.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., and smoke detecting alarm bells. 78.47-13 Section 78.47-13 Shipping COAST GUARD, DEPARTMENT OF.... § 78.47-13 Fire detecting and manual alarm, automatic sprinkler, and smoke detecting alarm bells. (a) The fire detecting and manual alarm automatic sprinklers, and smoke detecting alarm bells in the...

46 CFR 78.47-13 - Fire detecting and manual alarm, automatic sprinkler, and smoke detecting alarm bells.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., and smoke detecting alarm bells. 78.47-13 Section 78.47-13 Shipping COAST GUARD, DEPARTMENT OF.... § 78.47-13 Fire detecting and manual alarm, automatic sprinkler, and smoke detecting alarm bells. (a) The fire detecting and manual alarm automatic sprinklers, and smoke detecting alarm bells in the...

46 CFR 78.47-13 - Fire detecting and manual alarm, automatic sprinkler, and smoke detecting alarm bells.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., and smoke detecting alarm bells. 78.47-13 Section 78.47-13 Shipping COAST GUARD, DEPARTMENT OF.... § 78.47-13 Fire detecting and manual alarm, automatic sprinkler, and smoke detecting alarm bells. (a) The fire detecting and manual alarm automatic sprinklers, and smoke detecting alarm bells in the...

46 CFR 78.47-13 - Fire detecting and manual alarm, automatic sprinkler, and smoke detecting alarm bells.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., and smoke detecting alarm bells. 78.47-13 Section 78.47-13 Shipping COAST GUARD, DEPARTMENT OF.... § 78.47-13 Fire detecting and manual alarm, automatic sprinkler, and smoke detecting alarm bells. (a) The fire detecting and manual alarm automatic sprinklers, and smoke detecting alarm bells in the...

Radar image processing of real aperture SLAR data for the detection and identification of iceberg and ship targets

NASA Technical Reports Server (NTRS)

Marthaler, J. G.; Heighway, J. E.

1979-01-01

An iceberg detection and identification system consisting of a moderate resolution Side Looking Airborne Radar (SLAR) interfaced with a Radar Image Processor (RIP) based on a ROLM 1664 computer with a 32K core memory updatable to 64K is described. The system can be operated in high- or low-resolution sampling modes. Specifically designed algorithms are applied to digitized signal returns to provide automatic target detection and location, geometrically correct video image display and data recording. The real aperture Motorola AN/APS-94D SLAR operates in the X-band and is tunable between 9.10 and 9.40 GHz; its output power is 45 kW peak with a pulse repetition rate of 750 pulses per hour. Schematic diagrams of the system are provided, together with preliminary test data.

Fully automatic method for the determination of fat soluble vitamins and vitamin D metabolites in serum.

PubMed

Mata-Granados, J M; Quesada Gómez, J M; Luque de Castro, M D

2009-05-01

Fat soluble vitamins and vitamin D metabolites are key compounds in bone metabolism. Unfortunately, variability among 25(OH)D assays limits clinician ability to monitor vitamin D status, supplementation, and toxicity. 0.5 ml serum was mixed with 0.5 ml 60% acetonitrile 150 mM sodium dodecyl sulfate, vortexed for 30 s and injected into an automatic solid-phase extraction (SPE) system for cleanup-preconcentration, then on-line transferred to a reversed-phase analytical column by a 15% methanol-acetonitrile mobile phase at 1.0 ml/min for individual separation of the target analytes. Ultraviolet detection was performed at 265 nm, 325 nm and 292 for vitamin D metabolites, vitamin A and alpha- and delta-tocopherols, respectively. Detection limits were between 0.0015 and 0.26 microg/ml for the target compounds, the precision (expressed as relative standard deviation) between 0.83 and 3.6% for repeatability and between 1.8 and 4.62% for within laboratory reproducibility. Recoveries between 97-100.2% and 95-99% were obtained for low and high concentrations of the target analytes in serum. The total analysis time was 20 min. The on-line coupling of SPE-HPLC endows the proposed method with reliability, robustness, and user unattendance, making it a useful tool for high-throughput analysis in clinical and research laboratories.

Automatic rock detection for in situ spectroscopy applications on Mars

NASA Astrophysics Data System (ADS)

Mahapatra, Pooja; Foing, Bernard H.

A novel algorithm for rock detection has been developed for effectively utilising Mars rovers, and enabling autonomous selection of target rocks that require close-contact spectroscopic measurements. The algorithm demarcates small rocks in terrain images as seen by cameras on a Mars rover during traverse. This information may be used by the rover for selection of geologically relevant sample rocks, and (in conjunction with a rangefinder) to pick up target samples using a robotic arm for automatic in situ determination of rock composition and mineralogy using, for example, a Raman spectrometer. Determining rock samples within the region that are of specific interest without physically approaching them significantly reduces time, power and risk. Input images in colour are converted to greyscale for intensity analysis. Bilateral filtering is used for texture removal while preserving rock boundaries. Unsharp masking is used for contrast enhance-ment. Sharp contrasts in intensities are detected using Canny edge detection, with thresholds that are calculated from the image obtained after contrast-limited adaptive histogram equalisation of the unsharp masked image. Scale-space representations are then generated by convolving this image with a Gaussian kernel. A scale-invariant blob detector (Laplacian of the Gaussian, LoG) detects blobs independently of their sizes, and therefore requires a multi-scale approach with automatic scale se-lection. The scale-space blob detector consists of convolution of the Canny edge-detected image with a scale-normalised LoG at several scales, and finding the maxima of squared LoG response in scale-space. After the extraction of local intensity extrema, the intensity profiles along rays going out of the local extremum are investigated. An ellipse is fitted to the region determined by significant changes in the intensity profiles. The fitted ellipses are overlaid on the original Mars terrain image for a visual estimation of the rock detection accuracy, and the number of ellipses are counted. Since geometry and illumination have the least effect on small rocks, the proposed algorithm is effective in detecting small rocks (or bigger rocks at larger distances from the camera) that consist of a small fraction of image pixels. Acknowledgements: The first author would like to express her gratitude to the European Space Agency (ESA/ESTEC) and the International Lunar Exploration Working Group (ILEWG) for their support of this work.

Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers.

PubMed

Shu, Jie; Dolman, G E; Duan, Jiang; Qiu, Guoping; Ilyas, Mohammad

2016-04-27

Colour is the most important feature used in quantitative immunohistochemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to confirm malignancy. Statistical modelling is a technique widely used for colour detection in computer vision. We have developed a statistical model of colour detection applicable to detection of stain colour in digital IHC images. Model was first trained by massive colour pixels collected semi-automatically. To speed up the training and detection processes, we removed luminance channel, Y channel of YCbCr colour space and chose 128 histogram bins which is the optimal number. A maximum likelihood classifier is used to classify pixels in digital slides into positively or negatively stained pixels automatically. The model-based tool was developed within ImageJ to quantify targets identified using IHC and histochemistry. The purpose of evaluation was to compare the computer model with human evaluation. Several large datasets were prepared and obtained from human oesophageal cancer, colon cancer and liver cirrhosis with different colour stains. Experimental results have demonstrated the model-based tool achieves more accurate results than colour deconvolution and CMYK model in the detection of brown colour, and is comparable to colour deconvolution in the detection of pink colour. We have also demostrated the proposed model has little inter-dataset variations. A robust and effective statistical model is introduced in this paper. The model-based interactive tool in ImageJ, which can create a visual representation of the statistical model and detect a specified colour automatically, is easy to use and available freely at http://rsb.info.nih.gov/ij/plugins/ihc-toolbox/index.html . Testing to the tool by different users showed only minor inter-observer variations in results.

Neural network for intelligent query of an FBI forensic database

NASA Astrophysics Data System (ADS)

Uvanni, Lee A.; Rainey, Timothy G.; Balasubramanian, Uma; Brettle, Dean W.; Weingard, Fred; Sibert, Robert W.; Birnbaum, Eric

1997-02-01

Examiner is an automated fired cartridge case identification system utilizing a dual-use neural network pattern recognition technology, called the statistical-multiple object detection and location system (S-MODALS) developed by Booz(DOT)Allen & Hamilton, Inc. in conjunction with Rome Laboratory. S-MODALS was originally designed for automatic target recognition (ATR) of tactical and strategic military targets using multisensor fusion [electro-optical (EO), infrared (IR), and synthetic aperture radar (SAR)] sensors. Since S-MODALS is a learning system readily adaptable to problem domains other than automatic target recognition, the pattern matching problem of microscopic marks for firearms evidence was analyzed using S-MODALS. The physics; phenomenology; discrimination and search strategies; robustness requirements; error level and confidence level propagation that apply to the pattern matching problem of military targets were found to be applicable to the ballistic domain as well. The Examiner system uses S-MODALS to rank a set of queried cartridge case images from the most similar to the least similar image in reference to an investigative fired cartridge case image. The paper presents three independent tests and evaluation studies of the Examiner system utilizing the S-MODALS technology for the Federal Bureau of Investigation.

Ultra-Wideband EMI Sensing: Non-Metallic Target Detection and Automatic Classification of Unexploded Ordnance

NASA Astrophysics Data System (ADS)

Sigman, John Brevard

Buried explosive hazards present a pressing problem worldwide. Millions of acres and thousands of sites are contaminated in the United States alone [1, 2]. There are three categories of explosive hazards: metallic, intermediate-electrical conducting (IEC), and non-conducting targets. Metallic target detection and classification by electromagnetic (EM) signature has been the subject of research for many years. Key to the success of this research is modern multi-static Electromagnetic Induction (EMI) sensors, which are able to measure the wideband EMI response from metallic buried targets. However, no hardware solutions exist which can characterize IEC and non-conducting targets. While high-conducting metallic targets exhibit a quadrature peak response for frequencies in a traditional EMI regime under 100 kHz, the response of intermediate-conducting objects manifests at higher frequencies, between 100 kHz and 15 MHz. In addition to high-quality electromagnetic sensor data and robust electromagnetic models, a classification procedure is required to discriminate Targets of Interest (TOI) from clutter. Currently, costly human experts are used for this task. This expense and effort can be spared by using statistical signal processing and machine learning. This thesis has two main parts. In the first part, we explore using the high frequency EMI (HFEMI) band (100 kHz-15 MHz) for detection of carbon fiber UXO, voids, and of materials with characteristics that may be associated with improvised explosive devices (IED). We constructed an HFEMI sensing instrument, and apply the techniques of metal detection to sensing in a band of frequencies which are the transition between the induction and radar bands. In this transition domain, physical considerations and technological issues arise that cannot be solved via the approaches used in either of the bracketing lower and higher frequency ranges. In the second half of this thesis, we present a procedure for automatic classification of UXO. For maximum generality, our algorithm is robust and can handle sparse training examples of multi-class data. This procedure uses an unsupervised starter, semi-supervised techniques to gather training data, and concludes with supervised learning until all TOI are found. Additionally, an inference method for estimating the number of remaining true positives from a partial Receiver Operating Characteristic (ROC) curve is presented and applied to live-site dig histories.

A Novel Detection Platform for Shrimp White Spot Syndrome Virus Using an ICP11-Dependent Immunomagnetic Reduction (IMR) Assay.

PubMed

Liu, Bing-Hsien; Lin, Yu-Chen; Ho, Chia-Shin; Yang, Che-Chuan; Chang, Yun-Tsui; Chang, Jui-Feng; Li, Chun-Yuan; Cheng, Cheng-Shun; Huang, Jiun-Yan; Lee, Yen-Fu; Hsu, Ming-Hung; Lin, Feng-Chun; Wang, Hao-Ching; Lo, Chu-Fang; Yang, Shieh-Yueh; Wang, Han-Ching

2015-01-01

Shrimp white spot disease (WSD), which is caused by white spot syndrome virus (WSSV), is one of the world's most serious shrimp diseases. Our objective in this study was to use an immunomagnetic reduction (IMR) assay to develop a highly sensitive, automatic WSSV detection platform targeted against ICP11 (the most highly expressed WSSV protein). After characterizing the magnetic reagents (Fe3O4 magnetic nanoparticles coated with anti ICP11), the detection limit for ICP11 protein using IMR was approximately 2 x 10(-3) ng/ml, and the linear dynamic range of the assay was 0.1~1 x 10(6) ng/ml. In assays of ICP11 protein in pleopod protein lysates from healthy and WSSV-infected shrimp, IMR signals were successfully detected from shrimp with low WSSV genome copy numbers. We concluded that this IMR assay targeting ICP11 has potential for detecting the WSSV.

Multi-brain fusion and applications to intelligence analysis

NASA Astrophysics Data System (ADS)

Stoica, A.; Matran-Fernandez, A.; Andreou, D.; Poli, R.; Cinel, C.; Iwashita, Y.; Padgett, C.

2013-05-01

In a rapid serial visual presentation (RSVP) images are shown at an extremely rapid pace. Yet, the images can still be parsed by the visual system to some extent. In fact, the detection of specific targets in a stream of pictures triggers a characteristic electroencephalography (EEG) response that can be recognized by a brain-computer interface (BCI) and exploited for automatic target detection. Research funded by DARPA's Neurotechnology for Intelligence Analysts program has achieved speed-ups in sifting through satellite images when adopting this approach. This paper extends the use of BCI technology from individual analysts to collaborative BCIs. We show that the integration of information in EEGs collected from multiple operators results in performance improvements compared to the single-operator case.

An algorithm for automatic target recognition using passive radar and an EKF for estimating aircraft orientation

NASA Astrophysics Data System (ADS)

Ehrman, Lisa M.

2005-07-01

Rather than emitting pulses, passive radar systems rely on "illuminators of opportunity," such as TV and FM radio, to illuminate potential targets. These systems are attractive since they allow receivers to operate without emitting energy, rendering them covert. Until recently, most of the research regarding passive radar has focused on detecting and tracking targets. This dissertation focuses on extending the capabilities of passive radar systems to include automatic target recognition. The target recognition algorithm described in this dissertation uses the radar cross section (RCS) of potential targets, collected over a short period of time, as the key information for target recognition. To make the simulated RCS as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. An extended Kalman filter (EKF) estimates the target's orientation (and uncertainty in the estimate) from velocity measurements obtained from the passive radar tracker. Coupling the aircraft orientation and state with the known antenna locations permits computation of the incident and observed azimuth and elevation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of potential target classes as a function of these angles. Thus, the approximated incident and observed angles allow the appropriate RCS to be extracted from a database of FISC results. Using this process, the RCS of each aircraft in the target class is simulated as though each is executing the same maneuver as the target detected by the system. Two additional scaling processes are required to transform the RCS into a power profile (magnitude only) simulating the signal in the receiver. First, the RCS is scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. Then, the Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern, further scaling the RCS. A Rician likelihood model compares the scaled RCS of the illuminated aircraft with those of the potential targets. To improve the robustness of the result, the algorithm jointly optimizes over feasible orientation profiles and target types via dynamic programming.

A novel spatial-temporal detection method of dim infrared moving small target

NASA Astrophysics Data System (ADS)

Chen, Zhong; Deng, Tao; Gao, Lei; Zhou, Heng; Luo, Song

2014-09-01

Moving small target detection under complex background in infrared image sequence is one of the major challenges of modern military in Early Warning Systems (EWS) and the use of Long-Range Strike (LRS). However, because of the low SNR and undulating background, the infrared moving small target detection is a difficult problem in a long time. To solve this problem, a novel spatial-temporal detection method based on bi-dimensional empirical mode decomposition (EMD) and time-domain difference is proposed in this paper. This method is downright self-data decomposition and do not rely on any transition kernel function, so it has a strong adaptive capacity. Firstly, we generalized the 1D EMD algorithm to the 2D case. In this process, the project has solved serial issues in 2D EMD, such as large amount of data operations, define and identify extrema in 2D case, and two-dimensional signal boundary corrosion. The EMD algorithm studied in this project can be well adapted to the automatic detection of small targets under low SNR and complex background. Secondly, considering the characteristics of moving target, we proposed an improved filtering method based on three-frame difference on basis of the original difference filtering in time-domain, which greatly improves the ability of anti-jamming algorithm. Finally, we proposed a new time-space fusion method based on a combined processing of 2D EMD and improved time-domain differential filtering. And, experimental results show that this method works well in infrared small moving target detection under low SNR and complex background.

Automatic localization of the da Vinci surgical instrument tips in 3-D transrectal ultrasound.

PubMed

Mohareri, Omid; Ramezani, Mahdi; Adebar, Troy K; Abolmaesumi, Purang; Salcudean, Septimiu E

2013-09-01

Robot-assisted laparoscopic radical prostatectomy (RALRP) using the da Vinci surgical system is the current state-of-the-art treatment option for clinically confined prostate cancer. Given the limited field of view of the surgical site in RALRP, several groups have proposed the integration of transrectal ultrasound (TRUS) imaging in the surgical workflow to assist with accurate resection of the prostate and the sparing of the neurovascular bundles (NVBs). We previously introduced a robotic TRUS manipulator and a method for automatically tracking da Vinci surgical instruments with the TRUS imaging plane, in order to facilitate the integration of intraoperative TRUS in RALRP. Rapid and automatic registration of the kinematic frames of the da Vinci surgical system and the robotic TRUS probe manipulator is a critical component of the instrument tracking system. In this paper, we propose a fully automatic registration technique based on automatic 3-D TRUS localization of robot instrument tips pressed against the air-tissue boundary anterior to the prostate. The detection approach uses a multiscale filtering technique to identify and localize surgical instrument tips in the TRUS volume, and could also be used to detect other surface fiducials in 3-D ultrasound. Experiments have been performed using a tissue phantom and two ex vivo tissue samples to show the feasibility of the proposed methods. Also, an initial in vivo evaluation of the system has been carried out on a live anaesthetized dog with a da Vinci Si surgical system and a target registration error (defined as the root mean square distance of corresponding points after registration) of 2.68 mm has been achieved. Results show this method's accuracy and consistency for automatic registration of TRUS images to the da Vinci surgical system.

46 CFR 161.002-2 - Types of fire-protective systems.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., but not be limited to, automatic fire and smoke detecting systems, manual fire alarm systems, sample extraction smoke detection systems, watchman's supervisory systems, and combinations of these systems. (b) Automatic fire detecting systems. For the purpose of this subpart, automatic fire and smoke detecting...

46 CFR 161.002-2 - Types of fire-protective systems.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., but not be limited to, automatic fire and smoke detecting systems, manual fire alarm systems, sample extraction smoke detection systems, watchman's supervisory systems, and combinations of these systems. (b) Automatic fire detecting systems. For the purpose of this subpart, automatic fire and smoke detecting...

Automatic irradiation control by an optical feedback technique for selective retina treatment (SRT) in a rabbit model

NASA Astrophysics Data System (ADS)

Seifert, Eric; Roh, Young-Jung; Fritz, Andreas; Park, Young Gun; Kang, Seungbum; Theisen-Kunde, Dirk; Brinkmann, Ralf

2013-06-01

Selective Retina Therapy (SRT) targets the Retinal Pigment Epithelium (RPE) without effecting neighboring layers as the photoreceptors or the choroid. SRT related RPE defects are ophthalmoscopically invisible. Owing to this invisibility and the variation of the threshold radiant exposure for RPE damage the treating physician does not know whether the treatment was successful or not. Thus measurement techniques enabling a correct dosing are a demanded element in SRT devices. The acquired signal can be used for monitoring or automatic irradiation control. Existing monitoring techniques are based on the detection of micro-bubbles. These bubbles are the origin of RPE cell damage for pulse durations in the ns and μs time regime 5μs. The detection can be performed by optical or acoustical approaches. Monitoring based on an acoustical approach has already been used to study the beneficial effects of SRT on diabetic macula edema and central serous retinopathy. We have developed a first real time feedback technique able to detect micro-bubble induced characteristics in the backscattered laser light fast enough to cease the laser irradiation within a burst. Therefore the laser energy within a burst of at most 30 pulses is increased linearly with every pulse. The laser irradiation is ceased as soon as micro-bubbles are detected. With this automatic approach it was possible to observe invisible lesions, an intact photoreceptor layer and a reconstruction of the RPE within one week.

Location cue validity affects inhibition of return of visual processing.

PubMed

Wright, R D; Richard, C M

2000-01-01

Inhibition-of-return is the process by which visual search for an object positioned among others is biased toward novel rather than previously inspected items. It is thought to occur automatically and to increase search efficiency. We examined this phenomenon by studying the facilitative and inhibitory effects of location cueing on target-detection response times in a search task. The results indicated that facilitation was a reflexive consequence of cueing whereas inhibition appeared to depend on cue informativeness. More specifically, the inhibition-of-return effect occurred only when the cue provided no information about the impending target's location. We suggest that the results are consistent with the notion of two levels of visual processing. The first involves rapid and reflexive operations that underlie the facilitative effects of location cueing on target detection. The second involves a rapid but goal-driven inhibition procedure that the perceiver can invoke if doing so will enhance visual search performance.

POPCORN: a Supervisory Control Simulation for Workload and Performance Research

NASA Technical Reports Server (NTRS)

Hart, S. G.; Battiste, V.; Lester, P. T.

1984-01-01

A multi-task simulation of a semi-automatic supervisory control system was developed to provide an environment in which training, operator strategy development, failure detection and resolution, levels of automation, and operator workload can be investigated. The goal was to develop a well-defined, but realistically complex, task that would lend itself to model-based analysis. The name of the task (POPCORN) reflects the visual display that depicts different task elements milling around waiting to be released and pop out to be performed. The operator's task was to complete each of 100 task elements that ere represented by different symbols, by selecting a target task and entering the desired a command. The simulated automatic system then completed the selected function automatically. Highly significant differences in performance, strategy, and rated workload were found as a function of all experimental manipulations (except reward/penalty).

Does the perception of moving eyes trigger reflexive visual orienting in autism?

PubMed Central

Swettenham, John; Condie, Samantha; Campbell, Ruth; Milne, Elizabeth; Coleman, Mike

2003-01-01

Does movement of the eyes in one or another direction function as an automatic attentional cue to a location of interest? Two experiments explored the directional movement of the eyes in a full face for speed of detection of an aftercoming location target in young people with autism and in control participants. Our aim was to investigate whether a low-level perceptual impairment underlies the delay in gaze following characteristic of autism. The participants' task was to detect a target appearing on the left or right of the screen either 100 ms or 800 ms after a face cue appeared with eyes averting to the left or right. Despite instructions to ignore eye-movement in the face cue, people with autism and control adolescents were quicker to detect targets that had been preceded by an eye movement cue congruent with target location compared with targets preceded by an incongruent eye movement cue. The attention shifts are thought to be reflexive because the cue was to be ignored, and because the effect was found even when cue-target duration was short (100 ms). Because (experiment two) the effect persisted even when the face was inverted, it would seem that the direction of movement of eyes can provide a powerful (involuntary) cue to a location. PMID:12639330

Automatic Target Cueing and Operator Performance with Enhanced APG-70 Synthetic Aperture Radar Imagery.

DTIC Science & Technology

1997-11-01

tasks (Gescheider, 1985; Green & Swets, 1966; Macmillan & Creelman , 1991; See & Kuperman, 1995; See, Riegler, Fitzhugh, & Kuperman, 1996; See, Warm...available for designation and the operator’s ensuing proportion of correct localization responses (Hacker & Ratcliff, 1979; Macmillan & Creelman ...substituted for hits (Macmillan & Creelman , 1991). The Present Study The techniques of signal detection theory were applied in the present study in

Anomaly-Based Intrusion Detection Systems Utilizing System Call Data

DTIC Science & Technology

2012-03-01

Functionality Description Persistence mechanism Mimicry technique Camouflage malware image: • renaming its image • appending its image to victim...particular industrial plant . Exactly which one was targeted still remains unknown, however a majority of the attacks took place in Iran [24]. Due... plant to unstable phase and eventually physical damage. It is interesting to note that a particular block of code - block DB8061 is automatically

A real-time optical tracking and measurement processing system for flying targets.

PubMed

Guo, Pengyu; Ding, Shaowen; Zhang, Hongliang; Zhang, Xiaohu

2014-01-01

Optical tracking and measurement for flying targets is unlike the close range photography under a controllable observation environment, which brings extreme conditions like diverse target changes as a result of high maneuver ability and long cruising range. This paper first designed and realized a distributed image interpretation and measurement processing system to achieve resource centralized management, multisite simultaneous interpretation and adaptive estimation algorithm selection; then proposed a real-time interpretation method which contains automatic foreground detection, online target tracking, multiple features location, and human guidance. An experiment is carried out at performance and efficiency evaluation of the method by semisynthetic video. The system can be used in the field of aerospace tests like target analysis including dynamic parameter, transient states, and optical physics characteristics, with security control.

A Real-Time Optical Tracking and Measurement Processing System for Flying Targets

PubMed Central

Guo, Pengyu; Ding, Shaowen; Zhang, Hongliang; Zhang, Xiaohu

2014-01-01

Optical tracking and measurement for flying targets is unlike the close range photography under a controllable observation environment, which brings extreme conditions like diverse target changes as a result of high maneuver ability and long cruising range. This paper first designed and realized a distributed image interpretation and measurement processing system to achieve resource centralized management, multisite simultaneous interpretation and adaptive estimation algorithm selection; then proposed a real-time interpretation method which contains automatic foreground detection, online target tracking, multiple features location, and human guidance. An experiment is carried out at performance and efficiency evaluation of the method by semisynthetic video. The system can be used in the field of aerospace tests like target analysis including dynamic parameter, transient states, and optical physics characteristics, with security control. PMID:24987748

Computer systems for automatic earthquake detection

USGS Publications Warehouse

Stewart, S.W.

1974-01-01

U.S Geological Survey seismologists in Menlo park, California, are utilizing the speed, reliability, and efficiency of minicomputers to monitor seismograph stations and to automatically detect earthquakes. An earthquake detection computer system, believed to be the only one of its kind in operation, automatically reports about 90 percent of all local earthquakes recorded by a network of over 100 central California seismograph stations. The system also monitors the stations for signs of malfunction or abnormal operation. Before the automatic system was put in operation, all of the earthquakes recorded had to be detected by manually searching the records, a time-consuming process. With the automatic detection system, the stations are efficiently monitored continuously. 

[Medical computer-aided detection method based on deep learning].

PubMed

Tao, Pan; Fu, Zhongliang; Zhu, Kai; Wang, Lili

2018-03-01

This paper performs a comprehensive study on the computer-aided detection for the medical diagnosis with deep learning. Based on the region convolution neural network and the prior knowledge of target, this algorithm uses the region proposal network, the region of interest pooling strategy, introduces the multi-task loss function: classification loss, bounding box localization loss and object rotation loss, and optimizes it by end-to-end. For medical image it locates the target automatically, and provides the localization result for the next stage task of segmentation. For the detection of left ventricular in echocardiography, proposed additional landmarks such as mitral annulus, endocardial pad and apical position, were used to estimate the left ventricular posture effectively. In order to verify the robustness and effectiveness of the algorithm, the experimental data of ultrasonic and nuclear magnetic resonance images are selected. Experimental results show that the algorithm is fast, accurate and effective.

Detection and identification of concealed weapons using matrix pencil

NASA Astrophysics Data System (ADS)

Adve, Raviraj S.; Thayaparan, Thayananthan

2011-06-01

The detection and identification of concealed weapons is an extremely hard problem due to the weak signature of the target buried within the much stronger signal from the human body. This paper furthers the automatic detection and identification of concealed weapons by proposing the use of an effective approach to obtain the resonant frequencies in a measurement. The technique, based on Matrix Pencil, a scheme for model based parameter estimation also provides amplitude information, hence providing a level of confidence in the results. Of specific interest is the fact that Matrix Pencil is based on a singular value decomposition, making the scheme robust against noise.

Automatic multiresolution age-related macular degeneration detection from fundus images

NASA Astrophysics Data System (ADS)

Garnier, Mickaël.; Hurtut, Thomas; Ben Tahar, Houssem; Cheriet, Farida

2014-03-01

Age-related Macular Degeneration (AMD) is a leading cause of legal blindness. As the disease progress, visual loss occurs rapidly, therefore early diagnosis is required for timely treatment. Automatic, fast and robust screening of this widespread disease should allow an early detection. Most of the automatic diagnosis methods in the literature are based on a complex segmentation of the drusen, targeting a specific symptom of the disease. In this paper, we present a preliminary study for AMD detection from color fundus photographs using a multiresolution texture analysis. We analyze the texture at several scales by using a wavelet decomposition in order to identify all the relevant texture patterns. Textural information is captured using both the sign and magnitude components of the completed model of Local Binary Patterns. An image is finally described with the textural pattern distributions of the wavelet coefficient images obtained at each level of decomposition. We use a Linear Discriminant Analysis for feature dimension reduction, to avoid the curse of dimensionality problem, and image classification. Experiments were conducted on a dataset containing 45 images (23 healthy and 22 diseased) of variable quality and captured by different cameras. Our method achieved a recognition rate of 93:3%, with a specificity of 95:5% and a sensitivity of 91:3%. This approach shows promising results at low costs that in agreement with medical experts as well as robustness to both image quality and fundus camera model.

Automatic extraction of via in the CT image of PCB

NASA Astrophysics Data System (ADS)

Liu, Xifeng; Hu, Yuwei

2018-04-01

In modern industry, the nondestructive testing of printed circuit board (PCB) can prevent effectively the system failure and is becoming more and more important. In order to detect the via in the PCB base on the CT image automatically accurately and reliably, a novel algorithm for via extraction based on weighting stack combining the morphologic character of via is designed. Every slice data in the vertical direction of the PCB is superimposed to enhanced vias target. The OTSU algorithm is used to segment the slice image. OTSU algorithm of thresholding gray level images is efficient for separating an image into two classes where two types of fairly distinct classes exist in the image. Randomized Hough Transform was used to locate the region of via in the segmented binary image. Then the 3D reconstruction of via based on sequence slice images was done by volume rendering. The accuracy of via positioning and detecting from a CT images of PCB was demonstrated by proposed algorithm. It was found that the method is good in veracity and stability for detecting of via in three dimensional.

Automatic Censoring CFAR Detector Based on Ordered Data Difference for Low-Flying Helicopter Safety

PubMed Central

Jiang, Wen; Huang, Yulin; Yang, Jianyu

2016-01-01

Being equipped with a millimeter-wave radar allows a low-flying helicopter to sense the surroundings in real time, which significantly increases its safety. However, nonhomogeneous clutter environments, such as a multiple target situation and a clutter edge environment, can dramatically affect the radar signal detection performance. In order to improve the radar signal detection performance in nonhomogeneous clutter environments, this paper proposes a new automatic censored cell averaging CFAR detector. The proposed CFAR detector does not require any prior information about the background environment and uses the hypothesis test of the first-order difference (FOD) result of ordered data to reject the unwanted samples in the reference window. After censoring the unwanted ranked cells, the remaining samples are combined to form an estimate of the background power level, thus getting better radar signal detection performance. The simulation results show that the FOD-CFAR detector provides low loss CFAR performance in a homogeneous environment and also performs robustly in nonhomogeneous environments. Furthermore, the measured results of a low-flying helicopter validate the basic performance of the proposed method. PMID:27399714

Automatic flatness detection system for micro part

NASA Astrophysics Data System (ADS)

Luo, Yi; Wang, Xiaodong; Shan, Zhendong; Li, Kehong

2016-01-01

An automatic flatness detection system for micro rings is developed. It is made up of machine vision module, ring supporting module and control system. An industry CCD camera with the resolution of 1628×1236 pixel, a telecentric with magnification of two, and light sources are used to collect the vision information. A rotary stage with a polished silicon wafer is used to support the ring. The silicon wafer provides a mirror image and doubles the gap caused by unevenness of the ring. The control system comprise an industry computer and software written in LabVIEW Get Kernel and Convolute Function are selected to reduce noise and distortion, Laplacian Operator is used to sharp the image, and IMAQ Threshold function is used to separate the target object from the background. Based on this software, system repeating precision is 2.19 μm, less than one pixel. The designed detection system can easily identify the ring warpage larger than 5 μm, and if the warpage is less than 25 μm, it can be used in ring assembly and satisfied the final positionary and perpendicularity error requirement of the component.

CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction.

PubMed

Xu, Youjun; Wang, Shiwei; Hu, Qiwan; Gao, Shuaishi; Ma, Xiaomin; Zhang, Weilin; Shen, Yihang; Chen, Fangjin; Lai, Luhua; Pei, Jianfeng

2018-05-10

CavityPlus is a web server that offers protein cavity detection and various functional analyses. Using protein three-dimensional structural information as the input, CavityPlus applies CAVITY to detect potential binding sites on the surface of a given protein structure and rank them based on ligandability and druggability scores. These potential binding sites can be further analysed using three submodules, CavPharmer, CorrSite, and CovCys. CavPharmer uses a receptor-based pharmacophore modelling program, Pocket, to automatically extract pharmacophore features within cavities. CorrSite identifies potential allosteric ligand-binding sites based on motion correlation analyses between cavities. CovCys automatically detects druggable cysteine residues, which is especially useful to identify novel binding sites for designing covalent allosteric ligands. Overall, CavityPlus provides an integrated platform for analysing comprehensive properties of protein binding cavities. Such analyses are useful for many aspects of drug design and discovery, including target selection and identification, virtual screening, de novo drug design, and allosteric and covalent-binding drug design. The CavityPlus web server is freely available at http://repharma.pku.edu.cn/cavityplus or http://www.pkumdl.cn/cavityplus.

Autonomous collection of dynamically-cued multi-sensor imagery

NASA Astrophysics Data System (ADS)

Daniel, Brian; Wilson, Michael L.; Edelberg, Jason; Jensen, Mark; Johnson, Troy; Anderson, Scott

2011-05-01

The availability of imagery simultaneously collected from sensors of disparate modalities enhances an image analyst's situational awareness and expands the overall detection capability to a larger array of target classes. Dynamic cooperation between sensors is increasingly important for the collection of coincident data from multiple sensors either on the same or on different platforms suitable for UAV deployment. Of particular interest is autonomous collaboration between wide area survey detection, high-resolution inspection, and RF sensors that span large segments of the electromagnetic spectrum. The Naval Research Laboratory (NRL) in conjunction with the Space Dynamics Laboratory (SDL) is building sensors with such networked communications capability and is conducting field tests to demonstrate the feasibility of collaborative sensor data collection and exploitation. Example survey / detection sensors include: NuSAR (NRL Unmanned SAR), a UAV compatible synthetic aperture radar system; microHSI, an NRL developed lightweight hyper-spectral imager; RASAR (Real-time Autonomous SAR), a lightweight podded synthetic aperture radar; and N-WAPSS-16 (Nighttime Wide-Area Persistent Surveillance Sensor-16Mpix), a MWIR large array gimbaled system. From these sensors, detected target cues are automatically sent to the NRL/SDL developed EyePod, a high-resolution, narrow FOV EO/IR sensor, for target inspection. In addition to this cooperative data collection, EyePod's real-time, autonomous target tracking capabilities will be demonstrated. Preliminary results and target analysis will be presented.

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.

Convolutional neural network using generated data for SAR ATR with limited samples

NASA Astrophysics Data System (ADS)

Cong, Longjian; Gao, Lei; Zhang, Hui; Sun, Peng

2018-03-01

Being able to adapt all weather at all times, it has been a hot research topic that using Synthetic Aperture Radar(SAR) for remote sensing. Despite all the well-known advantages of SAR, it is hard to extract features because of its unique imaging methodology, and this challenge attracts the research interest of traditional Automatic Target Recognition(ATR) methods. With the development of deep learning technologies, convolutional neural networks(CNNs) give us another way out to detect and recognize targets, when a huge number of samples are available, but this premise is often not hold, when it comes to monitoring a specific type of ships. In this paper, we propose a method to enhance the performance of Faster R-CNN with limited samples to detect and recognize ships in SAR images.

Using Multiple Space Assests with In-Situ Measurements to Track Flooding in Thailand

NASA Technical Reports Server (NTRS)

Chien, Steve; Doubleday, Joshua; Mclaren, David; Tran, Daniel; Khunboa, Chatchai; Leelapatra, Watis; Pergamon, Vichain; Tanpipat, Veerachai; Chitradon, Royal; Boonya-aroonnet, Surajate;

Passive Polarimetric Information Processing for Target Classification

NASA Astrophysics Data System (ADS)

Sadjadi, Firooz; Sadjadi, Farzad

Polarimetric sensing is an area of active research in a variety of applications. In particular, the use of polarization diversity has been shown to improve performance in automatic target detection and recognition. Within the diverse scope of polarimetric sensing, the field of passive polarimetric sensing is of particular interest. This chapter presents several new methods for gathering in formation using such passive techniques. One method extracts three-dimensional (3D) information and surface properties using one or more sensors. Another method extracts scene-specific algebraic expressions that remain unchanged under polariza tion transformations (such as along the transmission path to the sensor).

Cueing spatial attention through timing and probability.

PubMed

Girardi, Giovanna; Antonucci, Gabriella; Nico, Daniele

2013-01-01

Even when focused on an effortful task we retain the ability to detect salient environmental information, and even irrelevant visual stimuli can be automatically detected. However, to which extent unattended information affects attentional control is not fully understood. Here we provide evidences of how the brain spontaneously organizes its cognitive resources by shifting attention between a selective-attending and a stimulus-driven modality within a single task. Using a spatial cueing paradigm we investigated the effect of cue-target asynchronies as a function of their probabilities of occurrence (i.e., relative frequency). Results show that this accessory information modulates attentional shifts. A valid spatial cue improved participants' performance as compared to an invalid one only in trials in which target onset was highly predictable because of its more robust occurrence. Conversely, cuing proved ineffective when spatial cue and target were associated according to a less frequent asynchrony. These patterns of response depended on asynchronies' probability and not on their duration. Our findings clearly demonstrate that through a fine decision-making, performed trial-by-trial, the brain utilizes implicit information to decide whether or not voluntarily shifting spatial attention. As if according to a cost-planning strategy, the cognitive effort of shifting attention depending on the cue is performed only when the expected advantages are higher. In a trade-off competition for cognitive resources, voluntary/automatic attending may thus be a more complex process than expected. Copyright © 2011 Elsevier Ltd. All rights reserved.

PCR-Free Detection of Genetically Modified Organisms Using Magnetic Capture Technology and Fluorescence Cross-Correlation Spectroscopy

PubMed Central

Zhou, Xiaoming; Xing, Da; Tang, Yonghong; Chen, Wei R.

2009-01-01

The safety of genetically modified organisms (GMOs) has attracted much attention recently. Polymerase chain reaction (PCR) amplification is a common method used in the identification of GMOs. However, a major disadvantage of PCR is the potential amplification of non-target DNA, causing false-positive identification. Thus, there remains a need for a simple, reliable and ultrasensitive method to identify and quantify GMO in crops. This report is to introduce a magnetic bead-based PCR-free method for rapid detection of GMOs using dual-color fluorescence cross-correlation spectroscopy (FCCS). The cauliflower mosaic virus 35S (CaMV35S) promoter commonly used in transgenic products was targeted. CaMV35S target was captured by a biotin-labeled nucleic acid probe and then purified using streptavidin-coated magnetic beads through biotin-streptavidin linkage. The purified target DNA fragment was hybridized with two nucleic acid probes labeled respectively by Rhodamine Green and Cy5 dyes. Finally, FCCS was used to detect and quantify the target DNA fragment through simultaneously detecting the fluorescence emissions from the two dyes. In our study, GMOs in genetically engineered soybeans and tomatoes were detected, using the magnetic bead-based PCR-free FCCS method. A detection limit of 50 pM GMOs target was achieved and PCR-free detection of GMOs from 5 µg genomic DNA with magnetic capture technology was accomplished. Also, the accuracy of GMO determination by the FCCS method is verified by spectrophotometry at 260 nm using PCR amplified target DNA fragment from GM tomato. The new method is rapid and effective as demonstrated in our experiments and can be easily extended to high-throughput and automatic screening format. We believe that the new magnetic bead-assisted FCCS detection technique will be a useful tool for PCR-free GMOs identification and other specific nucleic acids. PMID:19956680

Automatic Surveying For Hazard Prevention On Glacier De GiÉtro, Switzerland

NASA Astrophysics Data System (ADS)

Bauder, A.; Funk, M.; Bösch, H.

Breaking off of large ice masses from the steep tongue of Glacier de Giétro may endanger a nearby reservoir. Such a falling ice mass could cause an oversplash over the dam at timeof a nearly filled lake. For this reason the glacier has been monitored intensively since the 1960's. An automatic theodolite was installed three years ago. It allows continuous displacement measurements of several targets on the glacier in order to detect short-term acceleration events. The installation includes a telemetric data transmission, which provides for immediate recognition of hazardous situations and early alarming. The obtained data were analysed in terms of precision and performance of the applied method. A high temporal resolution was gained. The comparison with traditional ob- servations shows clearly the potential of modern instruments to improve monitoring schems. We summarize the main results of this study and discuss the applicability of a modern motorized theodolite with target tracking and recognition ability for moni- toring purposes.

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

Automatic panoramic thermal integrated sensor

NASA Astrophysics Data System (ADS)

Gutin, Mikhail A.; Tsui, Eddy K.; Gutin, Olga N.

2005-05-01

Historically, the US Army has recognized the advantages of panoramic imagers with high image resolution: increased area coverage with fewer cameras, instantaneous full horizon detection, location and tracking of multiple targets simultaneously, extended range, and others. The novel ViperViewTM high-resolution panoramic thermal imager is the heart of the Automatic Panoramic Thermal Integrated Sensor (APTIS), being jointly developed by Applied Science Innovative, Inc. (ASI) and the Armament Research, Development and Engineering Center (ARDEC) in support of the Future Combat Systems (FCS) and the Intelligent Munitions Systems (IMS). The APTIS is anticipated to operate as an intelligent node in a wireless network of multifunctional nodes that work together to improve situational awareness (SA) in many defense and offensive operations, as well as serve as a sensor node in tactical Intelligence Surveillance Reconnaissance (ISR). The ViperView is as an aberration-corrected omnidirectional imager with small optics designed to match the resolution of a 640x480 pixels IR camera with improved image quality for longer range target detection, classification, and tracking. The same approach is applicable to panoramic cameras working in the visible spectral range. Other components of the ATPIS sensor suite include ancillary sensors, advanced power management, and wakeup capability. This paper describes the development status of the APTIS system.

Automatic detection of confusion in elderly users of a web-based health instruction video.

PubMed

Postma-Nilsenová, Marie; Postma, Eric; Tates, Kiek

2015-06-01

Because of cognitive limitations and lower health literacy, many elderly patients have difficulty understanding verbal medical instructions. Automatic detection of facial movements provides a nonintrusive basis for building technological tools supporting confusion detection in healthcare delivery applications on the Internet. Twenty-four elderly participants (70-90 years old) were recorded while watching Web-based health instruction videos involving easy and complex medical terminology. Relevant fragments of the participants' facial expressions were rated by 40 medical students for perceived level of confusion and analyzed with automatic software for facial movement recognition. A computer classification of the automatically detected facial features performed more accurately and with a higher sensitivity than the human observers (automatic detection and classification, 64% accuracy, 0.64 sensitivity; human observers, 41% accuracy, 0.43 sensitivity). A drill-down analysis of cues to confusion indicated the importance of the eye and eyebrow region. Confusion caused by misunderstanding of medical terminology is signaled by facial cues that can be automatically detected with currently available facial expression detection technology. The findings are relevant for the development of Web-based services for healthcare consumers.

Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method

PubMed Central

Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

2016-01-01

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis. PMID:28029121

Target Transformation Constrained Sparse Unmixing (ttcsu) Algorithm for Retrieving Hydrous Minerals on Mars: Application to Southwest Melas Chasma

NASA Astrophysics Data System (ADS)

Lin, H.; Zhang, X.; Wu, X.; Tarnas, J. D.; Mustard, J. F.

2018-04-01

Quantitative analysis of hydrated minerals from hyperspectral remote sensing data is fundamental for understanding Martian geologic process. Because of the difficulties for selecting endmembers from hyperspectral images, a sparse unmixing algorithm has been proposed to be applied to CRISM data on Mars. However, it's challenge when the endmember library increases dramatically. Here, we proposed a new methodology termed Target Transformation Constrained Sparse Unmixing (TTCSU) to accurately detect hydrous minerals on Mars. A new version of target transformation technique proposed in our recent work was used to obtain the potential detections from CRISM data. Sparse unmixing constrained with these detections as prior information was applied to CRISM single-scattering albedo images, which were calculated using a Hapke radiative transfer model. This methodology increases success rate of the automatic endmember selection of sparse unmixing and could get more accurate abundances. CRISM images with well analyzed in Southwest Melas Chasma was used to validate our methodology in this study. The sulfates jarosite was detected from Southwest Melas Chasma, the distribution is consistent with previous work and the abundance is comparable. More validations will be done in our future work.

Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method.

PubMed

Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

2016-12-24

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis.

TU-FG-201-03: Automatic Pre-Delivery Verification Using Statistical Analysis of Consistencies in Treatment Plan Parameters by the Treatment Site and Modality

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

Liu, S; Wu, Y; Chang, X

Purpose: A novel computer software system, namely APDV (Automatic Pre-Delivery Verification), has been developed for verifying patient treatment plan parameters right prior to treatment deliveries in order to automatically detect and prevent catastrophic errors. Methods: APDV is designed to continuously monitor new DICOM plan files on the TMS computer at the treatment console. When new plans to be delivered are detected, APDV checks the consistencies of plan parameters and high-level plan statistics using underlying rules and statistical properties based on given treatment site, technique and modality. These rules were quantitatively derived by retrospectively analyzing all the EBRT treatment plans ofmore » the past 8 years at authors’ institution. Therapists and physicists will be notified with a warning message displayed on the TMS computer if any critical errors are detected, and check results, confirmation, together with dismissal actions will be saved into database for further review. Results: APDV was implemented as a stand-alone program using C# to ensure required real time performance. Mean values and standard deviations were quantitatively derived for various plan parameters including MLC usage, MU/cGy radio, beam SSD, beam weighting, and the beam gantry angles (only for lateral targets) per treatment site, technique and modality. 2D-based rules of combined MU/cGy ratio and averaged SSD values were also derived using joint probabilities of confidence error ellipses. The statistics of these major treatment plan parameters quantitatively evaluate the consistency of any treatment plans which facilitates automatic APDV checking procedures. Conclusion: APDV could be useful in detecting and preventing catastrophic errors immediately before treatment deliveries. Future plan including automatic patient identify and patient setup checks after patient daily images are acquired by the machine and become available on the TMS computer. This project is supported by the Agency for Healthcare Research and Quality (AHRQ) under award 1R01HS0222888. The senior author received research grants from ViewRay Inc. and Varian Medical System.« less

Automatic segmentation of equine larynx for diagnosis of laryngeal hemiplegia

NASA Astrophysics Data System (ADS)

Salehin, Md. Musfequs; Zheng, Lihong; Gao, Junbin

2013-10-01

This paper presents an automatic segmentation method for delineation of the clinically significant contours of the equine larynx from an endoscopic image. These contours are used to diagnose the most common disease of horse larynx laryngeal hemiplegia. In this study, hierarchal structured contour map is obtained by the state-of-the-art segmentation algorithm, gPb-OWT-UCM. The conic-shaped outer boundary of equine larynx is extracted based on Pascal's theorem. Lastly, Hough Transformation method is applied to detect lines related to the edges of vocal folds. The experimental results show that the proposed approach has better performance in extracting the targeted contours of equine larynx than the results of using only the gPb-OWT-UCM method.

APPARATUS FOR MEASURING NEUTRON CROSS SECTIONS

DOEpatents

Cranberg, L.

1959-07-14

An apparatus is described for analyzing the nuclear reaction products resulting from impingement of nuclear particles against a selected target material and automatically recording the number of reaction prcducts in selected energy levels. The target is bombarded by ions from a particle accelerator and the target potential is cyclicly varied over a particular energy range by a modulator. A nuclear reaction detector is lccated adjacent the target to produce pulses for each reaction product. The output from the detector and the modulator are coupled to a function sampler, for generating a pulse in respcnse to each detected event having an amplitude proportional to the amplitude of the instantaneous target potential. The later pulses are coupled to a multichannel analyzer, whereby nuclear reactions are recorded in appropriate channels of the analyzer in correspcndence with the magnitude of the energy of the impinging ions.

Design and application of a structured phantom for detection performance comparison between breast tomosynthesis and digital mammography

NASA Astrophysics Data System (ADS)

Cockmartin, L.; Marshall, N. W.; Zhang, G.; Lemmens, K.; Shaheen, E.; Van Ongeval, C.; Fredenberg, E.; Dance, D. R.; Salvagnini, E.; Michielsen, K.; Bosmans, H.

2017-02-01

This paper introduces and applies a structured phantom with inserted target objects for the comparison of detection performance of digital breast tomosynthesis (DBT) against 2D full field digital mammography (FFDM). The phantom consists of a 48 mm thick breast-shaped polymethyl methacrylate (PMMA) container filled with water and PMMA spheres of different diameters. Three-dimensionally (3D) printed spiculated masses (diameter range: 3.8-9.7 mm) and non-spiculated masses (1.6-6.2 mm) along with microcalcifications (90-250 µm) were inserted as targets. Reproducibility of the phantom application was studied on a single system using 30 acquisitions. Next, the phantom was evaluated on five different combined FFDM & DBT systems and target detection was compared for FFDM and DBT modes. Ten phantom images in both FFDM and DBT modes were acquired on these 5 systems using automatic exposure control. Five readers evaluated target detectability. Images were read with the four-alternative forced-choice (4-AFC) paradigm, with always one segment including a target and 3 normal background segments. The percentage of correct responses (PC) was assessed based on 10 trials of each reader for each object type, size and imaging modality. Additionally, detection threshold diameters at 62.5 PC were assessed via non-linear regression fitting of the psychometric curve. The reproducibility study showed no significant differences in PC values. Evaluation of target detection in FFDM showed that microcalcification detection thresholds ranged between 110 and 118 µm and were similar compared to the detection in DBT (range of 106-158 µm). In DBT, detection of both mass types increased significantly (p  =  0.0001 and p  =  0.0002 for non-spiculated and spiculated masses respectively) compared to FFDM, achieving almost 100% detection for all spiculated mass diameters. In conclusion, a structured phantom with inserted targets was able to show evidence for detectability differences between FFDM and DBT modes for five commercial systems. This phantom has potential for application in task-based assessment at acceptance and commissioning testing of DBT systems.

Calibration Method for IATS and Application in Multi-Target Monitoring Using Coded Targets

NASA Astrophysics Data System (ADS)

Zhou, Yueyin; Wagner, Andreas; Wunderlich, Thomas; Wasmeier, Peter

2017-06-01

The technique of Image Assisted Total Stations (IATS) has been studied for over ten years and is composed of two major parts: one is the calibration procedure which combines the relationship between the camera system and the theodolite system; the other is the automatic target detection on the image by various methods of photogrammetry or computer vision. Several calibration methods have been developed, mostly using prototypes with an add-on camera rigidly mounted on the total station. However, these prototypes are not commercially available. This paper proposes a calibration method based on Leica MS50 which has two built-in cameras each with a resolution of 2560 × 1920 px: an overview camera and a telescope (on-axis) camera. Our work in this paper is based on the on-axis camera which uses the 30-times magnification of the telescope. The calibration consists of 7 parameters to estimate. We use coded targets, which are common tools in photogrammetry for orientation, to detect different targets in IATS images instead of prisms and traditional ATR functions. We test and verify the efficiency and stability of this monitoring method with multi-target.

A comparison of machine learning techniques for detection of drug target articles.

PubMed

Danger, Roxana; Segura-Bedmar, Isabel; Martínez, Paloma; Rosso, Paolo

2010-12-01

Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure. Copyright © 2010 Elsevier Inc. All rights reserved.

An automatic target recognition system based on SAR image

NASA Astrophysics Data System (ADS)

Li, Qinfu; Wang, Jinquan; Zhao, Bo; Luo, Furen; Xu, Xiaojian

2009-10-01

In this paper, an automatic target recognition (ATR) system based on synthetic aperture radar (SAR) is proposed. This ATR system can play an important role in the simulation of up-to-data battlefield environment and be used in ATR research. To establish an integral and available system, the processing of SAR image was divided into four main stages which are de-noise, detection, cluster-discrimination and segment-recognition, respectively. The first three stages are used for searching region of interest (ROI). Once the ROIs are extracted, the recognition stage will be taken to compute the similarity between the ROIs and the templates in the electromagnetic simulation software National Electromagnetic Scattering Code (NESC). Due to the lack of the SAR raw data, the electromagnetic simulated images are added to the measured SAR background to simulate the battlefield environment8. The purpose of the system is to find the ROIs which can be the artificial military targets such as tanks, armored cars and so on and to categorize the ROIs into the right classes according to the existing templates. From the results we can see that the proposed system achieves a satisfactory result.

Who needs a referee? How incorrect basketball actions are automatically detected by basketball players' brain

PubMed Central

Proverbio, Alice Mado; Crotti, Nicola; Manfredi, Mirella; Adorni, Roberta; Zani, Alberto

2012-01-01

While the existence of a mirror neuron system (MNS) representing and mirroring simple purposeful actions (such as reaching) is known, neural mechanisms underlying the representation of complex actions (such as ballet, fencing, etc.) that are learned by imitation and exercise are not well understood. In this study, correct and incorrect basketball actions were visually presented to professional basketball players and naïve viewers while their EEG was recorded. The participants had to respond to rare targets (unanimated scenes). No category or group differences were found at perceptual level, ruling out the possibility that correct actions might be more visually familiar. Large, anterior N400 responses of event-related brain potentials to incorrectly performed basketball actions were recorded in skilled brains only. The swLORETA inverse solution for incorrect–correct contrast showed that the automatic detection of action ineffectiveness/incorrectness involved the fronto/parietal MNS, the cerebellum, the extra-striate body area, and the superior temporal sulcus. PMID:23181191

Thermal infrared panoramic imaging sensor

NASA Astrophysics Data System (ADS)

Gutin, Mikhail; Tsui, Eddy K.; Gutin, Olga; Wang, Xu-Ming; Gutin, Alexey

2006-05-01

Panoramic cameras offer true real-time, 360-degree coverage of the surrounding area, valuable for a variety of defense and security applications, including force protection, asset protection, asset control, security including port security, perimeter security, video surveillance, border control, airport security, coastguard operations, search and rescue, intrusion detection, and many others. Automatic detection, location, and tracking of targets outside protected area ensures maximum protection and at the same time reduces the workload on personnel, increases reliability and confidence of target detection, and enables both man-in-the-loop and fully automated system operation. Thermal imaging provides the benefits of all-weather, 24-hour day/night operation with no downtime. In addition, thermal signatures of different target types facilitate better classification, beyond the limits set by camera's spatial resolution. The useful range of catadioptric panoramic cameras is affected by their limited resolution. In many existing systems the resolution is optics-limited. Reflectors customarily used in catadioptric imagers introduce aberrations that may become significant at large camera apertures, such as required in low-light and thermal imaging. Advantages of panoramic imagers with high image resolution include increased area coverage with fewer cameras, instantaneous full horizon detection, location and tracking of multiple targets simultaneously, extended range, and others. The Automatic Panoramic Thermal Integrated Sensor (APTIS), being jointly developed by Applied Science Innovative, Inc. (ASI) and the Armament Research, Development and Engineering Center (ARDEC) combines the strengths of improved, high-resolution panoramic optics with thermal imaging in the 8 - 14 micron spectral range, leveraged by intelligent video processing for automated detection, location, and tracking of moving targets. The work in progress supports the Future Combat Systems (FCS) and the Intelligent Munitions Systems (IMS). The APTIS is anticipated to operate as an intelligent node in a wireless network of multifunctional nodes that work together to serve in a wide range of applications of homeland security, as well as serve the Army in tasks of improved situational awareness (SA) in defense and offensive operations, and as a sensor node in tactical Intelligence Surveillance Reconnaissance (ISR). The novel ViperView TM high-resolution panoramic thermal imager is the heart of the APTIS system. It features an aberration-corrected omnidirectional imager with small optics designed to match the resolution of a 640x480 pixels IR camera with improved image quality for longer range target detection, classification, and tracking. The same approach is applicable to panoramic cameras working in the visible spectral range. Other components of the ATPIS system include network communications, advanced power management, and wakeup capability. Recent developments include image processing, optical design being expanded into the visible spectral range, and wireless communications design. This paper describes the development status of the APTIS system.

Differences between automatically detected and steady-state fractional flow reserve.

PubMed

Härle, Tobias; Meyer, Sven; Vahldiek, Felix; Elsässer, Albrecht

2016-02-01

Measurement of fractional flow reserve (FFR) has become a standard diagnostic tool in the catheterization laboratory. FFR evaluation studies were based on pressure recordings during steady-state maximum hyperemia. Commercially available computer systems detect the lowest Pd/Pa ratio automatically, which might not always be measured during steady-state hyperemia. We sought to compare the automatically detected FFR and true steady-state FFR. Pressure measurement traces of 105 coronary lesions from 77 patients with intermediate coronary lesions or multivessel disease were reviewed. In all patients, hyperemia had been achieved by intravenous adenosine administration using a dosage of 140 µg/kg/min. In 42 lesions (40%) automatically detected FFR was lower than true steady-state FFR. Mean bias was 0.009 (standard deviation 0.015, limits of agreement -0.02, 0.037). In 4 lesions (3.8%) both methods lead to different treatment recommendations, in all 4 cases instantaneous wave-free ratio confirmed steady-state FFR. Automatically detected FFR was slightly lower than steady-state FFR in more than one-third of cases. Consequently, interpretation of automatically detected FFR values closely below the cutoff value requires special attention.

Methods for automatically analyzing humpback song units.

PubMed

Rickwood, Peter; Taylor, Andrew

2008-03-01

This paper presents mathematical techniques for automatically extracting and analyzing bioacoustic signals. Automatic techniques are described for isolation of target signals from background noise, extraction of features from target signals and unsupervised classification (clustering) of the target signals based on these features. The only user-provided inputs, other than raw sound, is an initial set of signal processing and control parameters. Of particular note is that the number of signal categories is determined automatically. The techniques, applied to hydrophone recordings of humpback whales (Megaptera novaeangliae), produce promising initial results, suggesting that they may be of use in automated analysis of not only humpbacks, but possibly also in other bioacoustic settings where automated analysis is desirable.

Rendezvous terminal phase automatic braking sequencing and targeting. [for space shuttle orbiter

NASA Technical Reports Server (NTRS)

Kachmar, P. M.

1973-01-01

The purpose of the rendezvous terminal phase braking program is to provide the means of automatically bringing the primary orbiter within desired station keeping boundaries relative to the target satellite. A detailed discussion is presented on the braking program and its navigation, targeting, and guidance functions.

Automated design of genomic Southern blot probes

PubMed Central

2010-01-01

Background Sothern blotting is a DNA analysis technique that has found widespread application in molecular biology. It has been used for gene discovery and mapping and has diagnostic and forensic applications, including mutation detection in patient samples and DNA fingerprinting in criminal investigations. Southern blotting has been employed as the definitive method for detecting transgene integration, and successful homologous recombination in gene targeting experiments. The technique employs a labeled DNA probe to detect a specific DNA sequence in a complex DNA sample that has been separated by restriction-digest and gel electrophoresis. Critically for the technique to succeed the probe must be unique to the target locus so as not to cross-hybridize to other endogenous DNA within the sample. Investigators routinely employ a manual approach to probe design. A genome browser is used to extract DNA sequence from the locus of interest, which is searched against the target genome using a BLAST-like tool. Ideally a single perfect match is obtained to the target, with little cross-reactivity caused by homologous DNA sequence present in the genome and/or repetitive and low-complexity elements in the candidate probe. This is a labor intensive process often requiring several attempts to find a suitable probe for laboratory testing. Results We have written an informatic pipeline to automatically design genomic Sothern blot probes that specifically attempts to optimize the resultant probe, employing a brute-force strategy of generating many candidate probes of acceptable length in the user-specified design window, searching all against the target genome, then scoring and ranking the candidates by uniqueness and repetitive DNA element content. Using these in silico measures we can automatically design probes that we predict to perform as well, or better, than our previous manual designs, while considerably reducing design time. We went on to experimentally validate a number of these automated designs by Southern blotting. The majority of probes we tested performed well confirming our in silico prediction methodology and the general usefulness of the software for automated genomic Southern probe design. Conclusions Software and supplementary information are freely available at: http://www.genes2cognition.org/software/southern_blot PMID:20113467

Federated Access to Cyber Observables for Detection of Targeted Attacks

DTIC Science & Technology

2014-10-01

each manages. The DQNs also utilize an intelligent information ex- traction capability for automatically suggesting mappings from text found in audit ...Harmelen, and others, “OWL web ontology language overview,” W3C Recomm., vol. 10, no. 2004–03, p. 10, 2004. [4] D. Miller and B. Pearson , Security...Online]. Available: http://www.disa.mil/Services/Information- Assurance /HBS/HBSS. [21] S. Zanikolas and R. Sakellariou, “A taxonomy of grid

Automatic target alignment of the Helios laser system

NASA Astrophysics Data System (ADS)

Liberman, I.; Viswanathan, V. K.; Klein, M.; Seery, B. D.

1980-05-01

An automatic target-alignment technique for the Helios laser facility is reported and verified experimentally. The desired alignment condition is completely described by an autocollimation test. A computer program examines the autocollimated return pattern from the surrogate target and correctly describes any changes required in mirror orientation to yield optimum target alignment with either aberrated or misaligned beams. Automated on-line target alignment is thus shown to be feasible.

VoIP attacks detection engine based on neural network

NASA Astrophysics Data System (ADS)

Safarik, Jakub; Slachta, Jiri

2015-05-01

The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

Automatic Lumbar Spondylolisthesis Measurement in CT Images.

PubMed

Liao, Shu; Zhan, Yiqiang; Dong, Zhongxing; Yan, Ruyi; Gong, Liyan; Zhou, Xiang Sean; Salganicoff, Marcos; Fei, Jun

2016-07-01

Lumbar spondylolisthesis is one of the most common spinal diseases. It is caused by the anterior shift of a lumbar vertebrae relative to subjacent vertebrae. In current clinical practices, staging of spondylolisthesis is often conducted in a qualitative way. Although meyerding grading opens the door to stage spondylolisthesis in a more quantitative way, it relies on the manual measurement, which is time consuming and irreproducible. Thus, an automatic measurement algorithm becomes desirable for spondylolisthesis diagnosis and staging. However, there are two challenges. 1) Accurate detection of the most anterior and posterior points on the superior and inferior surfaces of each lumbar vertebrae. Due to the small size of the vertebrae, slight errors of detection may lead to significant measurement errors, hence, wrong disease stages. 2) Automatic localize and label each lumbar vertebrae is required to provide the semantic meaning of the measurement. It is difficult since different lumbar vertebraes have high similarity of both shape and image appearance. To resolve these challenges, a new auto measurement framework is proposed with two major contributions: First, a learning based spine labeling method that integrates both the image appearance and spine geometry information is designed to detect lumbar vertebrae. Second, a hierarchical method using both the population information from atlases and domain-specific information in the target image is proposed for most anterior and posterior points positioning. Validated on 258 CT spondylolisthesis patients, our method shows very similar results to manual measurements by radiologists and significantly increases the measurement efficiency.

Advanced MicroObserver UGS integration with and cueing of the BattleHawk squad level loitering munition and UAV

NASA Astrophysics Data System (ADS)

Steadman, Bob; Finklea, John; Kershaw, James; Loughman, Cathy; Shaffner, Patti; Frost, Dean; Deller, Sean

2014-06-01

Textron's Advanced MicroObserver(R) is a next generation remote unattended ground sensor system (UGS) for border security, infrastructure protection, and small combat unit security. The original MicroObserver(R) is a sophisticated seismic sensor system with multi-node fusion that supports target tracking. This system has been deployed in combat theaters. The system's seismic sensor nodes are uniquely able to be completely buried (including antennas) for optimal covertness. The advanced version adds a wireless day/night Electro-Optic Infrared (EOIR) system, cued by seismic tracking, with sophisticated target discrimination and automatic frame capture features. Also new is a field deployable Gateway configurable with a variety of radio systems and flexible networking, an important upgrade that enabled the research described herein. BattleHawkTM is a small tube launched Unmanned Air Vehicle (UAV) with a warhead. Using transmitted video from its EOIR subsystem an operator can search for and acquire a target day or night, select a target for attack, and execute terminal dive to destroy the target. It is designed as a lightweight squad level asset carried by an individual infantryman. Although BattleHawk has the best loiter time in its class, it's still relatively short compared to large UAVs. Also it's a one-shot asset in its munition configuration. Therefore Textron Defense Systems conducted research, funded internally, to determine if there was military utility in having the highly persistent MicroObserver(R) system cue BattleHawk's launch and vector it to beyond visual range targets for engagement. This paper describes that research; the system configuration implemented, and the results of field testing that was performed on a government range early in 2013. On the integrated system that was implemented, MicroObserver(R) seismic detections activated that system's camera which then automatically captured images of the target. The geo-referenced and time-tagged MicroObserver(R) target reports and images were then automatically forwarded to the BattleHawk Android-based controller. This allowed the operator to see the intruder (classified and geo-located) on the map based display, assess the intruder as likely hostile (via the image), and launch BattleHawk with the pre-loaded target coordinates. The operator was thus able to quickly acquire the intended target (without a search) and initiate target engagement immediately. System latencies were a major concern encountered during the research.

Pilot study on real-time motion detection in UAS video data by human observer and image exploitation algorithm

NASA Astrophysics Data System (ADS)

Hild, Jutta; Krüger, Wolfgang; Brüstle, Stefan; Trantelle, Patrick; Unmüßig, Gabriel; Voit, Michael; Heinze, Norbert; Peinsipp-Byma, Elisabeth; Beyerer, Jürgen

2017-05-01

Real-time motion video analysis is a challenging and exhausting task for the human observer, particularly in safety and security critical domains. Hence, customized video analysis systems providing functions for the analysis of subtasks like motion detection or target tracking are welcome. While such automated algorithms relieve the human operators from performing basic subtasks, they impose additional interaction duties on them. Prior work shows that, e.g., for interaction with target tracking algorithms, a gaze-enhanced user interface is beneficial. In this contribution, we present an investigation on interaction with an independent motion detection (IDM) algorithm. Besides identifying an appropriate interaction technique for the user interface - again, we compare gaze-based and traditional mouse-based interaction - we focus on the benefit an IDM algorithm might provide for an UAS video analyst. In a pilot study, we exposed ten subjects to the task of moving target detection in UAS video data twice, once performing with automatic support, once performing without it. We compare the two conditions considering performance in terms of effectiveness (correct target selections). Additionally, we report perceived workload (measured using the NASA-TLX questionnaire) and user satisfaction (measured using the ISO 9241-411 questionnaire). The results show that a combination of gaze input and automated IDM algorithm provides valuable support for the human observer, increasing the number of correct target selections up to 62% and reducing workload at the same time.

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.

Semi-automatic mapping of cultural heritage from airborne laser scanning using deep learning

NASA Astrophysics Data System (ADS)

Due Trier, Øivind; Salberg, Arnt-Børre; Holger Pilø, Lars; Tonning, Christer; Marius Johansen, Hans; Aarsten, Dagrun

2016-04-01

This paper proposes to use deep learning to improve semi-automatic mapping of cultural heritage from airborne laser scanning (ALS) data. Automatic detection methods, based on traditional pattern recognition, have been applied in a number of cultural heritage mapping projects in Norway for the past five years. Automatic detection of pits and heaps have been combined with visual interpretation of the ALS data for the mapping of deer hunting systems, iron production sites, grave mounds and charcoal kilns. However, the performance of the automatic detection methods varies substantially between ALS datasets. For the mapping of deer hunting systems on flat gravel and sand sediment deposits, the automatic detection results were almost perfect. However, some false detections appeared in the terrain outside of the sediment deposits. These could be explained by other pit-like landscape features, like parts of river courses, spaces between boulders, and modern terrain modifications. However, these were easy to spot during visual interpretation, and the number of missed individual pitfall traps was still low. For the mapping of grave mounds, the automatic method produced a large number of false detections, reducing the usefulness of the semi-automatic approach. The mound structure is a very common natural terrain feature, and the grave mounds are less distinct in shape than the pitfall traps. Still, applying automatic mound detection on an entire municipality did lead to a new discovery of an Iron Age grave field with more than 15 individual mounds. Automatic mound detection also proved to be useful for a detailed re-mapping of Norway's largest Iron Age grave yard, which contains almost 1000 individual graves. Combined pit and mound detection has been applied to the mapping of more than 1000 charcoal kilns that were used by an iron work 350-200 years ago. The majority of charcoal kilns were indirectly detected as either pits on the circumference, a central mound, or both. However, kilns with a flat interior and a shallow ditch along the circumference were often missed by the automatic detection method. The successfulness of automatic detection seems to depend on two factors: (1) the density of ALS ground hits on the cultural heritage structures being sought, and (2) to what extent these structures stand out from natural terrain structures. The first factor may, to some extent, be improved by using a higher number of ALS pulses per square meter. The second factor is difficult to change, and also highlights another challenge: how to make a general automatic method that is applicable in all types of terrain within a country. The mixed experience with traditional pattern recognition for semi-automatic mapping of cultural heritage led us to consider deep learning as an alternative approach. The main principle is that a general feature detector has been trained on a large image database. The feature detector is then tailored to a specific task by using a modest number of images of true and false examples of the features being sought. Results of using deep learning are compared with previous results using traditional pattern recognition.

A novel visual saliency detection method for infrared video sequences

NASA Astrophysics Data System (ADS)

Wang, Xin; Zhang, Yuzhen; Ning, Chen

2017-12-01

Infrared video applications such as target detection and recognition, moving target tracking, and so forth can benefit a lot from visual saliency detection, which is essentially a method to automatically localize the ;important; content in videos. In this paper, a novel visual saliency detection method for infrared video sequences is proposed. Specifically, for infrared video saliency detection, both the spatial saliency and temporal saliency are considered. For spatial saliency, we adopt a mutual consistency-guided spatial cues combination-based method to capture the regions with obvious luminance contrast and contour features. For temporal saliency, a multi-frame symmetric difference approach is proposed to discriminate salient moving regions of interest from background motions. Then, the spatial saliency and temporal saliency are combined to compute the spatiotemporal saliency using an adaptive fusion strategy. Besides, to highlight the spatiotemporal salient regions uniformly, a multi-scale fusion approach is embedded into the spatiotemporal saliency model. Finally, a Gestalt theory-inspired optimization algorithm is designed to further improve the reliability of the final saliency map. Experimental results demonstrate that our method outperforms many state-of-the-art saliency detection approaches for infrared videos under various backgrounds.

Patients with schizophrenia do not preserve automatic grouping when mentally re-grouping figures: shedding light on an ignored difficulty.

PubMed

Giersch, Anne; van Assche, Mitsouko; Capa, Rémi L; Marrer, Corinne; Gounot, Daniel

2012-01-01

Looking at a pair of objects is easy when automatic grouping mechanisms bind these objects together, but visual exploration can also be more flexible. It is possible to mentally "re-group" two objects that are not only separate but belong to different pairs of objects. "Re-grouping" is in conflict with automatic grouping, since it entails a separation of each item from the set it belongs to. This ability appears to be impaired in patients with schizophrenia. Here we check if this impairment is selective, which would suggest a dissociation between grouping and "re-grouping," or if it impacts on usual, automatic grouping, which would call for a better understanding of the interactions between automatic grouping and "re-grouping." Sixteen outpatients with schizophrenia and healthy controls had to identify two identical and contiguous target figures within a display of circles and squares alternating around a fixation point. Eye-tracking was used to check central fixation. The target pair could be located in the same or separate hemifields. Identical figures were grouped by a connector (grouped automatically) or not (to be re-grouped). Attention modulation of automatic grouping was tested by manipulating the proportion of connected and unconnected targets, thus prompting subjects to focalize on either connected or unconnected pairs. Both groups were sensitive to automatic grouping in most conditions, but patients were unusually slowed down for connected targets while focalizing on unconnected pairs. In addition, this unusual effect occurred only when targets were presented within the same hemifield. Patients and controls differed on this asymmetry between within- and across-hemifield presentation, suggesting that patients with schizophrenia do not re-group figures in the same way as controls do. We discuss possible implications on how "re-grouping" ties in with ongoing, automatic perception in healthy volunteers.

Patients with Schizophrenia Do Not Preserve Automatic Grouping When Mentally Re-Grouping Figures: Shedding Light on an Ignored Difficulty

PubMed Central

Giersch, Anne; van Assche, Mitsouko; Capa, Rémi L.; Marrer, Corinne; Gounot, Daniel

2012-01-01

Looking at a pair of objects is easy when automatic grouping mechanisms bind these objects together, but visual exploration can also be more flexible. It is possible to mentally “re-group” two objects that are not only separate but belong to different pairs of objects. “Re-grouping” is in conflict with automatic grouping, since it entails a separation of each item from the set it belongs to. This ability appears to be impaired in patients with schizophrenia. Here we check if this impairment is selective, which would suggest a dissociation between grouping and “re-grouping,” or if it impacts on usual, automatic grouping, which would call for a better understanding of the interactions between automatic grouping and “re-grouping.” Sixteen outpatients with schizophrenia and healthy controls had to identify two identical and contiguous target figures within a display of circles and squares alternating around a fixation point. Eye-tracking was used to check central fixation. The target pair could be located in the same or separate hemifields. Identical figures were grouped by a connector (grouped automatically) or not (to be re-grouped). Attention modulation of automatic grouping was tested by manipulating the proportion of connected and unconnected targets, thus prompting subjects to focalize on either connected or unconnected pairs. Both groups were sensitive to automatic grouping in most conditions, but patients were unusually slowed down for connected targets while focalizing on unconnected pairs. In addition, this unusual effect occurred only when targets were presented within the same hemifield. Patients and controls differed on this asymmetry between within- and across-hemifield presentation, suggesting that patients with schizophrenia do not re-group figures in the same way as controls do. We discuss possible implications on how “re-grouping” ties in with ongoing, automatic perception in healthy volunteers. PMID:22912621

Evaluation of Earthquake Detection Performance in Terms of Quality and Speed in SEISCOMP3 Using New Modules Qceval, Npeval and Sceval

NASA Astrophysics Data System (ADS)

Roessler, D.; Weber, B.; Ellguth, E.; Spazier, J.

2017-12-01

The geometry of seismic monitoring networks, site conditions and data availability as well as monitoring targets and strategies typically impose trade-offs between data quality, earthquake detection sensitivity, false detections and alert times. Network detection capabilities typically change with alteration of the seismic noise level by human activity or by varying weather and sea conditions. To give helpful information to operators and maintenance coordinators, gempa developed a range of tools to evaluate earthquake detection and network performance including qceval, npeval and sceval. qceval is a module which analyzes waveform quality parameters in real-time and deactivates and reactivates data streams based on waveform quality thresholds for automatic processing. For example, thresholds can be defined for latency, delay, timing quality, spikes and gaps count and rms. As changes in the automatic processing have a direct influence on detection quality and speed, another tool called "npeval" was designed to calculate in real-time the expected time needed to detect and locate earthquakes by evaluating the effective network geometry. The effective network geometry is derived from the configuration of stations participating in the detection. The detection times are shown as an additional layer on the map and updated in real-time as soon as the effective network geometry changes. Yet another new tool, "sceval", is an automatic module which classifies located seismic events (Origins) in real-time. sceval evaluates the spatial distribution of the stations contributing to an Origin. It confirms or rejects the status of Origins, adds comments or leaves the Origin unclassified. The comments are passed to an additional sceval plug-in where the end user can customize event types. This unique identification of real and fake events in earthquake catalogues allows to lower network detection thresholds. In real-time monitoring situations operators can limit the processing to events with unclassified Origins, reducing their workload. Classified Origins can be treated specifically by other procedures. These modules have been calibrated and fully tested by several complex seismic monitoring networks in the region of Indonesia and Northern Chile.

Control of treatment size in cavitation-enhanced high-intensity focused ultrasound using radio-frequency echo signals

NASA Astrophysics Data System (ADS)

Tomiyasu, Kentaro; Takagi, Ryo; Iwasaki, Ryosuke; Yoshizawa, Shin; Umemura, Shin-ichiro

2017-07-01

In high-intensity focused ultrasound (HIFU) treatment, controlling the ultrasound dose at each focal target spot is important because it is a problem that the length of the coagulated region in front of the focal point deviates owing to the differences in absorption in each focal target spot and attenuation in the intervening tissues. In this study, the detected changes in the power spectra of HIFU echoes were used by controlling the HIFU duration in the “trigger HIFU” sequence with the aim to increase coagulation size through the enhancement of the ultrasonic heating by the cavitation induced by the preceding extremely high intensity short “trigger” pulse. The result shows that this method can be used to detect boiling bubbles and the following generated cavitation bubbles at their early stage. By automatically stopping HIFU exposure immediately after detecting the bubbles, overheating was prevented and the deviation of the length of the coagulated region was reduced.

Millimeter wave radar system on a rotating platform for combined search and track functionality with SAR imaging

NASA Astrophysics Data System (ADS)

Aulenbacher, Uwe; Rech, Klaus; Sedlmeier, Johannes; Pratisto, Hans; Wellig, Peter

2014-10-01

Ground based millimeter wave radar sensors offer the potential for a weather-independent automatic ground surveillance at day and night, e.g. for camp protection applications. The basic principle and the experimental verification of a radar system concept is described, which by means of an extreme off-axis positioning of the antenna(s) combines azimuthal mechanical beam steering with the formation of a circular-arc shaped synthetic aperture (SA). In automatic ground surveillance the function of search and detection of moving ground targets is performed by means of the conventional mechanical scan mode. The rotated antenna structure designed as a small array with two or more RX antenna elements with simultaneous receiver chains allows to instantaneous track multiple moving targets (monopulse principle). The simultaneously operated SAR mode yields areal images of the distribution of stationary scatterers. For ground surveillance application this SAR mode is best suited for identifying possible threats by means of change detection. The feasibility of this concept was tested by means of an experimental radar system comprising of a 94 GHz (W band) FM-CW module with 1 GHz bandwidth and two RX antennas with parallel receiver channels, placed off-axis at a rotating platform. SAR mode and search/track mode were tested during an outdoor measurement campaign. The scenery of two persons walking along a road and partially through forest served as test for the capability to track multiple moving targets. For SAR mode verification an image of the area composed of roads, grassland, woodland and several man-made objects was reconstructed from the measured data.

Automatic Coregistration for Multiview SAR Images in Urban Areas

NASA Astrophysics Data System (ADS)

Xiang, Y.; Kang, W.; Wang, F.; You, H.

2017-09-01

Due to the high resolution property and the side-looking mechanism of SAR sensors, complex buildings structures make the registration of SAR images in urban areas becomes very hard. In order to solve the problem, an automatic and robust coregistration approach for multiview high resolution SAR images is proposed in the paper, which consists of three main modules. First, both the reference image and the sensed image are segmented into two parts, urban areas and nonurban areas. Urban areas caused by double or multiple scattering in a SAR image have a tendency to show higher local mean and local variance values compared with general homogeneous regions due to the complex structural information. Based on this criterion, building areas are extracted. After obtaining the target regions, L-shape structures are detected using the SAR phase congruency model and Hough transform. The double bounce scatterings formed by wall and ground are shown as strong L- or T-shapes, which are usually taken as the most reliable indicator for building detection. According to the assumption that buildings are rectangular and flat models, planimetric buildings are delineated using the L-shapes, then the reconstructed target areas are obtained. For the orignal areas and the reconstructed target areas, the SAR-SIFT matching algorithm is implemented. Finally, correct corresponding points are extracted by the fast sample consensus (FSC) and the transformation model is also derived. The experimental results on a pair of multiview TerraSAR images with 1-m resolution show that the proposed approach gives a robust and precise registration performance, compared with the orignal SAR-SIFT method.

Physics-Based Image Segmentation Using First Order Statistical Properties and Genetic Algorithm for Inductive Thermography Imaging.

PubMed

Gao, Bin; Li, Xiaoqing; Woo, Wai Lok; Tian, Gui Yun

2018-05-01

Thermographic inspection has been widely applied to non-destructive testing and evaluation with the capabilities of rapid, contactless, and large surface area detection. Image segmentation is considered essential for identifying and sizing defects. To attain a high-level performance, specific physics-based models that describe defects generation and enable the precise extraction of target region are of crucial importance. In this paper, an effective genetic first-order statistical image segmentation algorithm is proposed for quantitative crack detection. The proposed method automatically extracts valuable spatial-temporal patterns from unsupervised feature extraction algorithm and avoids a range of issues associated with human intervention in laborious manual selection of specific thermal video frames for processing. An internal genetic functionality is built into the proposed algorithm to automatically control the segmentation threshold to render enhanced accuracy in sizing the cracks. Eddy current pulsed thermography will be implemented as a platform to demonstrate surface crack detection. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. In addition, a global quantitative assessment index F-score has been adopted to objectively evaluate the performance of different segmentation algorithms.

Automatic multimodal detection for long-term seizure documentation in epilepsy.

PubMed

Fürbass, F; Kampusch, S; Kaniusas, E; Koren, J; Pirker, S; Hopfengärtner, R; Stefan, H; Kluge, T; Baumgartner, C

2017-08-01

This study investigated sensitivity and false detection rate of a multimodal automatic seizure detection algorithm and the applicability to reduced electrode montages for long-term seizure documentation in epilepsy patients. An automatic seizure detection algorithm based on EEG, EMG, and ECG signals was developed. EEG/ECG recordings of 92 patients from two epilepsy monitoring units including 494 seizures were used to assess detection performance. EMG data were extracted by bandpass filtering of EEG signals. Sensitivity and false detection rate were evaluated for each signal modality and for reduced electrode montages. All focal seizures evolving to bilateral tonic-clonic (BTCS, n=50) and 89% of focal seizures (FS, n=139) were detected. Average sensitivity in temporal lobe epilepsy (TLE) patients was 94% and 74% in extratemporal lobe epilepsy (XTLE) patients. Overall detection sensitivity was 86%. Average false detection rate was 12.8 false detections in 24h (FD/24h) for TLE and 22 FD/24h in XTLE patients. Utilization of 8 frontal and temporal electrodes reduced average sensitivity from 86% to 81%. Our automatic multimodal seizure detection algorithm shows high sensitivity with full and reduced electrode montages. Evaluation of different signal modalities and electrode montages paces the way for semi-automatic seizure documentation systems. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Visualization of hyperspectral imagery

NASA Astrophysics Data System (ADS)

Hogervorst, Maarten A.; Bijl, Piet; Toet, Alexander

2007-04-01

We developed four new techniques to visualize hyper spectral image data for man-in-the-loop target detection. The methods respectively: (1) display the subsequent bands as a movie ("movie"), (2) map the data onto three channels and display these as a colour image ("colour"), (3) display the correlation between the pixel signatures and a known target signature ("match") and (4) display the output of a standard anomaly detector ("anomaly"). The movie technique requires no assumptions about the target signature and involves no information loss. The colour technique produces a single image that can be displayed in real-time. A disadvantage of this technique is loss of information. A display of the match between a target signature and pixels and can be interpreted easily and fast, but this technique relies on precise knowledge of the target signature. The anomaly detector signifies pixels with signatures that deviate from the (local) background. We performed a target detection experiment with human observers to determine their relative performance with the four techniques,. The results show that the "match" presentation yields the best performance, followed by "movie" and "anomaly", while performance with the "colour" presentation was the poorest. Each scheme has its advantages and disadvantages and is more or less suited for real-time and post-hoc processing. The rationale is that the final interpretation is best done by a human observer. In contrast to automatic target recognition systems, the interpretation of hyper spectral imagery by the human visual system is robust to noise and image transformations and requires a minimal number of assumptions (about signature of target and background, target shape etc.) When more knowledge about target and background is available this may be used to help the observer interpreting the data (aided target detection).

Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online

PubMed Central

Li, Zheng-Zhou; Chen, Jing; Hou, Qian; Fu, Hong-Xia; Dai, Zhen; Jin, Gang; Li, Ru-Zhang; Liu, Chang-Ju

2014-01-01

It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively. PMID:24871988

Sparse representation for infrared Dim target detection via a discriminative over-complete dictionary learned online.

PubMed

Li, Zheng-Zhou; Chen, Jing; Hou, Qian; Fu, Hong-Xia; Dai, Zhen; Jin, Gang; Li, Ru-Zhang; Liu, Chang-Ju

2014-05-27

It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.

A Patch-Based Method for Repetitive and Transient Event Detection in Fluorescence Imaging

PubMed Central

Boulanger, Jérôme; Gidon, Alexandre; Kervran, Charles; Salamero, Jean

2010-01-01

Automatic detection and characterization of molecular behavior in large data sets obtained by fast imaging in advanced light microscopy become key issues to decipher the dynamic architectures and their coordination in the living cell. Automatic quantification of the number of sudden and transient events observed in fluorescence microscopy is discussed in this paper. We propose a calibrated method based on the comparison of image patches expected to distinguish sudden appearing/vanishing fluorescent spots from other motion behaviors such as lateral movements. We analyze the performances of two statistical control procedures and compare the proposed approach to a frame difference approach using the same controls on a benchmark of synthetic image sequences. We have then selected a molecular model related to membrane trafficking and considered real image sequences obtained in cells stably expressing an endocytic-recycling trans-membrane protein, the Langerin-YFP, for validation. With this model, we targeted the efficient detection of fast and transient local fluorescence concentration arising in image sequences from a data base provided by two different microscopy modalities, wide field (WF) video microscopy using maximum intensity projection along the axial direction and total internal reflection fluorescence microscopy. Finally, the proposed detection method is briefly used to statistically explore the effect of several perturbations on the rate of transient events detected on the pilot biological model. PMID:20976222

Automatic Residential/Commercial Classification of Parcels with Solar Panel Detections

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

Morton, April M; Omitaomu, Olufemi A; Kotikot, Susan

A computational method to automatically detect solar panels on rooftops to aid policy and financial assessment of solar distributed generation. The code automatically classifies parcels containing solar panels in the U.S. as residential or commercial. The code allows the user to specify an input dataset containing parcels and detected solar panels, and then uses information about the parcels and solar panels to automatically classify the rooftops as residential or commercial using machine learning techniques. The zip file containing the code includes sample input and output datasets for the Boston and DC areas.

Scene Analysis: Non-Linear Spatial Filtering for Automatic Target Detection.

DTIC Science & Technology

1982-12-01

In this thesis, a method for two-dimensional pattern recognition was developed and tested. The method included a global search scheme for candidate...test global switch TYPEO Creating negative video file only.W 11=0 12=256 13=512 14=768 GO 70 2 1 TYPE" Creating negative and horizontally flipped video...purpose was to develop a base of image processing software for the AFIT Digital Signal Processing Laboratory NOVA- ECLIPSE minicomputer system, for

Does flexibility in perceptual organization compete with automatic grouping?

PubMed

van Assche, Mitsouko; Gos, Pierre; Giersch, Anne

2012-02-06

Segregated objects can be sought simultaneously, i.e., mentally "re-grouped." Although the mechanisms underlying such "re-grouping" clearly differ from automatic grouping, it is unclear whether or not the end products of "re-grouping" and automatic grouping are the same. If they are, they would have similar impact on visual organization but would be in conflict. We compared the consequences of grouping and re-grouping on the performance cost induced by stimuli presented across hemifields. Two identical and contiguous target figures had to be identified within a display of circles and squares alternating around a fixation point. Eye tracking was used to check central fixation. The target pair could be located in the same or separate hemifields. A large cost of presenting targets across hemifields was observed. Grouping by connectedness yielded two types of target pair, connected and unconnected. Subjects prioritized unconnected pairs efficiently when prompted to do so, suggesting "re-grouping." However, unlike automatic grouping, this did not affect the cost of across-hemifield presentation. The suggestion is that re-grouping yields different outputs to automatic grouping, such that a fresh representation resulting from re-grouping complements the one resulting from automatic grouping but does not replace it. This is one step toward understanding how our mental exploration of the world ties in and coexists with ongoing perception.

CT-guided automated detection of lung tumors on PET images

NASA Astrophysics Data System (ADS)

Cui, Yunfeng; Zhao, Binsheng; Akhurst, Timothy J.; Yan, Jiayong; Schwartz, Lawrence H.

2008-03-01

The calculation of standardized uptake values (SUVs) in tumors on serial [ 18F]2-fluoro-2-deoxy-D-glucose ( 18F-FDG) positron emission tomography (PET) images is often used for the assessment of therapy response. We present a computerized method that automatically detects lung tumors on 18F-FDG PET/Computed Tomography (CT) images using both anatomic and metabolic information. First, on CT images, relevant organs, including lung, bone, liver and spleen, are automatically identified and segmented based on their locations and intensity distributions. Hot spots (SUV >= 1.5) on 18F-FDG PET images are then labeled using the connected component analysis. The resultant "hot objects" (geometrically connected hot spots in three dimensions) that fall into, reside at the edges or are in the vicinity of the lungs are considered as tumor candidates. To determine true lesions, further analyses are conducted, including reduction of tumor candidates by the masking out of hot objects within CT-determined normal organs, and analysis of candidate tumors' locations, intensity distributions and shapes on both CT and PET. The method was applied to 18F-FDG-PET/CT scans from 9 patients, on which 31 target lesions had been identified by a nuclear medicine radiologist during a Phase II lung cancer clinical trial. Out of 31 target lesions, 30 (97%) were detected by the computer method. However, sensitivity and specificity were not estimated because not all lesions had been marked up in the clinical trial. The method effectively excluded the hot spots caused by mediastinum, liver, spleen, skeletal muscle and bone metastasis.

Automatic priming of attentional control by relevant colors.

PubMed

Ansorge, Ulrich; Becker, Stefanie I

2012-01-01

We tested whether color word cues automatically primed attentional control settings during visual search, or whether color words were used in a strategic manner for the control of attention. In Experiment 1, we used color words as cues that were informative or uninformative with respect to the target color. Regardless of the cue's informativeness, distractors similar to the color cue captured more attention. In Experiment 2, the participants either indicated their expectation about the target color or recalled the last target color, which was uncorrelated with the present target color. We observed more attentional capture by distractors that were similar to the participants' predictions and recollections, but no difference between effects of the recollected and predicted colors. In Experiment 3, we used 100%-informative word cues that were congruent with the predicted target color (e.g., the word "red" informed that the target would be red) or incongruent with the predicted target color (e.g., the word "green" informed that the target would be red) and found that informative incongruent word cues primed attention capture by a word-similar distractor. Together, the results suggest that word cues (Exps. 1 and 3) and color representations (Exp. 2) primed attention capture in an automatic manner. This indicates that color cues automatically primed temporary adjustments in attention control settings.

An Interative Grahical User Interface for Maritime Security Services

NASA Astrophysics Data System (ADS)

Reize, T.; Müller, R.; Kiefl, R.

2013-10-01

In order to analyse optical satellite images for maritime security issues in Near-Real-Time (NRT) an interactive graphical user interface (GUI) based on NASA World Wind was developed and is presented in this article. Targets or activities can be detected, measured and classified with this tool simply and quickly. The service uses optical satellite images, currently taken from 6 sensors: Worldview-1 and Worldview-2, Ikonos, Quickbird, GeoEye-1 and EROS-B. The GUI can also handle SAR-images, air-borne images or UAV images. Software configurations are provided in a job-order file and thus all preparation tasks, such as image installation are performed fully automatically. The imagery can be overlaid with vessels derived by an automatic detection processor. These potential vessel layers can be zoomed in by a single click and sorted with an adapted method. Further object properties, such as vessel type or confidence level of identification, can be added by the operator manually. The heading angle can be refined by dragging the vessel's head or switching it to 180° with a single click. Further vessels or other relevant objects can be added. The objects length, width, heading and position are calculated automatically from three clicks on top, bottom and an arbitrary point at one of the object's longer side. In case of an Activity Detection, the detected objects can be grouped in area of interests (AOI) and classified, according to the ordered activities. All relevant information is finally written to an exchange file, after quality control and necessary correction procedures are performed. If required, image thumbnails can be cut around objects or around whole areas of interest and saved as separated, geo-referenced images.

Full automatic fiducial marker detection on coil arrays for accurate instrumentation placement during MRI guided breast interventions

NASA Astrophysics Data System (ADS)

Filippatos, Konstantinos; Boehler, Tobias; Geisler, Benjamin; Zachmann, Harald; Twellmann, Thorsten

2010-02-01

With its high sensitivity, dynamic contrast-enhanced MR imaging (DCE-MRI) of the breast is today one of the first-line tools for early detection and diagnosis of breast cancer, particularly in the dense breast of young women. However, many relevant findings are very small or occult on targeted ultrasound images or mammography, so that MRI guided biopsy is the only option for a precise histological work-up [1]. State-of-the-art software tools for computer-aided diagnosis of breast cancer in DCE-MRI data offer also means for image-based planning of biopsy interventions. One step in the MRI guided biopsy workflow is the alignment of the patient position with the preoperative MR images. In these images, the location and orientation of the coil localization unit can be inferred from a number of fiducial markers, which for this purpose have to be manually or semi-automatically detected by the user. In this study, we propose a method for precise, full-automatic localization of fiducial markers, on which basis a virtual localization unit can be subsequently placed in the image volume for the purpose of determining the parameters for needle navigation. The method is based on adaptive thresholding for separating breast tissue from background followed by rigid registration of marker templates. In an evaluation of 25 clinical cases comprising 4 different commercial coil array models and 3 different MR imaging protocols, the method yielded a sensitivity of 0.96 at a false positive rate of 0.44 markers per case. The mean distance deviation between detected fiducial centers and ground truth information that was appointed from a radiologist was 0.94mm.

Toward real-time quantification of fluorescence molecular probes using target/background ratio for guiding biopsy and endoscopic therapy of esophageal neoplasia.

PubMed

Jiang, Yang; Gong, Yuanzheng; Rubenstein, Joel H; Wang, Thomas D; Seibel, Eric J

2017-04-01

Multimodal endoscopy using fluorescence molecular probes is a promising method of surveying the entire esophagus to detect cancer progression. Using the fluorescence ratio of a target compared to a surrounding background, a quantitative value is diagnostic for progression from Barrett's esophagus to high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC). However, current quantification of fluorescent images is done only after the endoscopic procedure. We developed a Chan-Vese-based algorithm to segment fluorescence targets, and subsequent morphological operations to generate background, thus calculating target/background (T/B) ratios, potentially to provide real-time guidance for biopsy and endoscopic therapy. With an initial processing speed of 2 fps and by calculating the T/B ratio for each frame, our method provides quasireal-time quantification of the molecular probe labeling to the endoscopist. Furthermore, an automatic computer-aided diagnosis algorithm can be applied to the recorded endoscopic video, and the overall T/B ratio is calculated for each patient. The receiver operating characteristic curve was employed to determine the threshold for classification of HGD/EAC using leave-one-out cross-validation. With 92% sensitivity and 75% specificity to classify HGD/EAC, our automatic algorithm shows promising results for a surveillance procedure to help manage esophageal cancer and other cancers inspected by endoscopy.

A distributed automatic target recognition system using multiple low resolution sensors

NASA Astrophysics Data System (ADS)

Yue, Zhanfeng; Lakshmi Narasimha, Pramod; Topiwala, Pankaj

2008-04-01

In this paper, we propose a multi-agent system which uses swarming techniques to perform high accuracy Automatic Target Recognition (ATR) in a distributed manner. The proposed system can co-operatively share the information from low-resolution images of different looks and use this information to perform high accuracy ATR. An advanced, multiple-agent Unmanned Aerial Vehicle (UAV) systems-based approach is proposed which integrates the processing capabilities, combines detection reporting with live video exchange, and swarm behavior modalities that dramatically surpass individual sensor system performance levels. We employ real-time block-based motion analysis and compensation scheme for efficient estimation and correction of camera jitter, global motion of the camera/scene and the effects of atmospheric turbulence. Our optimized Partition Weighted Sum (PWS) approach requires only bitshifts and additions, yet achieves a stunning 16X pixel resolution enhancement, which is moreover parallizable. We develop advanced, adaptive particle-filtering based algorithms to robustly track multiple mobile targets by adaptively changing the appearance model of the selected targets. The collaborative ATR system utilizes the homographies between the sensors induced by the ground plane to overlap the local observation with the received images from other UAVs. The motion of the UAVs distorts estimated homography frame to frame. A robust dynamic homography estimation algorithm is proposed to address this, by using the homography decomposition and the ground plane surface estimation.

Multi-Stage System for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Lu, Thomas T.; Ye, David; Edens, Weston; Johnson, Oliver

2010-01-01

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.

Review of automatic detection of pig behaviours by using image analysis

NASA Astrophysics Data System (ADS)

Han, Shuqing; Zhang, Jianhua; Zhu, Mengshuai; Wu, Jianzhai; Kong, Fantao

2017-06-01

Automatic detection of lying, moving, feeding, drinking, and aggressive behaviours of pigs by means of image analysis can save observation input by staff. It would help staff make early detection of diseases or injuries of pigs during breeding and improve management efficiency of swine industry. This study describes the progress of pig behaviour detection based on image analysis and advancement in image segmentation of pig body, segmentation of pig adhesion and extraction of pig behaviour characteristic parameters. Challenges for achieving automatic detection of pig behaviours were summarized.

Validation of automatic landmark identification for atlas-based segmentation for radiation treatment planning of the head-and-neck region

NASA Astrophysics Data System (ADS)

Leavens, Claudia; Vik, Torbjørn; Schulz, Heinrich; Allaire, Stéphane; Kim, John; Dawson, Laura; O'Sullivan, Brian; Breen, Stephen; Jaffray, David; Pekar, Vladimir

2008-03-01

Manual contouring of target volumes and organs at risk in radiation therapy is extremely time-consuming, in particular for treating the head-and-neck area, where a single patient treatment plan can take several hours to contour. As radiation treatment delivery moves towards adaptive treatment, the need for more efficient segmentation techniques will increase. We are developing a method for automatic model-based segmentation of the head and neck. This process can be broken down into three main steps: i) automatic landmark identification in the image dataset of interest, ii) automatic landmark-based initialization of deformable surface models to the patient image dataset, and iii) adaptation of the deformable models to the patient-specific anatomical boundaries of interest. In this paper, we focus on the validation of the first step of this method, quantifying the results of our automatic landmark identification method. We use an image atlas formed by applying thin-plate spline (TPS) interpolation to ten atlas datasets, using 27 manually identified landmarks in each atlas/training dataset. The principal variation modes returned by principal component analysis (PCA) of the landmark positions were used by an automatic registration algorithm, which sought the corresponding landmarks in the clinical dataset of interest using a controlled random search algorithm. Applying a run time of 60 seconds to the random search, a root mean square (rms) distance to the ground-truth landmark position of 9.5 +/- 0.6 mm was calculated for the identified landmarks. Automatic segmentation of the brain, mandible and brain stem, using the detected landmarks, is demonstrated.

Supporting the Development and Adoption of Automatic Lameness Detection Systems in Dairy Cattle: Effect of System Cost and Performance on Potential Market Shares

PubMed Central

Van Weyenberg, Stephanie; Van Nuffel, Annelies; Lauwers, Ludwig; Vangeyte, Jürgen

2017-01-01

Simple Summary Most prototypes of systems to automatically detect lameness in dairy cattle are still not available on the market. Estimating their potential adoption rate could support developers in defining development goals towards commercially viable and well-adopted systems. We simulated the potential market shares of such prototypes to assess the effect of altering the system cost and detection performance on the potential adoption rate. We found that system cost and lameness detection performance indeed substantially influence the potential adoption rate. In order for farmers to prefer automatic detection over current visual detection, the usefulness that farmers attach to a system with specific characteristics should be higher than that of visual detection. As such, we concluded that low system costs and high detection performances are required before automatic lameness detection systems become applicable in practice. Abstract Most automatic lameness detection system prototypes have not yet been commercialized, and are hence not yet adopted in practice. Therefore, the objective of this study was to simulate the effect of detection performance (percentage missed lame cows and percentage false alarms) and system cost on the potential market share of three automatic lameness detection systems relative to visual detection: a system attached to the cow, a walkover system, and a camera system. Simulations were done using a utility model derived from survey responses obtained from dairy farmers in Flanders, Belgium. Overall, systems attached to the cow had the largest market potential, but were still not competitive with visual detection. Increasing the detection performance or lowering the system cost led to higher market shares for automatic systems at the expense of visual detection. The willingness to pay for extra performance was €2.57 per % less missed lame cows, €1.65 per % less false alerts, and €12.7 for lame leg indication, respectively. The presented results could be exploited by system designers to determine the effect of adjustments to the technology on a system’s potential adoption rate. PMID:28991188

Target recognition based on convolutional neural network

NASA Astrophysics Data System (ADS)

Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

2017-11-01

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

Laser range profiling for small target recognition

NASA Astrophysics Data System (ADS)

Steinvall, Ove; Tulldahl, Michael

2016-05-01

The detection and classification of small surface and airborne targets at long ranges is a growing need for naval security. Long range ID or ID at closer range of small targets has its limitations in imaging due to the demand on very high transverse sensor resolution. It is therefore motivated to look for 1D laser techniques for target ID. These include vibrometry, and laser range profiling. Vibrometry can give good results but is also sensitive to certain vibrating parts on the target being in the field of view. Laser range profiling is attractive because the maximum range can be substantial, especially for a small laser beam width. A range profiler can also be used in a scanning mode to detect targets within a certain sector. The same laser can also be used for active imaging when the target comes closer and is angular resolved. The present paper will show both experimental and simulated results for laser range profiling of small boats out to 6-7 km range and a UAV mockup at close range (1.3 km). We obtained good results with the profiling system both for target detection and recognition. Comparison of experimental and simulated range waveforms based on CAD models of the target support the idea of having a profiling system as a first recognition sensor and thus narrowing the search space for the automatic target recognition based on imaging at close ranges. The naval experiments took place in the Baltic Sea with many other active and passive EO sensors beside the profiling system. Discussion of data fusion between laser profiling and imaging systems will be given. The UAV experiments were made from the rooftop laboratory at FOI.

Attentional Bias for Pain and Sex, and Automatic Appraisals of Sexual Penetration: Differential Patterns in Dyspareunia vs Vaginismus?

PubMed

Melles, Reinhilde J; Dewitte, Marieke D; Ter Kuile, Moniek M; Peters, Madelon M L; de Jong, Peter J

2016-08-01

Current information processing models propose that heightened attention bias for sex-related threats (eg, pain) and lowered automatic incentive processes ("wanting") may play an important role in the impairment of sexual arousal and the development of sexual dysfunctions such as genitopelvic pain/penetration disorder (GPPPD). Differential threat and incentive processing may also help explain the stronger persistence of coital avoidance in women with vaginismus compared to women with dyspareunia. As the first aim, we tested if women with GPPPD show (1) heightened attention for pain and sex, and (2) heightened threat and lower incentive associations with sexual penetration. Second, we examined whether the stronger persistence of coital avoidance in vaginismus vs dyspareunia might be explained by a stronger attentional bias or more dysfunctional automatic threat/incentive associations. Women with lifelong vaginismus (n = 37), dyspareunia (n = 29), and a no-symptoms comparison group (n = 51) completed a visual search task to assess attentional bias, and single target implicit-association tests to measure automatic sex-threat and sex-wanting associations. There were no group differences in attentional bias or automatic associations. Correlational analysis showed that slowed detection of sex stimuli and stronger automatic threat associations were related to lowered sexual arousal. The findings do not corroborate the view that attentional bias for pain or sex contributes to coital pain, or that differences in coital avoidance may be explained by differences in attentional bias or automatic threat/incentive associations. However, the correlational findings are consistent with the view that automatic threat associations and impaired attention for sex stimuli may interfere with the generation of sexual arousal. Copyright © 2016 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.

Fully automatic segmentation of the femur from 3D-CT images using primitive shape recognition and statistical shape models.

PubMed

Ben Younes, Lassad; Nakajima, Yoshikazu; Saito, Toki

2014-03-01

Femur segmentation is well established and widely used in computer-assisted orthopedic surgery. However, most of the robust segmentation methods such as statistical shape models (SSM) require human intervention to provide an initial position for the SSM. In this paper, we propose to overcome this problem and provide a fully automatic femur segmentation method for CT images based on primitive shape recognition and SSM. Femur segmentation in CT scans was performed using primitive shape recognition based on a robust algorithm such as the Hough transform and RANdom SAmple Consensus. The proposed method is divided into 3 steps: (1) detection of the femoral head as sphere and the femoral shaft as cylinder in the SSM and the CT images, (2) rigid registration between primitives of SSM and CT image to initialize the SSM into the CT image, and (3) fitting of the SSM to the CT image edge using an affine transformation followed by a nonlinear fitting. The automated method provided good results even with a high number of outliers. The difference of segmentation error between the proposed automatic initialization method and a manual initialization method is less than 1 mm. The proposed method detects primitive shape position to initialize the SSM into the target image. Based on primitive shapes, this method overcomes the problem of inter-patient variability. Moreover, the results demonstrate that our method of primitive shape recognition can be used for 3D SSM initialization to achieve fully automatic segmentation of the femur.

Air-to-air radar flight testing

NASA Astrophysics Data System (ADS)

Scott, Randall E.

1988-06-01

This volume in the AGARD Flight Test Techniques Series describes flight test techniques, flight test instrumentation, ground simulation, data reduction and analysis methods used to determine the performance characteristics of a modern air-to-air (a/a) radar system. Following a general coverage of specification requirements, test plans, support requirements, development and operational testing, and management information systems, the report goes into more detailed flight test techniques covering a/a radar capabilities of: detection, manual acquisition, automatic acquisition, tracking a single target, and detection and tracking of multiple targets. There follows a section on additional flight test considerations such as electromagnetic compatibility, electronic countermeasures, displays and controls, degraded and backup modes, radome effects, environmental considerations, and use of testbeds. Other sections cover ground simulation, flight test instrumentation, and data reduction and analysis. The final sections deal with reporting and a discussion of considerations for the future and how they may affect radar flight testing.

Design of tracking and detecting lens system by diffractive optical method

NASA Astrophysics Data System (ADS)

Yang, Jiang; Qi, Bo; Ren, Ge; Zhou, Jianwei

2016-10-01

Many target-tracking applications require an optical system to acquire the target for tracking and identification. This paper describes a new detecting optical system that can provide automatic flying object detecting, tracking and measuring in visible band. The main feature of the detecting lens system is the combination of diffractive optics with traditional lens design by a technique was invented by Schupmann. Diffractive lens has great potential for developing the larger aperture and lightweight lens. First, the optical system scheme was described. Then the Schupmann achromatic principle with diffractive lens and corrective optics is introduced. According to the technical features and requirements of the optical imaging system for detecting and tracking, we designed a lens system with flat surface Fresnel lens and cancels the optical system chromatic aberration by another flat surface Fresnel lens with effective focal length of 1980mm, an F-Number of F/9.9 and a field of view of 2ωω = 14.2', spatial resolution of 46 lp/mm and a working wavelength range of 0.6 0.85um. At last, the system is compact and easy to fabricate and assembly, the diffuse spot size and MTF function and other analysis provide good performance.

Clinical experience with a computer-aided diagnosis system for automatic detection of pulmonary nodules at spiral CT of the chest

NASA Astrophysics Data System (ADS)

Wormanns, Dag; Fiebich, Martin; Saidi, Mustafa; Diederich, Stefan; Heindel, Walter

2001-05-01

The purpose of the study was to evaluate a computer aided diagnosis (CAD) workstation with automatic detection of pulmonary nodules at low-dose spiral CT in a clinical setting for early detection of lung cancer. Two radiologists in consensus reported 88 consecutive spiral CT examinations. All examinations were reviewed using a UNIX-based CAD workstation with a self-developed algorithm for automatic detection of pulmonary nodules. The algorithm was designed to detect nodules with at least 5 mm diameter. The results of automatic nodule detection were compared to the consensus reporting of two radiologists as gold standard. Additional CAD findings were regarded as nodules initially missed by the radiologists or as false positive results. A total of 153 nodules were detected with all modalities (diameter: 85 nodules <5mm, 63 nodules 5-9 mm, 5 nodules >= 10 mm). Reasons for failure of automatic nodule detection were assessed. Sensitivity of radiologists for nodules >=5 mm was 85%, sensitivity of CAD was 38%. For nodules >=5 mm without pleural contact sensitivity was 84% for radiologists at 45% for CAD. CAD detected 15 (10%) nodules not mentioned in the radiologist's report but representing real nodules, among them 10 (15%) nodules with a diameter $GREW5 mm. Reasons for nodules missed by CAD include: exclusion because of morphological features during region analysis (33%), nodule density below the detection threshold (26%), pleural contact (33%), segmentation errors (5%) and other reasons (2%). CAD improves detection of pulmonary nodules at spiral CT significantly and is a valuable second opinion in a clinical setting for lung cancer screening. Optimization of region analysis and an appropriate density threshold have a potential for further improvement of automatic nodule detection.

Detecting buried explosive hazards with handheld GPR and deep learning

NASA Astrophysics Data System (ADS)

Besaw, Lance E.

2016-05-01

Buried explosive hazards (BEHs), including traditional landmines and homemade improvised explosives, have proven difficult to detect and defeat during and after conflicts around the world. Despite their various sizes, shapes and construction material, ground penetrating radar (GPR) is an excellent phenomenology for detecting BEHs due to its ability to sense localized differences in electromagnetic properties. Handheld GPR detectors are common equipment for detecting BEHs because of their flexibility (in part due to the human operator) and effectiveness in cluttered environments. With modern digital electronics and positioning systems, handheld GPR sensors can sense and map variation in electromagnetic properties while searching for BEHs. Additionally, large-scale computers have demonstrated an insatiable appetite for ingesting massive datasets and extracting meaningful relationships. This is no more evident than the maturation of deep learning artificial neural networks (ANNs) for image and speech recognition now commonplace in industry and academia. This confluence of sensing, computing and pattern recognition technologies offers great potential to develop automatic target recognition techniques to assist GPR operators searching for BEHs. In this work deep learning ANNs are used to detect BEHs and discriminate them from harmless clutter. We apply these techniques to a multi-antennae, handheld GPR with centimeter-accurate positioning system that was used to collect data over prepared lanes containing a wide range of BEHs. This work demonstrates that deep learning ANNs can automatically extract meaningful information from complex GPR signatures, complementing existing GPR anomaly detection and classification techniques.

Automatic spatiotemporal matching of detected pleural thickenings

NASA Astrophysics Data System (ADS)

Chaisaowong, Kraisorn; Keller, Simon Kai; Kraus, Thomas

2014-01-01

Pleural thickenings can be found in asbestos exposed patient's lung. Non-invasive diagnosis including CT imaging can detect aggressive malignant pleural mesothelioma in its early stage. In order to create a quantitative documentation of automatic detected pleural thickenings over time, the differences in volume and thickness of the detected thickenings have to be calculated. Physicians usually estimate the change of each thickening via visual comparison which provides neither quantitative nor qualitative measures. In this work, automatic spatiotemporal matching techniques of the detected pleural thickenings at two points of time based on the semi-automatic registration have been developed, implemented, and tested so that the same thickening can be compared fully automatically. As result, the application of the mapping technique using the principal components analysis turns out to be advantageous than the feature-based mapping using centroid and mean Hounsfield Units of each thickening, since the resulting sensitivity was improved to 98.46% from 42.19%, while the accuracy of feature-based mapping is only slightly higher (84.38% to 76.19%).

Group Dynamics in Automatic Imitation

PubMed Central

Wilson, Neil; Reddy, Geetha; Catmur, Caroline

2016-01-01

Imitation–matching the configural body movements of another individual–plays a crucial part in social interaction. We investigated whether automatic imitation is not only influenced by who we imitate (ingroup vs. outgroup member) but also by the nature of an expected interaction situation (competitive vs. cooperative). In line with assumptions from Social Identity Theory), we predicted that both social group membership and the expected situation impact on the level of automatic imitation. We adopted a 2 (group membership target: ingroup, outgroup) x 2 (situation: cooperative, competitive) design. The dependent variable was the degree to which participants imitated the target in a reaction time automatic imitation task. 99 female students from two British Universities participated. We found a significant two-way interaction on the imitation effect. When interacting in expectation of cooperation, imitation was stronger for an ingroup target compared to an outgroup target. However, this was not the case in the competitive condition where imitation did not differ between ingroup and outgroup target. This demonstrates that the goal structure of an expected interaction will determine the extent to which intergroup relations influence imitation, supporting a social identity approach. PMID:27657926

Group Dynamics in Automatic Imitation.

PubMed

Gleibs, Ilka H; Wilson, Neil; Reddy, Geetha; Catmur, Caroline

Imitation-matching the configural body movements of another individual-plays a crucial part in social interaction. We investigated whether automatic imitation is not only influenced by who we imitate (ingroup vs. outgroup member) but also by the nature of an expected interaction situation (competitive vs. cooperative). In line with assumptions from Social Identity Theory), we predicted that both social group membership and the expected situation impact on the level of automatic imitation. We adopted a 2 (group membership target: ingroup, outgroup) x 2 (situation: cooperative, competitive) design. The dependent variable was the degree to which participants imitated the target in a reaction time automatic imitation task. 99 female students from two British Universities participated. We found a significant two-way interaction on the imitation effect. When interacting in expectation of cooperation, imitation was stronger for an ingroup target compared to an outgroup target. However, this was not the case in the competitive condition where imitation did not differ between ingroup and outgroup target. This demonstrates that the goal structure of an expected interaction will determine the extent to which intergroup relations influence imitation, supporting a social identity approach.

Divided attention enhances the recognition of emotional stimuli: evidence from the attentional boost effect.

PubMed

Rossi-Arnaud, Clelia; Spataro, Pietro; Costanzi, Marco; Saraulli, Daniele; Cestari, Vincenzo

2018-01-01

The present study examined predictions of the early-phase-elevated-attention hypothesis of the attentional boost effect (ABE), which suggests that transient increases in attention at encoding, as instantiated in the ABE paradigm, should enhance the recognition of neutral and positive items (whose encoding is mostly based on controlled processes), while having small or null effects on the recognition of negative items (whose encoding is primarily based on automatic processes). Participants were presented a sequence of negative, neutral and positive stimuli (pictures in Experiment 1, words in Experiment 2) associated to target (red) squares, distractor (green) squares or no squares (baseline condition). They were told to attend to the pictures/words and simultaneously press the spacebar of the computer when a red square appeared. In a later recognition task, stimuli associated to target squares were recognised better than stimuli associated to distractor squares, replicating the standard ABE. More importantly, we also found that: (a) the memory enhancement following target detection occurred with all types of stimuli (neutral, negative and positive) and (b) the advantage of negative stimuli over neutral stimuli was intact in the DA condition. These findings suggest that the encoding of negative stimuli depends on both controlled (attention-dependent) and automatic (attention-independent) processes.

How automatic is the hand's automatic pilot? Evidence from dual-task studies.

PubMed

McIntosh, Robert D; Mulroue, Amy; Brockmole, James R

2010-10-01

The ability to correct reaching movements for changes in target position has been described as the hand's 'automatic pilot'. These corrections are preconscious and occur by default in double-step reaching tasks, even if the goal is to react to the target jump in some other way, for instance by stopping the movement (STOP instruction). Nonetheless, corrections are strongly modulated by conscious intention: participants make more corrections when asked to follow the target (GO instruction) and can suppress them when explicitly asked not to follow the target (NOGO instruction). We studied the influence of a cognitively demanding (auditory 1-back) task upon correction behaviour under GO, STOP and NOGO instructions. Correction rates under the STOP instruction were unaffected by cognitive load, consistent with the assumption that they reflect the default behaviour of the automatic pilot. Correction rates under the GO instruction were also unaffected, suggesting that minimal cognitive resources are required to enhance online correction. By contrast, cognitive load impeded the ability to suppress online corrections under the NOGO instruction. These data reveal a constitutional bias in the automatic pilot system: intentional suppression of the default correction behaviour is cognitively demanding, but enhancement towards greater responsiveness is seemingly effortless.

System transfer modelling for automatic target recognizer evaluations

NASA Astrophysics Data System (ADS)

Clark, Lloyd G.

1991-11-01

Image processing to accomplish automatic recognition of military vehicles has promised increased weapons systems effectiveness and reduced timelines for a number of Department of Defense missions. Automatic Target Recognizers (ATR) are often claimed to be able to recognize many different ground vehicles as possible targets in military air-to- surface targeting applications. The targeting scenario conditions include different vehicle poses and histories as well as a variety of imaging geometries, intervening atmospheres, and background environments. Testing these ATR subsystems in most cases has been limited to a handful of the scenario conditions of interest, as is represented by imagery collected with the desired imaging sensor. The question naturally arises as to how robust the performance of the ATR is for all scenario conditions of interest, not just for the set of imagery upon which an algorithm was trained.

Aphasic Patients Exhibit a Reversal of Hemispheric Asymmetries in Categorical Color Discrimination

PubMed Central

Paluy, Yulia; Gilbert, Aubrey L.; Baldo, Juliana V.; Dronkers, Nina F.; Ivry, Richard B.

2010-01-01

Patients with left hemisphere (LH) or right hemisphere (RH) brain injury due to stroke were tested on a speeded, color discrimination task in which two factors were manipulated: 1) the categorical relationship between the target and the distracters and 2) the visual field in which the target was presented. Similar to controls, the RH patients were faster in detecting targets in the right visual field when the target and distracters had different color names compared to when their names were the same. This effect was absent in the LH patients, consistent with the hypothesis that injury to the left hemisphere handicaps the automatic activation of lexical codes. Moreover, the LH patients showed a reversed effect, such that the advantage of different target-distracter names was now evident for targets in the left visual field. This reversal may suggest a reorganization of the color lexicon in the right hemisphere following left hemisphere brain injury and/or the unmasking of a heightened right hemisphere sensitivity to color categories. PMID:21216454

Automatic detection of asteroids by 16" and 41" telescopes at the SFA Observatory

NASA Astrophysics Data System (ADS)

Fuls, David Carson

2015-05-01

Major upgrades were carried out to the hardware and software of the 16-inch and 41-inch telescopes at the Stephen F. Austin State University Observatory. These upgrades allow remote operation of both telescopes and are specifically designed to enhance the amount of time the telescopes are looking for Near Earth Objects (NEO's). This ability was tested by obtaining images and astrometry data remotely from both telescopes of target NEO's which required follow-up observation.

Sequential Analysis of Automatic Target Detection with Classification Algorithms and Optimality of Dynamic Decision Making Under Uncertainty

DTIC Science & Technology

2013-02-01

Pavlovian   drug  cues  to  produce   excessive  “wanting”  to...motivation:  Incentive   salience  boosts  of   drug  or  appetite  states.  Behavioral  Brain  Science  31:440-­‐441...learning   into  motivation.  In  Gutkin,  B.  and  Ahmed,  S.H.  (Eds.)  Computational  Neuroscience  of   Drug

Processing the in the parafovea: are articles skipped automatically?

PubMed

Angele, Bernhard; Rayner, Keith

2013-03-01

One of the words that readers of English skip most often is the definite article the. Most accounts of reading assume that in order for a reader to skip a word, it must have received some lexical processing. The definite article is skipped so regularly, however, that the oculomotor system might have learned to skip the letter string t-h-e automatically. We tested whether skipping of articles in English is sensitive to context information or whether it is truly automatic in the sense that any occurrence of the letter string the will trigger a skip. This was done using the gaze-contingent boundary paradigm (Rayner, 1975) to provide readers with false parafoveal previews of the article the. All experimental sentences contained a short target verb, the preview of which could be correct (i.e., identical to the actual subsequent word in the sentence; e.g., ace), a nonword (tda), or an infelicitous article preview (the). Our results indicated that readers tended to skip the infelicitous the previews frequently, suggesting that, in many cases, they seemed to be unable to detect the syntactic anomaly in the preview and based their skipping decision solely on the orthographic properties of the article. However, there was some evidence that readers sometimes detected the anomaly, as they also showed increased skipping of the pretarget word in the the preview condition. (c) 2013 APA, all rights reserved.

Risk Assessment of Alzheimer's Disease using the Information Diffusion Model from Structural Magnetic Resonance Imaging.

PubMed

Beheshti, Iman; Olya, Hossain G T; Demirel, Hasan

2016-04-05

Recently, automatic risk assessment methods have been a target for the detection of Alzheimer's disease (AD) risk. This study aims to develop an automatic computer-aided AD diagnosis technique for risk assessment of AD using information diffusion theory. Information diffusion is a fuzzy mathematics logic of set-value that is used for risk assessment of natural phenomena, which attaches fuzziness (uncertainty) and incompleteness. Data were obtained from voxel-based morphometry analysis of structural magnetic resonance imaging. The information diffusion model results revealed that the risk of AD increases with a reduction of the normalized gray matter ratio (p > 0.5, normalized gray matter ratio <40%). The information diffusion model results were evaluated by calculation of the correlation of two traditional risk assessments of AD, the Mini-Mental State Examination and the Clinical Dementia Rating. The correlation results revealed that the information diffusion model findings were in line with Mini-Mental State Examination and Clinical Dementia Rating results. Application of information diffusion model contributes to the computerization of risk assessment of AD, which has a practical implication for the early detection of AD.

Research on application of LADAR in ground vehicle recognition

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Shen, Zhuoxun

2009-11-01

For the requirement of many practical applications in the field of military, the research of 3D target recognition is active. The representation that captures the salient attributes of a 3D target independent of the viewing angle will be especially useful to the automatic 3D target recognition system. This paper presents a new approach of image generation based on Laser Detection and Ranging (LADAR) data. Range image of target is obtained by transformation of point cloud. In order to extract features of different ground vehicle targets and to recognize targets, zernike moment properties of typical ground vehicle targets are researched in this paper. A technique of support vector machine is applied to the classification and recognition of target. The new method of image generation and feature representation has been applied to the outdoor experiments. Through outdoor experiments, it can be proven that the method of image generation is stability, the moments are effective to be used as features for recognition, and the LADAR can be applied to the field of 3D target recognition.

The algorithm for automatic detection of the calibration object

NASA Astrophysics Data System (ADS)

Artem, Kruglov; Irina, Ugfeld

2017-06-01

The problem of the automatic image calibration is considered in this paper. The most challenging task of the automatic calibration is a proper detection of the calibration object. The solving of this problem required the appliance of the methods and algorithms of the digital image processing, such as morphology, filtering, edge detection, shape approximation. The step-by-step process of the development of the algorithm and its adopting to the specific conditions of the log cuts in the image's background is presented. Testing of the automatic calibration module was carrying out under the conditions of the production process of the logging enterprise. Through the tests the average possibility of the automatic isolating of the calibration object is 86.1% in the absence of the type 1 errors. The algorithm was implemented in the automatic calibration module within the mobile software for the log deck volume measurement.

Integrating visual learning within a model-based ATR system

NASA Astrophysics Data System (ADS)

Carlotto, Mark; Nebrich, Mark

2017-05-01

Automatic target recognition (ATR) systems, like human photo-interpreters, rely on a variety of visual information for detecting, classifying, and identifying manmade objects in aerial imagery. We describe the integration of a visual learning component into the Image Data Conditioner (IDC) for target/clutter and other visual classification tasks. The component is based on an implementation of a model of the visual cortex developed by Serre, Wolf, and Poggio. Visual learning in an ATR context requires the ability to recognize objects independent of location, scale, and rotation. Our method uses IDC to extract, rotate, and scale image chips at candidate target locations. A bootstrap learning method effectively extends the operation of the classifier beyond the training set and provides a measure of confidence. We show how the classifier can be used to learn other features that are difficult to compute from imagery such as target direction, and to assess the performance of the visual learning process itself.

Ships and Maritime Targets Observation Campaigns Using Available C- and X-Band SAR Satellite

NASA Astrophysics Data System (ADS)

Velotto, Domenico; Bentes, Carlos; Lehner, Susanne

2015-04-01

Obviously, radar resolution and swath width are two very important factors when it comes to synthetic aperture radar (SAR) maritime targets detections. The dilemma of using single polarization SAR imagery with higher resolution and coverage or quad- (or dual- polarimetric) imagery with its richness of information, is still unsolved when it comes to this application.In the framework of ESA project MARISS and EU project DOLPHIN, in situ campaigns aimed at solving this dilemma have been carried out. Single and multi- polarimetric SAR data acquired by TerraSAR-X, RADARSAT-2 and COSMO-SkyMed have been acquired with close time gaps and partial coverage overlap. In this way several moving and non-moving maritime targets have been imaged with different polarization, geometry and working frequency. Available ground truth reports provided by Automatic Identification System (AIS) data, nautical chart and wind farm location are used to validate the different types of maritime targets.

Automatic Target Recognition Based on Cross-Plot

PubMed Central

Wong, Kelvin Kian Loong; Abbott, Derek

2011-01-01

Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository. PMID:21980508

Detection of Suspicious Persons using Internet Camera

NASA Astrophysics Data System (ADS)

Terada, Kenji; Kamogashira, Daisuke

Recently, many brutal crimes have shocked us. Therefore, the importance of security and self-defense have increased more and more. It is necessary to develop an automatic method of detecting suspicious persons. In this paper, we propose a method of detecting suspicious persons using the internet camera. An image sequence is obtained by the internet camera. By using these images, the recognition of suspicious persons is carried out. Our method classifies the condition of the target person into 3 postures: walking, staying and sitting. The system employs the subspace method which uses three features: the value of movement, the number of looking around restlessly, and the rate of stopping and going. Some experimental results using a simple experimental system are also reported, which indicate effectiveness of the proposed method. In most scenes, the suspicious persons are able to be detected by the proposed method.

Automatic anatomy partitioning of the torso region on CT images by using multiple organ localizations with a group-wise calibration technique

NASA Astrophysics Data System (ADS)

Zhou, Xiangrong; Morita, Syoichi; Zhou, Xinxin; Chen, Huayue; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Hoshi, Hiroaki; Fujita, Hiroshi

2015-03-01

This paper describes an automatic approach for anatomy partitioning on three-dimensional (3D) computedtomography (CT) images that divide the human torso into several volume-of-interesting (VOI) images based on anatomical definition. The proposed approach combines several individual detections of organ-location with a groupwise organ-location calibration and correction to achieve an automatic and robust multiple-organ localization task. The essence of the proposed method is to jointly detect the 3D minimum bounding box for each type of organ shown on CT images based on intra-organ-image-textures and inter-organ-spatial-relationship in the anatomy. Machine-learning-based template matching and generalized Hough transform-based point-distribution estimation are used in the detection and calibration processes. We apply this approach to the automatic partitioning of a torso region on CT images, which are divided into 35 VOIs presenting major organ regions and tissues required by routine diagnosis in clinical medicine. A database containing 4,300 patient cases of high-resolution 3D torso CT images is used for training and performance evaluations. We confirmed that the proposed method was successful in target organ localization on more than 95% of CT cases. Only two organs (gallbladder and pancreas) showed a lower success rate: 71 and 78% respectively. In addition, we applied this approach to another database that included 287 patient cases of whole-body CT images scanned for positron emission tomography (PET) studies and used for additional performance evaluation. The experimental results showed that no significant difference between the anatomy partitioning results from those two databases except regarding the spleen. All experimental results showed that the proposed approach was efficient and useful in accomplishing localization tasks for major organs and tissues on CT images scanned using different protocols.

Electrophysiological Correlates of Automatic Visual Change Detection in School-Age Children

ERIC Educational Resources Information Center

Clery, Helen; Roux, Sylvie; Besle, Julien; Giard, Marie-Helene; Bruneau, Nicole; Gomot, Marie

2012-01-01

Automatic stimulus-change detection is usually investigated in the auditory modality by studying Mismatch Negativity (MMN). Although the change-detection process occurs in all sensory modalities, little is known about visual deviance detection, particularly regarding the development of this brain function throughout childhood. The aim of the…

Automatic event recognition and anomaly detection with attribute grammar by learning scene semantics

NASA Astrophysics Data System (ADS)

Qi, Lin; Yao, Zhenyu; Li, Li; Dong, Junyu

2007-11-01

In this paper we present a novel framework for automatic event recognition and abnormal behavior detection with attribute grammar by learning scene semantics. This framework combines learning scene semantics by trajectory analysis and constructing attribute grammar-based event representation. The scene and event information is learned automatically. Abnormal behaviors that disobey scene semantics or event grammars rules are detected. By this method, an approach to understanding video scenes is achieved. Further more, with this prior knowledge, the accuracy of abnormal event detection is increased.

Utilizing Linked Open Data Sources for Automatic Generation of Semantic Metadata

NASA Astrophysics Data System (ADS)

Nummiaho, Antti; Vainikainen, Sari; Melin, Magnus

In this paper we present an application that can be used to automatically generate semantic metadata for tags given as simple keywords. The application that we have implemented in Java programming language creates the semantic metadata by linking the tags to concepts in different semantic knowledge bases (CrunchBase, DBpedia, Freebase, KOKO, Opencyc, Umbel and/or WordNet). The steps that our application takes in doing so include detecting possible languages, finding spelling suggestions and finding meanings from amongst the proper nouns and common nouns separately. Currently, our application supports English, Finnish and Swedish words, but other languages could be included easily if the required lexical tools (spellcheckers, etc.) are available. The created semantic metadata can be of great use in, e.g., finding and combining similar contents, creating recommendations and targeting advertisements.

Real-time multisensor data fusion for target detection, classification, tracking, counting, and range estimates

NASA Astrophysics Data System (ADS)

Tsui, Eddy K.; Thomas, Russell L.

2004-09-01

As part of the Commanding General of Army Material Command's Research, Development & Engineering Command (RDECOM), the U.S. Army Research Development and Engineering Center (ARDEC), Picatinny funded a joint development effort with McQ Associates, Inc. to develop an Advanced Minefield Sensor (AMS) as a technology evaluation prototype for the Anti-Personnel Landmine Alternatives (APLA) Track III program. This effort laid the fundamental groundwork of smart sensors for detection and classification of targets, identification of combatant or noncombatant, target location and tracking at and between sensors, fusion of information across targets and sensors, and automatic situation awareness to the 1st responder. The efforts have culminated in developing a performance oriented architecture meeting the requirements of size, weight, and power (SWAP). The integrated digital signal processor (DSP) paradigm is capable of computing signals from sensor modalities to extract needed information within either a 360° or fixed field of view with acceptable false alarm rate. This paper discusses the challenges in the developments of such a sensor, focusing on achieving reasonable operating ranges, achieving low power, small size and low cost, and applications for extensions of this technology.

System for critical infrastructure security based on multispectral observation-detection module

NASA Astrophysics Data System (ADS)

Trzaskawka, Piotr; Kastek, Mariusz; Życzkowski, Marek; Dulski, Rafał; Szustakowski, Mieczysław; Ciurapiński, Wiesław; Bareła, Jarosław

2013-10-01

Recent terrorist attacks and possibilities of such actions in future have forced to develop security systems for critical infrastructures that embrace sensors technologies and technical organization of systems. The used till now perimeter protection of stationary objects, based on construction of a ring with two-zone fencing, visual cameras with illumination are efficiently displaced by the systems of the multisensor technology that consists of: visible technology - day/night cameras registering optical contrast of a scene, thermal technology - cheap bolometric cameras recording thermal contrast of a scene and active ground radars - microwave and millimetre wavelengths that record and detect reflected radiation. Merging of these three different technologies into one system requires methodology for selection of technical conditions of installation and parameters of sensors. This procedure enables us to construct a system with correlated range, resolution, field of view and object identification. Important technical problem connected with the multispectral system is its software, which helps couple the radar with the cameras. This software can be used for automatic focusing of cameras, automatic guiding cameras to an object detected by the radar, tracking of the object and localization of the object on the digital map as well as target identification and alerting. Based on "plug and play" architecture, this system provides unmatched flexibility and simplistic integration of sensors and devices in TCP/IP networks. Using a graphical user interface it is possible to control sensors and monitor streaming video and other data over the network, visualize the results of data fusion process and obtain detailed information about detected intruders over a digital map. System provide high-level applications and operator workload reduction with features such as sensor to sensor cueing from detection devices, automatic e-mail notification and alarm triggering. The paper presents a structure and some elements of critical infrastructure protection solution which is based on a modular multisensor security system. System description is focused mainly on methodology of selection of sensors parameters. The results of the tests in real conditions are also presented.

A multispectral automatic target recognition application for maritime surveillance, search, and rescue

NASA Astrophysics Data System (ADS)

Schoonmaker, Jon; Reed, Scott; Podobna, Yuliya; Vazquez, Jose; Boucher, Cynthia

2010-04-01

Due to increased security concerns, the commitment to monitor and maintain security in the maritime environment is increasingly a priority. A country's coast is the most vulnerable area for the incursion of illegal immigrants, terrorists and contraband. This work illustrates the ability of a low-cost, light-weight, multi-spectral, multi-channel imaging system to handle the environment and see under difficult marine conditions. The system and its implemented detecting and tracking technologies should be organic to the maritime homeland security community for search and rescue, fisheries, defense, and law enforcement. It is tailored for airborne and ship based platforms to detect, track and monitor suspected objects (such as semi-submerged targets like marine mammals, vessels in distress, and drug smugglers). In this system, automated detection and tracking technology is used to detect, classify and localize potential threats or objects of interest within the imagery provided by the multi-spectral system. These algorithms process the sensor data in real time, thereby providing immediate feedback when features of interest have been detected. A supervised detection system based on Haar features and Cascade Classifiers is presented and results are provided on real data. The system is shown to be extendable and reusable for a variety of different applications.

Application of image recognition-based automatic hyphae detection in fungal keratitis.

PubMed

Wu, Xuelian; Tao, Yuan; Qiu, Qingchen; Wu, Xinyi

2018-03-01

The purpose of this study is to evaluate the accuracy of two methods in diagnosis of fungal keratitis, whereby one method is automatic hyphae detection based on images recognition and the other method is corneal smear. We evaluate the sensitivity and specificity of the method in diagnosis of fungal keratitis, which is automatic hyphae detection based on image recognition. We analyze the consistency of clinical symptoms and the density of hyphae, and perform quantification using the method of automatic hyphae detection based on image recognition. In our study, 56 cases with fungal keratitis (just single eye) and 23 cases with bacterial keratitis were included. All cases underwent the routine inspection of slit lamp biomicroscopy, corneal smear examination, microorganism culture and the assessment of in vivo confocal microscopy images before starting medical treatment. Then, we recognize the hyphae images of in vivo confocal microscopy by using automatic hyphae detection based on image recognition to evaluate its sensitivity and specificity and compare with the method of corneal smear. The next step is to use the index of density to assess the severity of infection, and then find the correlation with the patients' clinical symptoms and evaluate consistency between them. The accuracy of this technology was superior to corneal smear examination (p < 0.05). The sensitivity of the technology of automatic hyphae detection of image recognition was 89.29%, and the specificity was 95.65%. The area under the ROC curve was 0.946. The correlation coefficient between the grading of the severity in the fungal keratitis by the automatic hyphae detection based on image recognition and the clinical grading is 0.87. The technology of automatic hyphae detection based on image recognition was with high sensitivity and specificity, able to identify fungal keratitis, which is better than the method of corneal smear examination. This technology has the advantages when compared with the conventional artificial identification of confocal microscope corneal images, of being accurate, stable and does not rely on human expertise. It was the most useful to the medical experts who are not familiar with fungal keratitis. The technology of automatic hyphae detection based on image recognition can quantify the hyphae density and grade this property. Being noninvasive, it can provide an evaluation criterion to fungal keratitis in a timely, accurate, objective and quantitative manner.

[Application of automatic photography in Schistosoma japonicum miracidium hatching experiments].

PubMed

Ming-Li, Zhou; Ai-Ling, Cai; Xue-Feng, Wang

2016-05-20

To explore the value of automatic photography in the observation of results of Schistosoma japonicum miracidium hatching experiments. Some fresh S. japonicum eggs were added into cow feces, and the samples of feces were divided into a low infested experimental group and a high infested group (40 samples each group). In addition, there was a negative control group with 40 samples of cow feces without S. japonicum eggs. The conventional nylon bag S. japonicum miracidium hatching experiments were performed. The process was observed with the method of flashlight and magnifying glass combined with automatic video (automatic photography method), and, at the same time, with the naked eye observation method. The results were compared. In the low infested group, the miracidium positive detection rates were 57.5% and 85.0% by the naked eye observation method and automatic photography method, respectively ( χ 2 = 11.723, P < 0.05). In the high infested group, the positive detection rates were 97.5% and 100% by the naked eye observation method and automatic photography method, respectively ( χ 2 = 1.253, P > 0.05). In the two infested groups, the average positive detection rates were 77.5% and 92.5% by the naked eye observation method and automatic photography method, respectively ( χ 2 = 6.894, P < 0.05). The automatic photography can effectively improve the positive detection rate in the S. japonicum miracidium hatching experiments.

Automated night/day standoff detection, tracking, and identification of personnel for installation protection

NASA Astrophysics Data System (ADS)

Lemoff, Brian E.; Martin, Robert B.; Sluch, Mikhail; Kafka, Kristopher M.; McCormick, William; Ice, Robert

2013-06-01

The capability to positively and covertly identify people at a safe distance, 24-hours per day, could provide a valuable advantage in protecting installations, both domestically and in an asymmetric warfare environment. This capability would enable installation security officers to identify known bad actors from a safe distance, even if they are approaching under cover of darkness. We will describe an active-SWIR imaging system being developed to automatically detect, track, and identify people at long range using computer face recognition. The system illuminates the target with an eye-safe and invisible SWIR laser beam, to provide consistent high-resolution imagery night and day. SWIR facial imagery produced by the system is matched against a watch-list of mug shots using computer face recognition algorithms. The current system relies on an operator to point the camera and to review and interpret the face recognition results. Automation software is being developed that will allow the system to be cued to a location by an external system, automatically detect a person, track the person as they move, zoom in on the face, select good facial images, and process the face recognition results, producing alarms and sharing data with other systems when people are detected and identified. Progress on the automation of this system will be presented along with experimental night-time face recognition results at distance.

Automatic thermographic image defect detection of composites

NASA Astrophysics Data System (ADS)

Luo, Bin; Liebenberg, Bjorn; Raymont, Jeff; Santospirito, SP

2011-05-01

Detecting defects, and especially reliably measuring defect sizes, are critical objectives in automatic NDT defect detection applications. In this work, the Sentence software is proposed for the analysis of pulsed thermography and near IR images of composite materials. Furthermore, the Sentence software delivers an end-to-end, user friendly platform for engineers to perform complete manual inspections, as well as tools that allow senior engineers to develop inspection templates and profiles, reducing the requisite thermographic skill level of the operating engineer. Finally, the Sentence software can also offer complete independence of operator decisions by the fully automated "Beep on Defect" detection functionality. The end-to-end automatic inspection system includes sub-systems for defining a panel profile, generating an inspection plan, controlling a robot-arm and capturing thermographic images to detect defects. A statistical model has been built to analyze the entire image, evaluate grey-scale ranges, import sentencing criteria and automatically detect impact damage defects. A full width half maximum algorithm has been used to quantify the flaw sizes. The identified defects are imported into the sentencing engine which then sentences (automatically compares analysis results against acceptance criteria) the inspection by comparing the most significant defect or group of defects against the inspection standards.

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

Automatic detection of typical dust devils from Mars landscape images

NASA Astrophysics Data System (ADS)

Ogohara, Kazunori; Watanabe, Takeru; Okumura, Susumu; Hatanaka, Yuji

2018-02-01

This paper presents an improved algorithm for automatic detection of Martian dust devils that successfully extracts tiny bright dust devils and obscured large dust devils from two subtracted landscape images. These dust devils are frequently observed using visible cameras onboard landers or rovers. Nevertheless, previous research on automated detection of dust devils has not focused on these common types of dust devils, but on dust devils that appear on images to be irregularly bright and large. In this study, we detect these common dust devils automatically using two kinds of parameter sets for thresholding when binarizing subtracted images. We automatically extract dust devils from 266 images taken by the Spirit rover to evaluate our algorithm. Taking dust devils detected by visual inspection to be ground truth, the precision, recall and F-measure values are 0.77, 0.86, and 0.81, respectively.

Automatic detection of articulation disorders in children with cleft lip and palate.

PubMed

Maier, Andreas; Hönig, Florian; Bocklet, Tobias; Nöth, Elmar; Stelzle, Florian; Nkenke, Emeka; Schuster, Maria

2009-11-01

Speech of children with cleft lip and palate (CLP) is sometimes still disordered even after adequate surgical and nonsurgical therapies. Such speech shows complex articulation disorders, which are usually assessed perceptually, consuming time and manpower. Hence, there is a need for an easy to apply and reliable automatic method. To create a reference for an automatic system, speech data of 58 children with CLP were assessed perceptually by experienced speech therapists for characteristic phonetic disorders at the phoneme level. The first part of the article aims to detect such characteristics by a semiautomatic procedure and the second to evaluate a fully automatic, thus simple, procedure. The methods are based on a combination of speech processing algorithms. The semiautomatic method achieves moderate to good agreement (kappa approximately 0.6) for the detection of all phonetic disorders. On a speaker level, significant correlations between the perceptual evaluation and the automatic system of 0.89 are obtained. The fully automatic system yields a correlation on the speaker level of 0.81 to the perceptual evaluation. This correlation is in the range of the inter-rater correlation of the listeners. The automatic speech evaluation is able to detect phonetic disorders at an experts'level without any additional human postprocessing.

Automatic colonic lesion detection and tracking in endoscopic videos

NASA Astrophysics Data System (ADS)

Li, Wenjing; Gustafsson, Ulf; A-Rahim, Yoursif

2011-03-01

The biology of colorectal cancer offers an opportunity for both early detection and prevention. Compared with other imaging modalities, optical colonoscopy is the procedure of choice for simultaneous detection and removal of colonic polyps. Computer assisted screening makes it possible to assist physicians and potentially improve the accuracy of the diagnostic decision during the exam. This paper presents an unsupervised method to detect and track colonic lesions in endoscopic videos. The aim of the lesion screening and tracking is to facilitate detection of polyps and abnormal mucosa in real time as the physician is performing the procedure. For colonic lesion detection, the conventional marker controlled watershed based segmentation is used to segment the colonic lesions, followed by an adaptive ellipse fitting strategy to further validate the shape. For colonic lesion tracking, a mean shift tracker with background modeling is used to track the target region from the detection phase. The approach has been tested on colonoscopy videos acquired during regular colonoscopic procedures and demonstrated promising results.

Assessing the performance of a covert automatic target recognition algorithm

NASA Astrophysics Data System (ADS)

Ehrman, Lisa M.; Lanterman, Aaron D.

2005-05-01

Passive radar systems exploit illuminators of opportunity, such as TV and FM radio, to illuminate potential targets. Doing so allows them to operate covertly and inexpensively. Our research seeks to enhance passive radar systems by adding automatic target recognition (ATR) capabilities. In previous papers we proposed conducting ATR by comparing the radar cross section (RCS) of aircraft detected by a passive radar system to the precomputed RCS of aircraft in the target class. To effectively model the low-frequency setting, the comparison is made via a Rician likelihood model. Monte Carlo simulations indicate that the approach is viable. This paper builds on that work by developing a method for quickly assessing the potential performance of the ATR algorithm without using exhaustive Monte Carlo trials. This method exploits the relation between the probability of error in a binary hypothesis test under the Bayesian framework to the Chernoff information. Since the data are well-modeled as Rician, we begin by deriving a closed-form approximation for the Chernoff information between two Rician densities. This leads to an approximation for the probability of error in the classification algorithm that is a function of the number of available measurements. We conclude with an application that would be particularly cumbersome to accomplish via Monte Carlo trials, but that can be quickly addressed using the Chernoff information approach. This application evaluates the length of time that an aircraft must be tracked before the probability of error in the ATR algorithm drops below a desired threshold.

Using Activity-Related Behavioural Features towards More Effective Automatic Stress Detection

PubMed Central

Giakoumis, Dimitris; Drosou, Anastasios; Cipresso, Pietro; Tzovaras, Dimitrios; Hassapis, George; Gaggioli, Andrea; Riva, Giuseppe

2012-01-01

This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing. PMID:23028461

JEFX 10 demonstration of Cooperative Hunter Killer UAS and upstream data fusion

NASA Astrophysics Data System (ADS)

Funk, Brian K.; Castelli, Jonathan C.; Watkins, Adam S.; McCubbin, Christopher B.; Marshall, Steven J.; Barton, Jeffrey D.; Newman, Andrew J.; Peterson, Cammy K.; DeSena, Jonathan T.; Dutrow, Daniel A.; Rodriguez, Pedro A.

2011-05-01

The Johns Hopkins University Applied Physics Laboratory deployed and demonstrated a prototype Cooperative Hunter Killer (CHK) Unmanned Aerial System (UAS) capability and a prototype Upstream Data Fusion (UDF) capability as participants in the Joint Expeditionary Force Experiment 2010 in April 2010. The CHK capability was deployed at the Nevada Test and Training Range to prosecute a convoy protection operational thread. It used mission-level autonomy (MLA) software applied to a networked swarm of three Raven hunter UAS and a Procerus Miracle surrogate killer UAS, all equipped with full motion video (FMV). The MLA software provides the capability for the hunter-killer swarm to autonomously search an area or road network, divide the search area, deconflict flight paths, and maintain line of sight communications with mobile ground stations. It also provides an interface for an operator to designate a threat and initiate automatic engagement of the target by the killer UAS. The UDF prototype was deployed at the Maritime Operations Center at Commander Second Fleet, Naval Station Norfolk to provide intelligence analysts and the ISR commander with a common fused track picture from the available FMV sources. It consisted of a video exploitation component that automatically detected moving objects, a multiple hypothesis tracker that fused all of the detection data to produce a common track picture, and a display and user interface component that visualized the common track picture along with appropriate geospatial information such as maps and terrain as well as target coordinates and the source video.

Automated dynamic hollow fiber liquid-liquid-liquid microextraction combined with capillary electrophoresis for speciation of mercury in biological and environmental samples.

PubMed

Li, Pingjing; He, Man; Chen, Beibei; Hu, Bin

2015-10-09

A simple home-made automatic dynamic hollow fiber based liquid-liquid-liquid microextraction (AD-HF-LLLME) device was designed and constructed for the simultaneous extraction of organomercury and inorganic mercury species with the assistant of a programmable flow injection analyzer. With 18-crown-6 as the complexing reagent, mercury species including methyl-, ethyl-, phenyl- and inorganic mercury were extracted into the organic phase (chlorobenzene), and then back-extracted into the acceptor phase of 0.1% (m/v) 3-mercapto-1-propanesulfonic acid (MPS) aqueous solution. Compared with automatic static (AS)-HF-LLLME system, the extraction equilibrium of target mercury species was obtained in shorter time with higher extraction efficiency in AD-HF-LLLME system. Based on it, a new method of AD-HF-LLLME coupled with large volume sample stacking (LVSS)-capillary electrophoresis (CE)/UV detection was developed for the simultaneous analysis of methyl-, phenyl- and inorganic mercury species in biological samples and environmental water. Under the optimized conditions, AD-HF-LLLME provided high enrichment factors (EFs) of 149-253-fold within relatively short extraction equilibrium time (25min) and good precision with RSD between 3.8 and 8.1%. By combining AD-HF-LLLME with LVSS-CE/UV, EFs were magnified up to 2195-fold and the limits of detection (at S/N=3) for target mercury species were improved to be sub ppb level. Copyright © 2015 Elsevier B.V. All rights reserved.

Towards an automatic lab-on-valve-ion mobility spectrometric system for detection of cocaine abuse.

PubMed

Cocovi-Solberg, David J; Esteve-Turrillas, Francesc A; Armenta, Sergio; de la Guardia, Miguel; Miró, Manuel

2017-08-25

A lab-on-valve miniaturized system integrating on-line disposable micro-solid phase extraction has been interfaced with ion mobility spectrometry for the accurate and sensitive determination of cocaine and ecgonine methyl ester in oral fluids. The method is based on the automatic loading of 500μL of oral fluid along with the retention of target analytes and matrix clean-up by mixed-mode cationic/reversed-phase solid phase beads, followed by elution with 100μL of 2-propanol containing (3% v/v) ammonia, which are online injected into the IMS. The sorptive particles are automatically discarded after every individual assay inasmuch as the sorptive capacity of the sorbent material is proven to be dramatically deteriorated with reuse. The method provided a limit of detection of 0.3 and 0.14μgL -1 for cocaine and ecgonine methyl ester, respectively, with relative standard deviation values from 8 till 14% with a total analysis time per sample of 7.5min. Method trueness was evaluated by analyzing oral fluid samples spiked with cocaine at different concentration levels (1, 5 and 25μgL -1 ) affording relative recoveries within the range of 85±24%. Fifteen saliva samples were collected from volunteers and analysed following the proposed automatic procedure, showing a 40% cocaine occurrence with concentrations ranging from 1.3 to 97μgL -1 . Field saliva samples were also analysed by reference methods based on lateral flow immunoassay and gas chromatography-mass spectrometry. The application of this procedure to the control of oral fluids of cocaine consumers represents a step forward towards the development of a point-of-care cocaine abuse sensing system. Copyright © 2017 Elsevier B.V. All rights reserved.

Automatic identification of artifacts in electrodermal activity data.

PubMed

Taylor, Sara; Jaques, Natasha; Chen, Weixuan; Fedor, Szymon; Sano, Akane; Picard, Rosalind

2015-01-01

Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.

Recent advances in automatic alignment system for the National Ignition Facility

NASA Astrophysics Data System (ADS)

Wilhelmsen, Karl; Awwal, Abdul A. S.; Kalantar, Dan; Leach, Richard; Lowe-Webb, Roger; McGuigan, David; Miller Kamm, Vicki

2011-03-01

The automatic alignment system for the National Ignition Facility (NIF) is a large-scale parallel system that directs all 192 laser beams along the 300-m optical path to a 50-micron focus at target chamber in less than 50 minutes. The system automatically commands 9,000 stepping motors to adjust mirrors and other optics based upon images acquired from high-resolution digital cameras viewing beams at various locations. Forty-five control loops per beamline request image processing services running on a LINUX cluster to analyze these images of the beams and references, and automatically steer the beams toward the target. This paper discusses the upgrades to the NIF automatic alignment system to handle new alignment needs and evolving requirements as related to various types of experiments performed. As NIF becomes a continuously-operated system and more experiments are performed, performance monitoring is increasingly important for maintenance and commissioning work. Data, collected during operations, is analyzed for tuning of the laser and targeting maintenance work. Handling evolving alignment and maintenance needs is expected for the planned 30-year operational life of NIF.

Learning target masks in infrared linescan imagery

NASA Astrophysics Data System (ADS)

Fechner, Thomas; Rockinger, Oliver; Vogler, Axel; Knappe, Peter

1997-04-01

In this paper we propose a neural network based method for the automatic detection of ground targets in airborne infrared linescan imagery. Instead of using a dedicated feature extraction stage followed by a classification procedure, we propose the following three step scheme: In the first step of the recognition process, the input image is decomposed into its pyramid representation, thus obtaining a multiresolution signal representation. At the lowest three levels of the Laplacian pyramid a neural network filter of moderate size is trained to indicate the target location. The last step consists of a fusion process of the several neural network filters to obtain the final result. To perform this fusion we use a belief network to combine the various filter outputs in a statistical meaningful way. In addition, the belief network allows the integration of further knowledge about the image domain. By applying this multiresolution recognition scheme, we obtain a nearly scale- and rotational invariant target recognition with a significantly decreased false alarm rate compared with a single resolution target recognition scheme.

SU-F-J-113: Multi-Atlas Based Automatic Organ Segmentation for Lung Radiotherapy Planning

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

Kim, J; Han, J; Ailawadi, S

Purpose: Normal organ segmentation is one time-consuming and labor-intensive step for lung radiotherapy treatment planning. The aim of this study is to evaluate the performance of a multi-atlas based segmentation approach for automatic organs at risk (OAR) delineation. Methods: Fifteen Lung stereotactic body radiation therapy patients were randomly selected. Planning CT images and OAR contours of the heart - HT, aorta - AO, vena cava - VC, pulmonary trunk - PT, and esophagus – ES were exported and used as reference and atlas sets. For automatic organ delineation for a given target CT, 1) all atlas sets were deformably warpedmore » to the target CT, 2) the deformed sets were accumulated and normalized to produce organ probability density (OPD) maps, and 3) the OPD maps were converted to contours via image thresholding. Optimal threshold for each organ was empirically determined by comparing the auto-segmented contours against their respective reference contours. The delineated results were evaluated by measuring contour similarity metrics: DICE, mean distance (MD), and true detection rate (TD), where DICE=(intersection volume/sum of two volumes) and TD = {1.0 - (false positive + false negative)/2.0}. Diffeomorphic Demons algorithm was employed for CT-CT deformable image registrations. Results: Optimal thresholds were determined to be 0.53 for HT, 0.38 for AO, 0.28 for PT, 0.43 for VC, and 0.31 for ES. The mean similarity metrics (DICE[%], MD[mm], TD[%]) were (88, 3.2, 89) for HT, (79, 3.2, 82) for AO, (75, 2.7, 77) for PT, (68, 3.4, 73) for VC, and (51,2.7, 60) for ES. Conclusion: The investigated multi-atlas based approach produced reliable segmentations for the organs with large and relatively clear boundaries (HT and AO). However, the detection of small and narrow organs with diffused boundaries (ES) were challenging. Sophisticated atlas selection and multi-atlas fusion algorithms may further improve the quality of segmentations.« less

Young Children's Automatic Encoding of Social Categories

ERIC Educational Resources Information Center

Weisman, Kara; Johnson, Marissa V.; Shutts, Kristin

2015-01-01

The present research investigated young children's automatic encoding of two social categories that are highly relevant to adults: gender and race. Three- to 6-year-old participants learned facts about unfamiliar target children who varied in either gender or race and were asked to remember which facts went with which targets. When participants…

Optimization of LC-Orbitrap-HRMS acquisition and MZmine 2 data processing for nontarget screening of environmental samples using design of experiments.

PubMed

Hu, Meng; Krauss, Martin; Brack, Werner; Schulze, Tobias

2016-11-01

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a well-established technique for nontarget screening of contaminants in complex environmental samples. Automatic peak detection is essential, but its performance has only rarely been assessed and optimized so far. With the aim to fill this gap, we used pristine water extracts spiked with 78 contaminants as a test case to evaluate and optimize chromatogram and spectral data processing. To assess whether data acquisition strategies have a significant impact on peak detection, three values of MS cycle time (CT) of an LTQ Orbitrap instrument were tested. Furthermore, the key parameter settings of the data processing software MZmine 2 were optimized to detect the maximum number of target peaks from the samples by the design of experiments (DoE) approach and compared to a manual evaluation. The results indicate that short CT significantly improves the quality of automatic peak detection, which means that full scan acquisition without additional MS 2 experiments is suggested for nontarget screening. MZmine 2 detected 75-100 % of the peaks compared to manual peak detection at an intensity level of 10 5 in a validation dataset on both spiked and real water samples under optimal parameter settings. Finally, we provide an optimization workflow of MZmine 2 for LC-HRMS data processing that is applicable for environmental samples for nontarget screening. The results also show that the DoE approach is useful and effort-saving for optimizing data processing parameters. Graphical Abstract ᅟ.

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

PubMed

Nguyen, Thanh; Bui, Vy; Lam, Van; Raub, Christopher B; Chang, Lin-Ching; Nehmetallah, George

2017-06-26

We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

Automatic building identification under bomb damage conditions

NASA Astrophysics Data System (ADS)

Woodley, Robert; Noll, Warren; Barker, Joseph; Wunsch, Donald C., II

2009-05-01

Given the vast amount of image intelligence utilized in support of planning and executing military operations, a passive automated image processing capability for target identification is urgently required. Furthermore, transmitting large image streams from remote locations would quickly use available band width (BW) precipitating the need for processing to occur at the sensor location. This paper addresses the problem of automatic target recognition for battle damage assessment (BDA). We utilize an Adaptive Resonance Theory approach to cluster templates of target buildings. The results show that the network successfully classifies targets from non-targets in a virtual test bed environment.

Farm-specific economic value of automatic lameness detection systems in dairy cattle: From concepts to operational simulations.

PubMed

Van De Gucht, Tim; Saeys, Wouter; Van Meensel, Jef; Van Nuffel, Annelies; Vangeyte, Jurgen; Lauwers, Ludwig

2018-01-01

Although prototypes of automatic lameness detection systems for dairy cattle exist, information about their economic value is lacking. In this paper, a conceptual and operational framework for simulating the farm-specific economic value of automatic lameness detection systems was developed and tested on 4 system types: walkover pressure plates, walkover pressure mats, camera systems, and accelerometers. The conceptual framework maps essential factors that determine economic value (e.g., lameness prevalence, incidence and duration, lameness costs, detection performance, and their relationships). The operational simulation model links treatment costs and avoided losses with detection results and farm-specific information, such as herd size and lameness status. Results show that detection performance, herd size, discount rate, and system lifespan have a large influence on economic value. In addition, lameness prevalence influences the economic value, stressing the importance of an adequate prior estimation of the on-farm prevalence. The simulations provide first estimates for the upper limits for purchase prices of automatic detection systems. The framework allowed for identification of knowledge gaps obstructing more accurate economic value estimation. These include insights in cost reductions due to early detection and treatment, and links between specific lameness causes and their related losses. Because this model provides insight in the trade-offs between automatic detection systems' performance and investment price, it is a valuable tool to guide future research and developments. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A signal amplification electrochemical aptasensor for the detection of breast cancer cell via free-running DNA walker.

PubMed

Cai, Shuxian; Chen, Mei; Liu, Mengmeng; He, Wenhui; Liu, Zhijing; Wu, Dongzhi; Xia, Yaokun; Yang, Huanghao; Chen, Jinghua

2016-11-15

Herein, a signal magnification electrochemical aptasensor for the detection of breast cancer cell via free-running DNA walker is constructed. Theoretically, just one DNA walker, released by target cell-responsive reaction, can automatically cleave all D-RNA (a chimeric DNA/RNA oligonucleotide with a cleavage point rArU) anchored on electrode into shorter produces, giving rise to considerably detectable signal finally. Under the optimal conditions, the electrochemical signal decreased linearly with the concentration of MCF-7 cell. The linear range is from 0 to 500 cells mL(-1) with a detection limit of 47 cellsmL(-1). In a word, this approach may have advantages over traditional reported DNA machines for bioassay, particularly in terms of ease of operation, cost efficiency, free of labeling and of complex track design, which may hold great potential for wide application. Copyright © 2016 Elsevier B.V. All rights reserved.

MixDroid: A multi-features and multi-classifiers bagging system for Android malware detection

NASA Astrophysics Data System (ADS)

Huang, Weiqing; Hou, Erhang; Zheng, Liang; Feng, Weimiao

2018-05-01

In the past decade, Android platform has rapidly taken over the mobile market for its superior convenience and open source characteristics. However, with the popularity of Android, malwares targeting on Android devices are increasing rapidly, while the conventional rule-based and expert-experienced approaches are no longer able to handle such explosive growth. In this paper, combining with the theory of natural language processing and machine learning, we not only implement the basic feature extraction of permission application features, but also propose two innovative schemes of feature extraction: Dalvik opcode features and malicious code image, and implement an automatic Android malware detection system MixDroid which is based on multi-features and multi-classifiers. According to our experiment results on 20,000 Android applications, detection accuracy of MixDroid is 98.1%, which proves our schemes' effectiveness in Android malware detection.

Automatic Processing of Changes in Facial Emotions in Dysphoria: A Magnetoencephalography Study.

PubMed

Xu, Qianru; Ruohonen, Elisa M; Ye, Chaoxiong; Li, Xueqiao; Kreegipuu, Kairi; Stefanics, Gabor; Luo, Wenbo; Astikainen, Piia

2018-01-01

It is not known to what extent the automatic encoding and change detection of peripherally presented facial emotion is altered in dysphoria. The negative bias in automatic face processing in particular has rarely been studied. We used magnetoencephalography (MEG) to record automatic brain responses to happy and sad faces in dysphoric (Beck's Depression Inventory ≥ 13) and control participants. Stimuli were presented in a passive oddball condition, which allowed potential negative bias in dysphoria at different stages of face processing (M100, M170, and M300) and alterations of change detection (visual mismatch negativity, vMMN) to be investigated. The magnetic counterpart of the vMMN was elicited at all stages of face processing, indexing automatic deviance detection in facial emotions. The M170 amplitude was modulated by emotion, response amplitudes being larger for sad faces than happy faces. Group differences were found for the M300, and they were indexed by two different interaction effects. At the left occipital region of interest, the dysphoric group had larger amplitudes for sad than happy deviant faces, reflecting negative bias in deviance detection, which was not found in the control group. On the other hand, the dysphoric group showed no vMMN to changes in facial emotions, while the vMMN was observed in the control group at the right occipital region of interest. Our results indicate that there is a negative bias in automatic visual deviance detection, but also a general change detection deficit in dysphoria.

Designing and Implementing a Retrospective Earthquake Detection Framework at the U.S. Geological Survey National Earthquake Information Center

NASA Astrophysics Data System (ADS)

Patton, J.; Yeck, W.; Benz, H.

2017-12-01

The U.S. Geological Survey National Earthquake Information Center (USGS NEIC) is implementing and integrating new signal detection methods such as subspace correlation, continuous beamforming, multi-band picking and automatic phase identification into near-real-time monitoring operations. Leveraging the additional information from these techniques help the NEIC utilize a large and varied network on local to global scales. The NEIC is developing an ordered, rapid, robust, and decentralized framework for distributing seismic detection data as well as a set of formalized formatting standards. These frameworks and standards enable the NEIC to implement a seismic event detection framework that supports basic tasks, including automatic arrival time picking, social media based event detections, and automatic association of different seismic detection data into seismic earthquake events. In addition, this framework enables retrospective detection processing such as automated S-wave arrival time picking given a detected event, discrimination and classification of detected events by type, back-azimuth and slowness calculations, and ensuring aftershock and induced sequence detection completeness. These processes and infrastructure improve the NEIC's capabilities, accuracy, and speed of response. In addition, this same infrastructure provides an improved and convenient structure to support access to automatic detection data for both research and algorithmic development.

Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.

PubMed

Li, Jun; Mei, Xue; Prokhorov, Danil; Tao, Dacheng

2017-03-01

Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.

A robust recognition and accurate locating method for circular coded diagonal target

NASA Astrophysics Data System (ADS)

Bao, Yunna; Shang, Yang; Sun, Xiaoliang; Zhou, Jiexin

2017-10-01

As a category of special control points which can be automatically identified, artificial coded targets have been widely developed in the field of computer vision, photogrammetry, augmented reality, etc. In this paper, a new circular coded target designed by RockeTech technology Corp. Ltd is analyzed and studied, which is called circular coded diagonal target (CCDT). A novel detection and recognition method with good robustness is proposed in the paper, and implemented on Visual Studio. In this algorithm, firstly, the ellipse features of the center circle are used for rough positioning. Then, according to the characteristics of the center diagonal target, a circular frequency filter is designed to choose the correct center circle and eliminates non-target noise. The precise positioning of the coded target is done by the correlation coefficient fitting extreme value method. Finally, the coded target recognition is achieved by decoding the binary sequence in the outer ring of the extracted target. To test the proposed algorithm, this paper has carried out simulation experiments and real experiments. The results show that the CCDT recognition and accurate locating method proposed in this paper can robustly recognize and accurately locate the targets in complex and noisy background.

Automatic patient respiration failure detection system with wireless transmission

NASA Technical Reports Server (NTRS)

Dimeff, J.; Pope, J. M.

1968-01-01

Automatic respiration failure detection system detects respiration failure in patients with a surgically implanted tracheostomy tube, and actuates an audible and/or visual alarm. The system incorporates a miniature radio transmitter so that the patient is unencumbered by wires yet can be monitored from a remote location.

Automatic PSO-Based Deformable Structures Markerless Tracking in Laparoscopic Cholecystectomy

NASA Astrophysics Data System (ADS)

Djaghloul, Haroun; Batouche, Mohammed; Jessel, Jean-Pierre

An automatic and markerless tracking method of deformable structures (digestive organs) during laparoscopic cholecystectomy intervention that uses the (PSO) behavour and the preoperative a priori knowledge is presented. The associated shape to the global best particles of the population determines a coarse representation of the targeted organ (the gallbladder) in monocular laparoscopic colored images. The swarm behavour is directed by a new fitness function to be optimized to improve the detection and tracking performance. The function is defined by a linear combination of two terms, namely, the human a priori knowledge term (H) and the particle's density term (D). Under the limits of standard (PSO) characteristics, experimental results on both synthetic and real data show the effectiveness and robustness of our method. Indeed, it outperforms existing methods without need of explicit initialization (such as active contours, deformable models and Gradient Vector Flow) on accuracy and convergence rate.

Learning to Like Exercising: Evaluative Conditioning Changes Automatic Evaluations of Exercising and Influences Subsequent Exercising Behavior.

PubMed

Antoniewicz, Franziska; Brand, Ralf

2016-04-01

This multistudy report used an experimental approach to alter automatic evaluations of exercise (AEE). First, we investigated the plasticity of AEE (study 1). A computerized evaluative conditioning task was developed that altered the AEE of participants in two experimental groups (acquisition of positive/negative associations involving exercising) and a control group (η2 part. = .11). Second, we examined connections between changes in AEE and subsequent exercise behavior (chosen intensity on a bike ergometer; study 2) in individuals that were placed in groups according to their baseline AEE. Group differences in exercise behavior were detected (η2 part. = .29). The effect was driven by the performance of the group with preexisting negative AEE that acquired more positive associations. This illustrates the effect of altered AEE on subsequent exercise behavior and the potential of AEE as a target for exercise intervention.

Convolution neural-network-based detection of lung structures

NASA Astrophysics Data System (ADS)

Hasegawa, Akira; Lo, Shih-Chung B.; Freedman, Matthew T.; Mun, Seong K.

1994-05-01

Chest radiography is one of the most primary and widely used techniques in diagnostic imaging. Nowadays with the advent of digital radiology, the digital medical image processing techniques for digital chest radiographs have attracted considerable attention, and several studies on the computer-aided diagnosis (CADx) as well as on the conventional image processing techniques for chest radiographs have been reported. In the automatic diagnostic process for chest radiographs, it is important to outline the areas of the lungs, the heart, and the diaphragm. This is because the original chest radiograph is composed of important anatomic structures and, without knowing exact positions of the organs, the automatic diagnosis may result in unexpected detections. The automatic extraction of an anatomical structure from digital chest radiographs can be a useful tool for (1) the evaluation of heart size, (2) automatic detection of interstitial lung diseases, (3) automatic detection of lung nodules, and (4) data compression, etc. Based on the clearly defined boundaries of heart area, rib spaces, rib positions, and rib cage extracted, one should be able to use this information to facilitate the tasks of the CADx on chest radiographs. In this paper, we present an automatic scheme for the detection of lung field from chest radiographs by using a shift-invariant convolution neural network. A novel algorithm for smoothing boundaries of lungs is also presented.

Neural network model for automatic traffic incident detection : executive summary.

DOT National Transportation Integrated Search

2001-04-01

Automatic freeway incident detection is an important component of advanced transportation management systems (ATMS) that provides information for emergency relief and traffic control and management purposes. In this research, a multi-paradigm intelli...

Supporting the Development and Adoption of Automatic Lameness Detection Systems in Dairy Cattle: Effect of System Cost and Performance on Potential Market Shares.

PubMed

Van De Gucht, Tim; Van Weyenberg, Stephanie; Van Nuffel, Annelies; Lauwers, Ludwig; Vangeyte, Jürgen; Saeys, Wouter

2017-10-08

Most automatic lameness detection system prototypes have not yet been commercialized, and are hence not yet adopted in practice. Therefore, the objective of this study was to simulate the effect of detection performance (percentage missed lame cows and percentage false alarms) and system cost on the potential market share of three automatic lameness detection systems relative to visual detection: a system attached to the cow, a walkover system, and a camera system. Simulations were done using a utility model derived from survey responses obtained from dairy farmers in Flanders, Belgium. Overall, systems attached to the cow had the largest market potential, but were still not competitive with visual detection. Increasing the detection performance or lowering the system cost led to higher market shares for automatic systems at the expense of visual detection. The willingness to pay for extra performance was €2.57 per % less missed lame cows, €1.65 per % less false alerts, and €12.7 for lame leg indication, respectively. The presented results could be exploited by system designers to determine the effect of adjustments to the technology on a system's potential adoption rate.

Automatic zebrafish heartbeat detection and analysis for zebrafish embryos.

PubMed

Pylatiuk, Christian; Sanchez, Daniela; Mikut, Ralf; Alshut, Rüdiger; Reischl, Markus; Hirth, Sofia; Rottbauer, Wolfgang; Just, Steffen

2014-08-01

A fully automatic detection and analysis method of heartbeats in videos of nonfixed and nonanesthetized zebrafish embryos is presented. This method reduces the manual workload and time needed for preparation and imaging of the zebrafish embryos, as well as for evaluating heartbeat parameters such as frequency, beat-to-beat intervals, and arrhythmicity. The method is validated by a comparison of the results from automatic and manual detection of the heart rates of wild-type zebrafish embryos 36-120 h postfertilization and of embryonic hearts with bradycardia and pauses in the cardiac contraction.

Diffusion tensor driven contour closing for cell microinjection targeting.

PubMed

Becattini, Gabriele; Mattos, Leonardo S; Caldwell, Darwin G

2010-01-01

This article introduces a novel approach to robust automatic detection of unstained living cells in bright-field (BF) microscope images with the goal of producing a target list for an automated microinjection system. The overall image analysis process is described and includes: preprocessing, ridge enhancement, image segmentation, shape analysis and injection point definition. The developed algorithm implements a new version of anisotropic contour completion (ACC) based on the partial differential equation (PDE) for heat diffusion which improves the cell segmentation process by elongating the edges only along their tangent direction. The developed ACC algorithm is equivalent to a dilation of the binary edge image with a continuous elliptic structural element that takes into account local orientation of the contours preventing extension towards normal direction. Experiments carried out on real images of 10 to 50 microm CHO-K1 adherent cells show a remarkable reliability in the algorithm along with up to 85% success for cell detection and injection point definition.

Visual Benefits in Apparent Motion Displays: Automatically Driven Spatial and Temporal Anticipation Are Partially Dissociated

PubMed Central

Ahrens, Merle-Marie; Veniero, Domenica; Gross, Joachim; Harvey, Monika; Thut, Gregor

2015-01-01

Many behaviourally relevant sensory events such as motion stimuli and speech have an intrinsic spatio-temporal structure. This will engage intentional and most likely unintentional (automatic) prediction mechanisms enhancing the perception of upcoming stimuli in the event stream. Here we sought to probe the anticipatory processes that are automatically driven by rhythmic input streams in terms of their spatial and temporal components. To this end, we employed an apparent visual motion paradigm testing the effects of pre-target motion on lateralized visual target discrimination. The motion stimuli either moved towards or away from peripheral target positions (valid vs. invalid spatial motion cueing) at a rhythmic or arrhythmic pace (valid vs. invalid temporal motion cueing). Crucially, we emphasized automatic motion-induced anticipatory processes by rendering the motion stimuli non-predictive of upcoming target position (by design) and task-irrelevant (by instruction), and by creating instead endogenous (orthogonal) expectations using symbolic cueing. Our data revealed that the apparent motion cues automatically engaged both spatial and temporal anticipatory processes, but that these processes were dissociated. We further found evidence for lateralisation of anticipatory temporal but not spatial processes. This indicates that distinct mechanisms may drive automatic spatial and temporal extrapolation of upcoming events from rhythmic event streams. This contrasts with previous findings that instead suggest an interaction between spatial and temporal attention processes when endogenously driven. Our results further highlight the need for isolating intentional from unintentional processes for better understanding the various anticipatory mechanisms engaged in processing behaviourally relevant stimuli with predictable spatio-temporal structure such as motion and speech. PMID:26623650

Priming production: Neural evidence for enhanced automatic semantic activity preceding language production in schizophrenia.

PubMed

Kuperberg, Gina R; Delaney-Busch, Nathaniel; Fanucci, Kristina; Blackford, Trevor

2018-01-01

Lexico-semantic disturbances are considered central to schizophrenia. Clinically, their clearest manifestation is in language production. However, most studies probing their underlying mechanisms have used comprehension or categorization tasks. Here, we probed automatic semantic activity prior to language production in schizophrenia using event-related potentials (ERPs). 19 people with schizophrenia and 16 demographically-matched healthy controls named target pictures that were very quickly preceded by masked prime words. To probe automatic semantic activity prior to production, we measured the N400 ERP component evoked by these targets. To determine the origin of any automatic semantic abnormalities, we manipulated the type of relationship between prime and target such that they overlapped in (a) their semantic features (semantically related, e.g. "cake" preceding a < picture of a pie >, (b) their initial phonemes (phonemically related, e.g. "stomach" preceding a < picture of a starfish >), or (c) both their semantic features and their orthographic/phonological word form (identity related, e.g. "socks" preceding a < picture of socks >). For each of these three types of relationship, the same targets were paired with unrelated prime words (counterbalanced across lists). We contrasted ERPs and naming times to each type of related target with its corresponding unrelated target. People with schizophrenia showed abnormal N400 modulation prior to naming identity related (versus unrelated) targets: whereas healthy control participants produced a smaller amplitude N400 to identity related than unrelated targets, patients showed the opposite pattern, producing a larger N400 to identity related than unrelated targets. This abnormality was specific to the identity related targets. Just like healthy control participants, people with schizophrenia produced a smaller N400 to semantically related than to unrelated targets, and showed no difference in the N400 evoked by phonemically related and unrelated targets. There were no differences between the two groups in the pattern of naming times across conditions. People with schizophrenia can show abnormal neural activity associated with automatic semantic processing prior to language production. The specificity of this abnormality to the identity related targets suggests that that, rather than arising from abnormalities of either semantic features or lexical form alone, it may stem from disruptions of mappings (connections) between the meaning of words and their form.

SAR image dataset of military ground targets with multiple poses for ATR

NASA Astrophysics Data System (ADS)

Belloni, Carole; Balleri, Alessio; Aouf, Nabil; Merlet, Thomas; Le Caillec, Jean-Marc

2017-10-01

Automatic Target Recognition (ATR) is the task of automatically detecting and classifying targets. Recognition using Synthetic Aperture Radar (SAR) images is interesting because SAR images can be acquired at night and under any weather conditions, whereas optical sensors operating in the visible band do not have this capability. Existing SAR ATR algorithms have mostly been evaluated using the MSTAR dataset.1 The problem with the MSTAR is that some of the proposed ATR methods have shown good classification performance even when targets were hidden,2 suggesting the presence of a bias in the dataset. Evaluations of SAR ATR techniques are currently challenging due to the lack of publicly available data in the SAR domain. In this paper, we present a high resolution SAR dataset consisting of images of a set of ground military target models taken at various aspect angles, The dataset can be used for a fair evaluation and comparison of SAR ATR algorithms. We applied the Inverse Synthetic Aperture Radar (ISAR) technique to echoes from targets rotating on a turntable and illuminated with a stepped frequency waveform. The targets in the database consist of four variants of two 1.7m-long models of T-64 and T-72 tanks. The gun, the turret position and the depression angle are varied to form 26 different sequences of images. The emitted signal spanned the frequency range from 13 GHz to 18 GHz to achieve a bandwidth of 5 GHz sampled with 4001 frequency points. The resolution obtained with respect to the size of the model targets is comparable to typical values obtained using SAR airborne systems. Single polarized images (Horizontal-Horizontal) are generated using the backprojection algorithm.3 A total of 1480 images are produced using a 20° integration angle. The images in the dataset are organized in a suggested training and testing set to facilitate a standard evaluation of SAR ATR algorithms.

Detection of reflecting surfaces by a statistical model

NASA Astrophysics Data System (ADS)

He, Qiang; Chu, Chee-Hung H.

2009-02-01

Remote sensing is widely used assess the destruction from natural disasters and to plan relief and recovery operations. How to automatically extract useful features and segment interesting objects from digital images, including remote sensing imagery, becomes a critical task for image understanding. Unfortunately, current research on automated feature extraction is ignorant of contextual information. As a result, the fidelity of populating attributes corresponding to interesting features and objects cannot be satisfied. In this paper, we present an exploration on meaningful object extraction integrating reflecting surfaces. Detection of specular reflecting surfaces can be useful in target identification and then can be applied to environmental monitoring, disaster prediction and analysis, military, and counter-terrorism. Our method is based on a statistical model to capture the statistical properties of specular reflecting surfaces. And then the reflecting surfaces are detected through cluster analysis.

Is gaze following purely reflexive or goal-directed instead? Revisiting the automaticity of orienting attention by gaze cues.

PubMed

Ricciardelli, Paola; Carcagno, Samuele; Vallar, Giuseppe; Bricolo, Emanuela

2013-01-01

Distracting gaze has been shown to elicit automatic gaze following. However, it is still debated whether the effects of perceived gaze are a simple automatic spatial orienting response or are instead sensitive to the context (i.e. goals and task demands). In three experiments, we investigated the conditions under which gaze following occurs. Participants were instructed to saccade towards one of two lateral targets. A face distracter, always present in the background, could gaze towards: (a) a task-relevant target--("matching" goal-directed gaze shift)--congruent or incongruent with the instructed direction, (b) a task-irrelevant target, orthogonal to the one instructed ("non-matching" goal-directed gaze shift), or (c) an empty spatial location (no-goal-directed gaze shift). Eye movement recordings showed faster saccadic latencies in correct trials in congruent conditions especially when the distracting gaze shift occurred before the instruction to make a saccade. Interestingly, while participants made a higher proportion of gaze-following errors (i.e. errors in the direction of the distracting gaze) in the incongruent conditions when the distracter's gaze shift preceded the instruction onset indicating an automatic gaze following, they never followed the distracting gaze when it was directed towards an empty location or a stimulus that was never the target. Taken together, these findings suggest that gaze following is likely to be a product of both automatic and goal-driven orienting mechanisms.

Neural network model for automatic traffic incident detection : final report, August 2001.

DOT National Transportation Integrated Search

2001-08-01

Automatic freeway incident detection is an important component of advanced transportation management systems (ATMS) that provides information for emergency relief and traffic control and management purposes. In this research, a multi-paradigm intelli...

A new type industrial total station based on target automatic collimation

NASA Astrophysics Data System (ADS)

Lao, Dabao; Zhou, Weihu; Ji, Rongyi; Dong, Dengfeng; Xiong, Zhi; Wei, Jiang

2018-01-01

In the case of industrial field measurement, the present measuring instruments work with manual operation and collimation, which give rise to low efficiency for field measurement. In order to solve the problem, a new type industrial total station is presented in this paper. The new instrument can identify and trace cooperative target automatically, in the mean time, coordinate of the target is measured in real time. For realizing the system, key technology including high precision absolutely distance measurement, small high accuracy angle measurement, target automatic collimation with vision, and quick precise controlling should be worked out. After customized system assemblage and adjustment, the new type industrial total station will be established. As the experiments demonstrated, the coordinate accuracy of the instrument is under 15ppm in the distance of 60m, which proved that the measuring system is feasible. The result showed that the total station can satisfy most industrial field measurement requirements.

Detecting cheaters without thinking: testing the automaticity of the cheater detection module.

PubMed

Van Lier, Jens; Revlin, Russell; De Neys, Wim

2013-01-01

Evolutionary psychologists have suggested that our brain is composed of evolved mechanisms. One extensively studied mechanism is the cheater detection module. This module would make people very good at detecting cheaters in a social exchange. A vast amount of research has illustrated performance facilitation on social contract selection tasks. This facilitation is attributed to the alleged automatic and isolated operation of the module (i.e., independent of general cognitive capacity). This study, using the selection task, tested the critical automaticity assumption in three experiments. Experiments 1 and 2 established that performance on social contract versions did not depend on cognitive capacity or age. Experiment 3 showed that experimentally burdening cognitive resources with a secondary task had no impact on performance on the social contract version. However, in all experiments, performance on a non-social contract version did depend on available cognitive capacity. Overall, findings validate the automatic and effortless nature of social exchange reasoning.

Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines

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

Gibbons, Steven J.; Kvaerna, Tormod; Harris, David B.

We report aftershock sequences following very large earthquakes present enormous challenges to near-real-time generation of seismic bulletins. The increase in analyst resources needed to relocate an inflated number of events is compounded by failures of phase-association algorithms and a significant deterioration in the quality of underlying, fully automatic event bulletins. Current processing pipelines were designed a generation ago, and, due to computational limitations of the time, are usually limited to single passes over the raw data. With current processing capability, multiple passes over the data are feasible. Processing the raw data at each station currently generates parametric data streams thatmore » are then scanned by a phase-association algorithm to form event hypotheses. We consider the scenario in which a large earthquake has occurred and propose to define a region of likely aftershock activity in which events are detected and accurately located, using a separate specially targeted semiautomatic process. This effort may focus on so-called pattern detectors, but here we demonstrate a more general grid-search algorithm that may cover wider source regions without requiring waveform similarity. Given many well-located aftershocks within our source region, we may remove all associated phases from the original detection lists prior to a new iteration of the phase-association algorithm. We provide a proof-of-concept example for the 2015 Gorkha sequence, Nepal, recorded on seismic arrays of the International Monitoring System. Even with very conservative conditions for defining event hypotheses within the aftershock source region, we can automatically remove about half of the original detections that could have been generated by Nepal earthquakes and reduce the likelihood of false associations and spurious event hypotheses. Lastly, further reductions in the number of detections in the parametric data streams are likely, using correlation and subspace detectors and/or empirical matched field processing.« less

Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines

DOE PAGES

Gibbons, Steven J.; Kvaerna, Tormod; Harris, David B.; ...

2016-06-08

We report aftershock sequences following very large earthquakes present enormous challenges to near-real-time generation of seismic bulletins. The increase in analyst resources needed to relocate an inflated number of events is compounded by failures of phase-association algorithms and a significant deterioration in the quality of underlying, fully automatic event bulletins. Current processing pipelines were designed a generation ago, and, due to computational limitations of the time, are usually limited to single passes over the raw data. With current processing capability, multiple passes over the data are feasible. Processing the raw data at each station currently generates parametric data streams thatmore » are then scanned by a phase-association algorithm to form event hypotheses. We consider the scenario in which a large earthquake has occurred and propose to define a region of likely aftershock activity in which events are detected and accurately located, using a separate specially targeted semiautomatic process. This effort may focus on so-called pattern detectors, but here we demonstrate a more general grid-search algorithm that may cover wider source regions without requiring waveform similarity. Given many well-located aftershocks within our source region, we may remove all associated phases from the original detection lists prior to a new iteration of the phase-association algorithm. We provide a proof-of-concept example for the 2015 Gorkha sequence, Nepal, recorded on seismic arrays of the International Monitoring System. Even with very conservative conditions for defining event hypotheses within the aftershock source region, we can automatically remove about half of the original detections that could have been generated by Nepal earthquakes and reduce the likelihood of false associations and spurious event hypotheses. Lastly, further reductions in the number of detections in the parametric data streams are likely, using correlation and subspace detectors and/or empirical matched field processing.« less

Automatic Detection of Student Mental Models during Prior Knowledge Activation in MetaTutor

ERIC Educational Resources Information Center

Rus, Vasile; Lintean, Mihai; Azevedo, Roger

2009-01-01

This paper presents several methods to automatically detecting students' mental models in MetaTutor, an intelligent tutoring system that teaches students self-regulatory processes during learning of complex science topics. In particular, we focus on detecting students' mental models based on student-generated paragraphs during prior knowledge…

Long-Term Tracking of a Specific Vehicle Using Airborne Optical Camera Systems

NASA Astrophysics Data System (ADS)

Kurz, F.; Rosenbaum, D.; Runge, H.; Cerra, D.; Mattyus, G.; Reinartz, P.

2016-06-01

In this paper we present two low cost, airborne sensor systems capable of long-term vehicle tracking. Based on the properties of the sensors, a method for automatic real-time, long-term tracking of individual vehicles is presented. This combines the detection and tracking of the vehicle in low frame rate image sequences and applies the lagged Cell Transmission Model (CTM) to handle longer tracking outages occurring in complex traffic situations, e.g. tunnels. The CTM model uses the traffic conditions in the proximities of the target vehicle and estimates its motion to predict the position where it reappears. The method is validated on an airborne image sequence acquired from a helicopter. Several reference vehicles are tracked within a range of 500m in a complex urban traffic situation. An artificial tracking outage of 240m is simulated, which is handled by the CTM. For this, all the vehicles in the close proximity are automatically detected and tracked to estimate the basic density-flow relations of the CTM model. Finally, the real and simulated trajectories of the reference vehicles in the outage are compared showing good correspondence also in congested traffic situations.

Attentional capture by alcohol-related stimuli may be activated involuntarily by top-down search goals.

PubMed

Brown, Chris R H; Duka, Theodora; Forster, Sophie

2018-04-25

Previous research has found that the attention of social drinkers is preferentially oriented towards alcohol-related stimuli (attentional capture). This is argued to play a role in escalating craving for alcohol that can result in hazardous drinking. According to incentive theories of drug addiction, the stimuli associated with the drug reward acquire learned incentive salience and grab attention. However, it is not clear whether the mechanism by which this bias is created is a voluntary or an automatic one, although some evidence suggests a stimulus-driven mechanism. Here, we test for the first time whether this attentional capture could reflect an involuntary consequence of a goal-driven mechanism. Across three experiments, participants were given search goals to detect either an alcoholic or a non-alcoholic object (target) in a stream of briefly presented objects unrelated to the target. Prior to the target, a task-irrelevant parafoveal distractor appeared. This could either be congruent or incongruent with the current search goal. Applying a meta-analysis, we combined the results across the three experiments and found consistent evidence of goal-driven attentional capture, whereby alcohol distractors impeded target detection when the search goal was for alcohol. By contrast, alcohol distractors did not interfere with target detection, whilst participants were searching for a non-alcoholic category. A separate experiment revealed that the goal-driven capture effect was not found when participants held alcohol features active in memory but did not intentionally search for them. These findings suggest a strong goal-driven account of attentional capture by alcohol cues in social drinkers.

Feature Extraction and Selection Strategies for Automated Target Recognition

NASA Technical Reports Server (NTRS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-01-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Feature extraction and selection strategies for automated target recognition

NASA Astrophysics Data System (ADS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-04-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory regionof- interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Miss-distance indicator for tank main gun systems

NASA Astrophysics Data System (ADS)

Bornstein, Jonathan A.; Hillis, David B.

1994-07-01

The initial development of a passive, automated system to track bullet trajectories near a target to determine the `miss distance,' and the corresponding correction necessary to bring the following round `on target' is discussed. The system consists of a visible wavelength CCD sensor, long focal length optics, and a separate IR sensor to detect the muzzle flash of the firing event; this is coupled to a `PC' based image processing and automatic tracking system designed to follow the projectile trajectory by intelligently comparing frame to frame variation of the projectile tracer image. An error analysis indicates that the device is particularly sensitive to variation of the projectile time of flight to the target, and requires development of algorithms to estimate this value from the 2D images employed by the sensor to monitor the projectile trajectory. Initial results obtained by using a brassboard prototype to track training ammunition are promising.

DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D.

PubMed

Shuvaev, Sergey A; Lazutkin, Alexander A; Kedrov, Alexander V; Anokhin, Konstantin V; Enikolopov, Grigori N; Koulakov, Alexei A

2017-01-01

Current 3D imaging methods, including optical projection tomography, light-sheet microscopy, block-face imaging, and serial two photon tomography enable visualization of large samples of biological tissue. Large volumes of data obtained at high resolution require development of automatic image processing techniques, such as algorithms for automatic cell detection or, more generally, point-like object detection. Current approaches to automated cell detection suffer from difficulties originating from detection of particular cell types, cell populations of different brightness, non-uniformly stained, and overlapping cells. In this study, we present a set of algorithms for robust automatic cell detection in 3D. Our algorithms are suitable for, but not limited to, whole brain regions and individual brain sections. We used watershed procedure to split regional maxima representing overlapping cells. We developed a bootstrap Gaussian fit procedure to evaluate the statistical significance of detected cells. We compared cell detection quality of our algorithm and other software using 42 samples, representing 6 staining and imaging techniques. The results provided by our algorithm matched manual expert quantification with signal-to-noise dependent confidence, including samples with cells of different brightness, non-uniformly stained, and overlapping cells for whole brain regions and individual tissue sections. Our algorithm provided the best cell detection quality among tested free and commercial software.

Testing continuous earthquake detection and location in Alentejo (South Portugal) by waveform coherency analysis

NASA Astrophysics Data System (ADS)

Matos, Catarina; Grigoli, Francesco; Cesca, Simone; Custódio, Susana

2015-04-01

In the last decade a permanent seismic network of 30 broadband stations, complemented by dense temporary deployments, covered Portugal. This extraordinary network coverage enables now the computation of a high-resolution image of the seismicity of Portugal, which in turn will shed light on the seismotectonics of Portugal. The large data volumes available cannot be analyzed by traditional time-consuming manual location procedures. In this presentation we show first results on the automatic detection and location of earthquakes occurred in a selected region in the south of Portugal Our main goal is to implement an automatic earthquake detection and location routine in order to have a tool to quickly process large data sets, while at the same time detecting low magnitude earthquakes (i.e., lowering the detection threshold). We present a modified version of the automatic seismic event location by waveform coherency analysis developed by Grigoli et al. (2013, 2014), designed to perform earthquake detections and locations in continuous data. The event detection is performed by continuously computing the short-term-average/long-term-average of two different characteristic functions (CFs). For the P phases we used a CF based on the vertical energy trace, while for S phases we used a CF based on the maximum eigenvalue of the instantaneous covariance matrix (Vidale 1991). Seismic event detection and location is obtained by performing waveform coherence analysis scanning different hypocentral coordinates. We apply this technique to earthquakes in the Alentejo region (South Portugal), taking advantage from a small aperture seismic network installed in the south of Portugal for two years (2010 - 2011) during the DOCTAR experiment. In addition to the good network coverage, the Alentejo region was chosen for its simple tectonic setting and also because the relationship between seismicity, tectonics and local lithospheric structure is intriguing and still poorly understood. Inside the target area the seismicity clusters mainly within two clouds, oriented SE-NW and SW-NE. Should these clusters be seen as the expression of local active faults? Are they associated to lithological transitions? Or do the locations obtained from the previously sparse permanent network have large errors and generate fake clusters? We present preliminary results from this study, and compare them with manual locations. This work is supported by project QuakeLoc, reference: PTDC/GEO-FIQ/3522/2012

Multi-dimensional position sensor using range detectors

DOEpatents

Vann, Charles S.

2000-01-01

A small, non-contact optical sensor uses ranges and images to detect its relative position to an object in up to six degrees of freedom. The sensor has three light emitting range detectors which illuminate a target and can be used to determine distance and two tilt angles. A camera located between the three range detectors senses the three remaining degrees of freedom, two translations and one rotation. Various range detectors, with different light sources, e.g. lasers and LEDs, different collection options, and different detection schemes, e.g. diminishing return and time of flight can be used. This sensor increases the capability and flexibility of computer controlled machines, e.g. it can instruct a robot how to adjust automatically to different positions and orientations of a part.

Design of "Eye Closure" system for the stealth of photo-electric equipments

NASA Astrophysics Data System (ADS)

Zhang, Y.; Hua, W. S.; Li, G.

2012-10-01

Based on the optical activity of liquid crystal, a new approach for the stealth of "cat's eye" targets is proposed in this paper. It imitates the physiological close reaction of human eyes when strong light irradiates eyes. With this approach, the "cat's eye" effect will vanish, which is applied in restricting photo-electric equipments being detected and located by active laser detection system. The structure and working principle of the design are presented. The drive circuit is given to control the optical switch automatically. Feasibility of this design is demonstrated by experimental method. The measured data illustrate that the proposed approach is effective to eliminate the "cat's eye" effect, so as to enhancing the viability of photo-electric equipments on the battlefield.

OKCARS : Oklahoma Collision Analysis and Response System.

DOT National Transportation Integrated Search

2012-10-01

By continuously monitoring traffic intersections to automatically detect that a collision or nearcollision : has occurred, automatically call for assistance, and automatically forewarn oncoming traffic, : our OKCARS has the capability to effectively ...

Testing & Evaluation of Close-Range SAR for Monitoring & Automatically Detecting Pavement Conditions

DOT National Transportation Integrated Search

2012-01-01

This report summarizes activities in support of the DOT contract on Testing & Evaluating Close-Range SAR for Monitoring & Automatically Detecting Pavement Conditions & Improve Visual Inspection Procedures. The work of this project was performed by Dr...

Automatic Earthquake Detection by Active Learning

NASA Astrophysics Data System (ADS)

Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

Automated Segmentation Methods of Drusen to Diagnose Age-Related Macular Degeneration Screening in Retinal Images.

PubMed

Kim, Young Jae; Kim, Kwang Gi

2018-01-01

Existing drusen measurement is difficult to use in clinic because it requires a lot of time and effort for visual inspection. In order to resolve this problem, we propose an automatic drusen detection method to help clinical diagnosis of age-related macular degeneration. First, we changed the fundus image to a green channel and extracted the ROI of the macular area based on the optic disk. Next, we detected the candidate group using the difference image of the median filter within the ROI. We also segmented vessels and removed them from the image. Finally, we detected the drusen through Renyi's entropy threshold algorithm. We performed comparisons and statistical analysis between the manual detection results and automatic detection results for 30 cases in order to verify validity. As a result, the average sensitivity was 93.37% (80.95%~100%) and the average DSC was 0.73 (0.3~0.98). In addition, the value of the ICC was 0.984 (CI: 0.967~0.993, p < 0.01), showing the high reliability of the proposed automatic method. We expect that the automatic drusen detection helps clinicians to improve the diagnostic performance in the detection of drusen on fundus image.

Automatic voxel-based morphometry of structural MRI by SPM8 plus diffeomorphic anatomic registration through exponentiated lie algebra improves the diagnosis of probable Alzheimer Disease.

PubMed

Matsuda, H; Mizumura, S; Nemoto, K; Yamashita, F; Imabayashi, E; Sato, N; Asada, T

2012-06-01

The necessity for structural MRI is greater than ever to both diagnose AD in its early stage and objectively evaluate its progression. We propose a new VBM-based software program for automatic detection of early specific atrophy in AD. A target VOI was determined by group comparison of 30 patients with very mild AD and 40 age-matched healthy controls by using SPM. Then this target VOI was incorporated into a newly developed automated software program independently running on a Windows PC for VBM by using SPM8 plus DARTEL. ROC analysis was performed for discrimination of 116 other patients with AD with very mild stage (n = 45), mild stage (n = 30) and moderate-to-advanced stages (n = 41) from 40 other age-matched healthy controls by using a z score map in the target VOI. Medial temporal structures involving the entire region of the entorhinal cortex, hippocampus, and amygdala showed significant atrophy in the patients with very mild AD and were determined as a target VOI. When we used the severity score of atrophy in this target VOI, 91.6%, 95.8%, and 98.2% accuracies were obtained in the very mild AD, mild AD, and moderate-to-severe AD groups, respectively. In the very mild AD group, a high specificity of 97.5% with a sensitivity of 86.4% was obtained, and age at onset of AD did not influence this accuracy. This software program with application of SPM8 plus DARTEL to VBM provides a high performance for AD diagnosis by using MRI.

Assisting People with Multiple Disabilities by Improving Their Computer Pointing Efficiency with an Automatic Target Acquisition Program

ERIC Educational Resources Information Center

Shih, Ching-Hsiang; Shih, Ching-Tien; Peng, Chin-Ling

2011-01-01

This study evaluated whether two people with multiple disabilities would be able to improve their pointing performance through an Automatic Target Acquisition Program (ATAP) and a newly developed mouse driver (i.e. a new mouse driver replaces standard mouse driver, and is able to monitor mouse movement and intercept click action). Initially, both…

Virus-Clip: a fast and memory-efficient viral integration site detection tool at single-base resolution with annotation capability.

PubMed

Ho, Daniel W H; Sze, Karen M F; Ng, Irene O L

2015-08-28

Viral integration into the human genome upon infection is an important risk factor for various human malignancies. We developed viral integration site detection tool called Virus-Clip, which makes use of information extracted from soft-clipped sequencing reads to identify exact positions of human and virus breakpoints of integration events. With initial read alignment to virus reference genome and streamlined procedures, Virus-Clip delivers a simple, fast and memory-efficient solution to viral integration site detection. Moreover, it can also automatically annotate the integration events with the corresponding affected human genes. Virus-Clip has been verified using whole-transcriptome sequencing data and its detection was validated to have satisfactory sensitivity and specificity. Marked advancement in performance was detected, compared to existing tools. It is applicable to versatile types of data including whole-genome sequencing, whole-transcriptome sequencing, and targeted sequencing. Virus-Clip is available at http://web.hku.hk/~dwhho/Virus-Clip.zip.

Self-assessing target with automatic feedback

DOEpatents

Larkin, Stephen W.; Kramer, Robert L.

2004-03-02

A self assessing target with four quadrants and a method of use thereof. Each quadrant containing possible causes for why shots are going into that particular quadrant rather than the center mass of the target. Each possible cause is followed by a solution intended to help the marksman correct the problem causing the marksman to shoot in that particular area. In addition, the self assessing target contains possible causes for general shooting errors and solutions to the causes of the general shooting error. The automatic feedback with instant suggestions and corrections enables the shooter to improve their marksmanship.

Supervised non-negative tensor factorization for automatic hyperspectral feature extraction and target discrimination

NASA Astrophysics Data System (ADS)

Anderson, Dylan; Bapst, Aleksander; Coon, Joshua; Pung, Aaron; Kudenov, Michael

2017-05-01

Hyperspectral imaging provides a highly discriminative and powerful signature for target detection and discrimination. Recent literature has shown that considering additional target characteristics, such as spatial or temporal profiles, simultaneously with spectral content can greatly increase classifier performance. Considering these additional characteristics in a traditional discriminative algorithm requires a feature extraction step be performed first. An example of such a pipeline is computing a filter bank response to extract spatial features followed by a support vector machine (SVM) to discriminate between targets. This decoupling between feature extraction and target discrimination yields features that are suboptimal for discrimination, reducing performance. This performance reduction is especially pronounced when the number of features or available data is limited. In this paper, we propose the use of Supervised Nonnegative Tensor Factorization (SNTF) to jointly perform feature extraction and target discrimination over hyperspectral data products. SNTF learns a tensor factorization and a classification boundary from labeled training data simultaneously. This ensures that the features learned via tensor factorization are optimal for both summarizing the input data and separating the targets of interest. Practical considerations for applying SNTF to hyperspectral data are presented, and results from this framework are compared to decoupled feature extraction/target discrimination pipelines.

Automatic segmentation for detecting uterine fibroid regions treated with MR-guided high intensity focused ultrasound (MR-HIFU).

PubMed

Antila, Kari; Nieminen, Heikki J; Sequeiros, Roberto Blanco; Ehnholm, Gösta

2014-07-01

Up to 25% of women suffer from uterine fibroids (UF) that cause infertility, pain, and discomfort. MR-guided high intensity focused ultrasound (MR-HIFU) is an emerging technique for noninvasive, computer-guided thermal ablation of UFs. The volume of induced necrosis is a predictor of the success of the treatment. However, accurate volume assessment by hand can be time consuming, and quick tools produce biased results. Therefore, fast and reliable tools are required in order to estimate the technical treatment outcome during the therapy event so as to predict symptom relief. A novel technique has been developed for the segmentation and volume assessment of the treated region. Conventional algorithms typically require user interaction ora priori knowledge of the target. The developed algorithm exploits the treatment plan, the coordinates of the intended ablation, for fully automatic segmentation with no user input. A good similarity to an expert-segmented manual reference was achieved (Dice similarity coefficient = 0.880 ± 0.074). The average automatic segmentation time was 1.6 ± 0.7 min per patient against an order of tens of minutes when done manually. The results suggest that the segmentation algorithm developed, requiring no user-input, provides a feasible and practical approach for the automatic evaluation of the boundary and volume of the HIFU-treated region.

Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging

NASA Astrophysics Data System (ADS)

Litkey, P.; Nurminen, K.; Honkavaara, E.

2013-05-01

The risks of storms that cause damage in forests are increasing due to climate change. Quickly detecting fallen trees, assessing the amount of fallen trees and efficiently collecting them are of great importance for economic and environmental reasons. Visually detecting and delineating storm damage is a laborious and error-prone process; thus, it is important to develop cost-efficient and highly automated methods. Objective of our research project is to investigate and develop a reliable and efficient method for automatic storm damage detection, which is based on airborne imagery that is collected after a storm. The requirements for the method are the before-storm and after-storm surface models. A difference surface is calculated using two DSMs and the locations where significant changes have appeared are automatically detected. In our previous research we used four-year old airborne laser scanning surface model as the before-storm surface. The after-storm DSM was provided from the photogrammetric images using the Next Generation Automatic Terrain Extraction (NGATE) algorithm of Socet Set software. We obtained 100% accuracy in detection of major storm damages. In this investigation we will further evaluate the sensitivity of the storm-damage detection process. We will investigate the potential of national airborne photography, that is collected at no-leaf season, to automatically produce a before-storm DSM using image matching. We will also compare impact of the terrain extraction algorithm to the results. Our results will also promote the potential of national open source data sets in the management of natural disasters.

Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

NASA Astrophysics Data System (ADS)

Sa, Qila; Wang, Zhihui

2018-03-01

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

Research on Automatic Positioning System of Ultrasonic Testing of Wind Turbine Blade Flaws

NASA Astrophysics Data System (ADS)

Liu, Q. X.; Wang, Z. H.; Long, S. G.; Cai, M.; Cai, M.; Wang, X.; Chen, X. Y.; Bu, J. L.

2017-11-01

Ultrasonic testing technology has been used essentially in non-destructive testing of wind turbine blades. However, it is fact that the ultrasonic flaw detection method has inefficiently employed in recent years. This is because the testing result will illustrate a small deviation due to the artificial, environmental and technical factors. Therefore, it is an urgent technical demand for engineers to test the various flaws efficiently and quickly. An automatic positioning system has been designed in this paper to record the moving coordinates and the target distance in real time. Simultaneously, it could launch and acquire the sonic wave automatically. The ADNS-3080 optoelectronic chip is manufactured by Agilent Technologies Inc, which is also utilized in the system. With the combination of the chip, the power conversion module and the USB transmission module, the collected data can be transmitted from the upper monitor to the hardware that could process and control the data through software programming. An experiment has been designed to prove the reliability of automotive positioning system. The result has been validated by comparing the result collected form LABVIEW and actual plots on Perspex plane, it concludes that the system possesses high accuracy and magnificent meanings in practical engineering.

Precision targeting with a tracking adaptive optics scanning laser ophthalmoscope

NASA Astrophysics Data System (ADS)

Hammer, Daniel X.; Ferguson, R. Daniel; Bigelow, Chad E.; Iftimia, Nicusor V.; Ustun, Teoman E.; Noojin, Gary D.; Stolarski, David J.; Hodnett, Harvey M.; Imholte, Michelle L.; Kumru, Semih S.; McCall, Michelle N.; Toth, Cynthia A.; Rockwell, Benjamin A.

2006-02-01

Precise targeting of retinal structures including retinal pigment epithelial cells, feeder vessels, ganglion cells, photoreceptors, and other cells important for light transduction may enable earlier disease intervention with laser therapies and advanced methods for vision studies. A novel imaging system based upon scanning laser ophthalmoscopy (SLO) with adaptive optics (AO) and active image stabilization was designed, developed, and tested in humans and animals. An additional port allows delivery of aberration-corrected therapeutic/stimulus laser sources. The system design includes simultaneous presentation of non-AO, wide-field (~40 deg) and AO, high-magnification (1-2 deg) retinal scans easily positioned anywhere on the retina in a drag-and-drop manner. The AO optical design achieves an error of <0.45 waves (at 800 nm) over +/-6 deg on the retina. A MEMS-based deformable mirror (Boston Micromachines Inc.) is used for wave-front correction. The third generation retinal tracking system achieves a bandwidth of greater than 1 kHz allowing acquisition of stabilized AO images with an accuracy of ~10 μm. Normal adult human volunteers and animals with previously-placed lesions (cynomolgus monkeys) were tested to optimize the tracking instrumentation and to characterize AO imaging performance. Ultrafast laser pulses were delivered to monkeys to characterize the ability to precisely place lesions and stimulus beams. Other advanced features such as real-time image averaging, automatic highresolution mosaic generation, and automatic blink detection and tracking re-lock were also tested. The system has the potential to become an important tool to clinicians and researchers for early detection and treatment of retinal diseases.

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound.

PubMed

Baumgartner, Christian F; Kamnitsas, Konstantinos; Matthew, Jacqueline; Fletcher, Tara P; Smith, Sandra; Koch, Lisa M; Kainz, Bernhard; Rueckert, Daniel

2017-11-01

Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks, which can automatically detect 13 fetal standard views in freehand 2-D ultrasound data as well as provide a localization of the fetal structures via a bounding box. An important contribution is that the network learns to localize the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localization task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localization on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modeling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localization task.

A Track Initiation Method for the Underwater Target Tracking Environment

NASA Astrophysics Data System (ADS)

Li, Dong-dong; Lin, Yang; Zhang, Yao

2018-04-01

A novel efficient track initiation method is proposed for the harsh underwater target tracking environment (heavy clutter and large measurement errors): track splitting, evaluating, pruning and merging method (TSEPM). Track initiation demands that the method should determine the existence and initial state of a target quickly and correctly. Heavy clutter and large measurement errors certainly pose additional difficulties and challenges, which deteriorate and complicate the track initiation in the harsh underwater target tracking environment. There are three primary shortcomings for the current track initiation methods to initialize a target: (a) they cannot eliminate the turbulences of clutter effectively; (b) there may be a high false alarm probability and low detection probability of a track; (c) they cannot estimate the initial state for a new confirmed track correctly. Based on the multiple hypotheses tracking principle and modified logic-based track initiation method, in order to increase the detection probability of a track, track splitting creates a large number of tracks which include the true track originated from the target. And in order to decrease the false alarm probability, based on the evaluation mechanism, track pruning and track merging are proposed to reduce the false tracks. TSEPM method can deal with the track initiation problems derived from heavy clutter and large measurement errors, determine the target's existence and estimate its initial state with the least squares method. What's more, our method is fully automatic and does not require any kind manual input for initializing and tuning any parameter. Simulation results indicate that our new method improves significantly the performance of the track initiation in the harsh underwater target tracking environment.

Detecting measurement outliers: remeasure efficiently

NASA Astrophysics Data System (ADS)

Ullrich, Albrecht

2010-09-01

Shrinking structures, advanced optical proximity correction (OPC) and complex measurement strategies continually challenge critical dimension (CD) metrology tools and recipe creation processes. One important quality ensuring task is the control of measurement outlier behavior. Outliers could trigger false positive alarm for specification violations impacting cycle time or potentially yield. Constant high level of outliers not only deteriorates cycle time but also puts unnecessary stress on tool operators leading eventually to human errors. At tool level the sources of outliers are natural variations (e.g. beam current etc.), drifts, contrast conditions, focus determination or pattern recognition issues, etc. Some of these can result from suboptimal or even wrong recipe settings, like focus position or measurement box size. Such outliers, created by an automatic recipe creation process faced with more complicated structures, would manifest itself rather as systematic variation of measurements than the one caused by 'pure' tool variation. I analyzed several statistical methods to detect outliers. These range from classical outlier tests for extrema, robust metrics like interquartile range (IQR) to methods evaluating the distribution of different populations of measurement sites, like the Cochran test. The latter suits especially the detection of systematic effects. The next level of outlier detection entwines additional information about the mask and the manufacturing process with the measurement results. The methods were reviewed for measured variations assumed to be normally distributed with zero mean but also for the presence of a statistically significant spatial process signature. I arrive at the conclusion that intelligent outlier detection can influence the efficiency and cycle time of CD metrology greatly. In combination with process information like target, typical platform variation and signature, one can tailor the detection to the needs of the photomask at hand. By monitoring the outlier behavior carefully, weaknesses of the automatic recipe creation process can be spotted.

Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation.

PubMed

Daisne, Jean-François; Blumhofer, Andreas

2013-06-26

Intensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions. The updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for "manual to automatic" and "manual to corrected" volumes comparisons. In both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors. The updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert.

Rheticus Displacement: an Automatic Geo-Information Service Platform for Ground Instabilities Detection and Monitoring

NASA Astrophysics Data System (ADS)

Chiaradia, M. T.; Samarelli, S.; Agrimano, L.; Lorusso, A. P.; Nutricato, R.; Nitti, D. O.; Morea, A.; Tijani, K.

2016-12-01

Rheticus® is an innovative cloud-based data and services hub able to deliver Earth Observation added-value products through automatic complex processes and a minimum interaction with human operators. This target is achieved by means of programmable components working as different software layers in a modern enterprise system which relies on SOA (service-oriented-architecture) model. Due to its architecture, where every functionality is well defined and encapsulated in a standalone component, Rheticus is potentially highly scalable and distributable allowing different configurations depending on the user needs. Rheticus offers a portfolio of services, ranging from the detection and monitoring of geohazards and infrastructural instabilities, to marine water quality monitoring, wildfires detection or land cover monitoring. In this work, we outline the overall cloud-based platform and focus on the "Rheticus Displacement" service, aimed at providing accurate information to monitor movements occurring across landslide features or structural instabilities that could affect buildings or infrastructures. Using Sentinel-1 (S1) open data images and Multi-Temporal SAR Interferometry techniques (i.e., SPINUA), the service is complementary to traditional survey methods, providing a long-term solution to slope instability monitoring. Rheticus automatically browses and accesses (on a weekly basis) the products of the rolling archive of ESA S1 Scientific Data Hub; S1 data are then handled by a mature running processing chain, which is responsible of producing displacement maps immediately usable to measure with sub-centimetric precision movements of coherent points. Examples are provided, concerning the automatic displacement map generation process, as well as the integration of point and distributed scatterers, the integration of multi-sensors displacement maps (e.g., Sentinel-1 IW and COSMO-SkyMed HIMAGE), the combination of displacement rate maps acquired along both ascending and descending passes. ACK: Study carried out in the framework of the FAST4MAP project and co-funded by the Italian Space Agency (Contract n. 2015-020-R.0). Sentinel-1A products provided by ESA. CSK® Products, ASI, provided by ASI under a license to use. Rheticus® is a registered trademark of Planetek Italia srl.

Curiosity Finds Hydrogen-Rich Area of Mars Subsurface

NASA Image and Video Library

2015-08-19

Curiosity's Russian-made instrument for checking hydration levels in the ground beneath the rover detected an unusually high amount at a site near "Marias Pass," prompting repeated passes over the area to map the hydrogen amounts. The instrument is named Dynamic Albedo of Neutrons, or DAN. It detects hydrogen by the effect of hydrogen atoms on neutrons entering the ground either from cosmic rays and Curiosity's power source (DAN's passive mode) or from the instrument's neutron pulse generator (DAN's active mode). DAN recognizes which neutrons have bounced off hydrogen from their rerduced energy level. This map, covering an area about 130 feet (40 meters) across, shows results from DAN's multiple traverses over the area, with color coding for levels of hydrogen detected. The red coding indicates amounts of hydrogen three to four times as high as the amounts detected anywhere previously along Curiosity's traverse of about 6.9 miles (11.1 kilometers) since landing in August 2012. The inset map at lower right shows the full traverse through Sol 1051 (July 21, 2015), with names assigned to rectangles within Gale Crater for geological mapping purposes. The vertical bar at left indicates the color coding according to counts per second in DAN's passive mode. The hydrogen detected by DAN is interpreted as water molecules or hydroxyl ions bound within minerals or water absorbed onto minerals in the rocks and soil, to a depth of about 3 feet (1 meter) beneath the rover. The amount of hydrogen is often expressed as "water equivalent hydrogen" based on two hydrogen atoms per molecule of water. In the same area where DAN detected an unusually high amount of hydration, Curiosity's Chemistry and Camera (ChemCam) instrument detected an unusually high amount of silica in several rock targets. The DAN and ChemCam findings led to the rover's science team choosing a rock target called "Buckskin" for collection of a drilled sample to be analyzed by the rover's internal laboratory instruments. Russia's Space Research Institute developed DAN in close cooperation with the N.L. Dukhov All-Russia Research Institute of Automatics, Moscow, and the Joint Institute for Nuclear Research, Dubna. The neutron generator development was supervised by the late technical designer German A. Smirnov of the All-Russia Institute of Automatics. Moscow. http://photojournal.jpl.nasa.gov/catalog/PIA19809

SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

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

Qiu, J; Yang, D

2015-06-15

Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets,more » and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from Varian Medical System.« less

VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls.

PubMed

Kim, Byoungjip; Kang, Seungwoo; Ha, Jin-Young; Song, Junehwa

2015-07-16

In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user's place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense.

Pattern recognition for passive polarimetric data using nonparametric classifiers

NASA Astrophysics Data System (ADS)

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

2005-08-01

Passive polarization based imaging is a useful tool in computer vision and pattern recognition. A passive polarization imaging system forms a polarimetric image from the reflection of ambient light that contains useful information for computer vision tasks such as object detection (classification) and recognition. Applications of polarization based pattern recognition include material classification and automatic shape recognition. In this paper, we present two target detection algorithms for images captured by a passive polarimetric imaging system. The proposed detection algorithms are based on Bayesian decision theory. In these approaches, an object can belong to one of any given number classes and classification involves making decisions that minimize the average probability of making incorrect decisions. This minimum is achieved by assigning an object to the class that maximizes the a posteriori probability. Computing a posteriori probabilities requires estimates of class conditional probability density functions (likelihoods) and prior probabilities. A Probabilistic neural network (PNN), which is a nonparametric method that can compute Bayes optimal boundaries, and a -nearest neighbor (KNN) classifier, is used for density estimation and classification. The proposed algorithms are applied to polarimetric image data gathered in the laboratory with a liquid crystal-based system. The experimental results validate the effectiveness of the above algorithms for target detection from polarimetric data.

Chromatic biosensor for detection of phosphinothricin acetyltransferase by use of polydiacetylene vesicles encapsulated within automatically generated immunohydrogel beads.

PubMed

Jung, Sung-Ho; Jang, Huisoo; Lim, Min-Cheol; Kim, Jae-Hwan; Shin, Kong-Sik; Kim, Sun Min; Kim, Hae-Yeong; Kim, Young-Rok; Jeon, Tae-Joon

2015-02-17

We developed a simple and sensitive colorimetric biosensor in the form of microparticles by using polydiacetylene (PDA) vesicles encapsulated within a hydrogel matrix for the detection of phosphinothricin acetyltransferase (PAT) protein, which is one of the most important marker proteins in genetically modified (GM) crops. Although PDA is commonly used as a sensing material due to its unique colorimetric properties, existing PDA biosensors are ineffective due to their low sensitivity as well as their lack of robustness. To overcome these disadvantages, we devised immunohydrogel beads made of anti-PAT-conjugated PDA vesicles embedded at high density within a poly(ethylene glycol) diacrylate (PEG-DA) hydrogel matrix. In addition, the construction of immunohydrogel beads was automated by use of a microfluidic device. In the immunoreaction, the sensitivity of antibody-conjugated PDA vesicles was significantly amplified, as monitored by the unaided eye. The limit of detection for target molecules reached as low as 20 nM, which is sufficiently low enough to detect target materials in GM organisms. Collectively, the results show that immunohydrogel beads constitute a promising colorimetric sensing platform for onsite testing in a number of fields, such as the food and medical industries, as well as warfare situations.

Design criteria for a high energy Compton Camera and possible application to targeted cancer therapy

NASA Astrophysics Data System (ADS)

Conka Nurdan, T.; Nurdan, K.; Brill, A. B.; Walenta, A. H.

2015-07-01

The proposed research focuses on the design criteria for a Compton Camera with high spatial resolution and sensitivity, operating at high gamma energies and its possible application for molecular imaging. This application is mainly on the detection and visualization of the pharmacokinetics of tumor targeting substances specific for particular cancer sites. Expected high resolution (< 0.5 mm) permits monitoring the pharmacokinetics of labeled gene constructs in vivo in small animals with a human tumor xenograft which is one of the first steps in evaluating the potential utility of a candidate gene. The additional benefit of high sensitivity detection will be improved cancer treatment strategies in patients based on the use of specific molecules binding to cancer sites for early detection of tumors and identifying metastasis, monitoring drug delivery and radionuclide therapy for optimum cell killing at the tumor site. This new technology can provide high resolution, high sensitivity imaging of a wide range of gamma energies and will significantly extend the range of radiotracers that can be investigated and used clinically. The small and compact construction of the proposed camera system allows flexible application which will be particularly useful for monitoring residual tumor around the resection site during surgery. It is also envisaged as able to test the performance of new drug/gene-based therapies in vitro and in vivo for tumor targeting efficacy using automatic large scale screening methods.

Automatic-repeat-request error control schemes

NASA Technical Reports Server (NTRS)

Lin, S.; Costello, D. J., Jr.; Miller, M. J.

1983-01-01

Error detection incorporated with automatic-repeat-request (ARQ) is widely used for error control in data communication systems. This method of error control is simple and provides high system reliability. If a properly chosen code is used for error detection, virtually error-free data transmission can be attained. Various types of ARQ and hybrid ARQ schemes, and error detection using linear block codes are surveyed.

Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods.

PubMed

Kong, Xiangyi; Gong, Shun; Su, Lijuan; Howard, Newton; Kong, Yanguo

2018-01-01

Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Automatic Identification of Critical Data Items in a Database to Mitigate the Effects of Malicious Insiders

NASA Astrophysics Data System (ADS)

White, Jonathan; Panda, Brajendra

A major concern for computer system security is the threat from malicious insiders who target and abuse critical data items in the system. In this paper, we propose a solution to enable automatic identification of critical data items in a database by way of data dependency relationships. This identification of critical data items is necessary because insider threats often target mission critical data in order to accomplish malicious tasks. Unfortunately, currently available systems fail to address this problem in a comprehensive manner. It is more difficult for non-experts to identify these critical data items because of their lack of familiarity and due to the fact that data systems are constantly changing. By identifying the critical data items automatically, security engineers will be better prepared to protect what is critical to the mission of the organization and also have the ability to focus their security efforts on these critical data items. We have developed an algorithm that scans the database logs and forms a directed graph showing which items influence a large number of other items and at what frequency this influence occurs. This graph is traversed to reveal the data items which have a large influence throughout the database system by using a novel metric based formula. These items are critical to the system because if they are maliciously altered or stolen, the malicious alterations will spread throughout the system, delaying recovery and causing a much more malignant effect. As these items have significant influence, they are deemed to be critical and worthy of extra security measures. Our proposal is not intended to replace existing intrusion detection systems, but rather is intended to complement current and future technologies. Our proposal has never been performed before, and our experimental results have shown that it is very effective in revealing critical data items automatically.

Investigation of an automatic trim algorithm for restructurable aircraft control

NASA Technical Reports Server (NTRS)

Weiss, J.; Eterno, J.; Grunberg, D.; Looze, D.; Ostroff, A.

1986-01-01

This paper develops and solves an automatic trim problem for restructurable aircraft control. The trim solution is applied as a feed-forward control to reject measurable disturbances following control element failures. Disturbance rejection and command following performances are recovered through the automatic feedback control redesign procedure described by Looze et al. (1985). For this project the existence of a failure detection mechanism is assumed, and methods to cope with potential detection and identification inaccuracies are addressed.

Multiwaveband simulation-based signature analysis of camouflaged human dismounts in cluttered environments with TAIThermIR and MuSES

NASA Astrophysics Data System (ADS)

Packard, Corey D.; Klein, Mark D.; Viola, Timothy S.; Hepokoski, Mark A.

2016-10-01

The ability to predict electro-optical (EO) signatures of diverse targets against cluttered backgrounds is paramount for signature evaluation and/or management. Knowledge of target and background signatures is essential for a variety of defense-related applications. While there is no substitute for measured target and background signatures to determine contrast and detection probability, the capability to simulate any mission scenario with desired environmental conditions is a tremendous asset for defense agencies. In this paper, a systematic process for the thermal and visible-through-infrared simulation of camouflaged human dismounts in cluttered outdoor environments is presented. This process, utilizing the thermal and EO/IR radiance simulation tool TAIThermIR (and MuSES), provides a repeatable and accurate approach for analyzing contrast, signature and detectability of humans in multiple wavebands. The engineering workflow required to combine natural weather boundary conditions and the human thermoregulatory module developed by ThermoAnalytics is summarized. The procedure includes human geometry creation, human segmental physiology description and transient physical temperature prediction using environmental boundary conditions and active thermoregulation. Radiance renderings, which use Sandford-Robertson BRDF optical surface property descriptions and are coupled with MODTRAN for the calculation of atmospheric effects, are demonstrated. Sensor effects such as optical blurring and photon noise can be optionally included, increasing the accuracy of detection probability outputs that accompany each rendering. This virtual evaluation procedure has been extensively validated and provides a flexible evaluation process that minimizes the difficulties inherent in human-subject field testing. Defense applications such as detection probability assessment, camouflage pattern evaluation, conspicuity tests and automatic target recognition are discussed.

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.

Chemometric strategy for automatic chromatographic peak detection and background drift correction in chromatographic data.

PubMed

Yu, Yong-Jie; Xia, Qiao-Ling; Wang, Sheng; Wang, Bing; Xie, Fu-Wei; Zhang, Xiao-Bing; Ma, Yun-Ming; Wu, Hai-Long

2014-09-12

Peak detection and background drift correction (BDC) are the key stages in using chemometric methods to analyze chromatographic fingerprints of complex samples. This study developed a novel chemometric strategy for simultaneous automatic chromatographic peak detection and BDC. A robust statistical method was used for intelligent estimation of instrumental noise level coupled with first-order derivative of chromatographic signal to automatically extract chromatographic peaks in the data. A local curve-fitting strategy was then employed for BDC. Simulated and real liquid chromatographic data were designed with various kinds of background drift and degree of overlapped chromatographic peaks to verify the performance of the proposed strategy. The underlying chromatographic peaks can be automatically detected and reasonably integrated by this strategy. Meanwhile, chromatograms with BDC can be precisely obtained. The proposed method was used to analyze a complex gas chromatography dataset that monitored quality changes in plant extracts during storage procedure. Copyright © 2014 Elsevier B.V. All rights reserved.

Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

PubMed

Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

2013-11-01

The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

Detecting Cheaters without Thinking: Testing the Automaticity of the Cheater Detection Module

PubMed Central

Van Lier, Jens; Revlin, Russell; De Neys, Wim

2013-01-01

Evolutionary psychologists have suggested that our brain is composed of evolved mechanisms. One extensively studied mechanism is the cheater detection module. This module would make people very good at detecting cheaters in a social exchange. A vast amount of research has illustrated performance facilitation on social contract selection tasks. This facilitation is attributed to the alleged automatic and isolated operation of the module (i.e., independent of general cognitive capacity). This study, using the selection task, tested the critical automaticity assumption in three experiments. Experiments 1 and 2 established that performance on social contract versions did not depend on cognitive capacity or age. Experiment 3 showed that experimentally burdening cognitive resources with a secondary task had no impact on performance on the social contract version. However, in all experiments, performance on a non-social contract version did depend on available cognitive capacity. Overall, findings validate the automatic and effortless nature of social exchange reasoning. PMID:23342012

[Advances in automatic detection technology for images of thin blood film of malaria parasite].

PubMed

Juan-Sheng, Zhang; Di-Qiang, Zhang; Wei, Wang; Xiao-Guang, Wei; Zeng-Guo, Wang

2017-05-05

This paper reviews the computer vision and image analysis studies aiming at automated diagnosis or screening of malaria in microscope images of thin blood film smears. On the basis of introducing the background and significance of automatic detection technology, the existing detection technologies are summarized and divided into several steps, including image acquisition, pre-processing, morphological analysis, segmentation, count, and pattern classification components. Then, the principles and implementation methods of each step are given in detail. In addition, the promotion and application in automatic detection technology of thick blood film smears are put forwarded as questions worthy of study, and a perspective of the future work for realization of automated microscopy diagnosis of malaria is provided.

Corner detection and sorting method based on improved Harris algorithm in camera calibration

NASA Astrophysics Data System (ADS)

Xiao, Ying; Wang, Yonghong; Dan, Xizuo; Huang, Anqi; Hu, Yue; Yang, Lianxiang

2016-11-01

In traditional Harris corner detection algorithm, the appropriate threshold which is used to eliminate false corners is selected manually. In order to detect corners automatically, an improved algorithm which combines Harris and circular boundary theory of corners is proposed in this paper. After detecting accurate corner coordinates by using Harris algorithm and Forstner algorithm, false corners within chessboard pattern of the calibration plate can be eliminated automatically by using circular boundary theory. Moreover, a corner sorting method based on an improved calibration plate is proposed to eliminate false background corners and sort remaining corners in order. Experiment results show that the proposed algorithms can eliminate all false corners and sort remaining corners correctly and automatically.

Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System

PubMed Central

Jung, Jaehoon; Yoon, Inhye; Paik, Joonki

2016-01-01

This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i) automatic camera calibration using both moving objects and a background structure; (ii) object depth estimation; and (iii) detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB) camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems. PMID:27347978

Automatic target recognition and detection in infrared imagery under cluttered background

NASA Astrophysics Data System (ADS)

Gundogdu, Erhan; Koç, Aykut; Alatan, A. Aydın.

2017-10-01

Visual object classification has long been studied in visible spectrum by utilizing conventional cameras. Since the labeled images has recently increased in number, it is possible to train deep Convolutional Neural Networks (CNN) with significant amount of parameters. As the infrared (IR) sensor technology has been improved during the last two decades, labeled images extracted from IR sensors have been started to be used for object detection and recognition tasks. We address the problem of infrared object recognition and detection by exploiting 15K images from the real-field with long-wave and mid-wave IR sensors. For feature learning, a stacked denoising autoencoder is trained in this IR dataset. To recognize the objects, the trained stacked denoising autoencoder is fine-tuned according to the binary classification loss of the target object. Once the training is completed, the test samples are propagated over the network, and the probability of the test sample belonging to a class is computed. Moreover, the trained classifier is utilized in a detect-by-classification method, where the classification is performed in a set of candidate object boxes and the maximum confidence score in a particular location is accepted as the score of the detected object. To decrease the computational complexity, the detection step at every frame is avoided by running an efficient correlation filter based tracker. The detection part is performed when the tracker confidence is below a pre-defined threshold. The experiments conducted on the real field images demonstrate that the proposed detection and tracking framework presents satisfactory results for detecting tanks under cluttered background.

Model-based vision using geometric hashing

NASA Astrophysics Data System (ADS)

Akerman, Alexander, III; Patton, Ronald

1991-04-01

The Geometric Hashing technique developed by the NYU Courant Institute has been applied to various automatic target recognition applications. In particular, I-MATH has extended the hashing algorithm to perform automatic target recognition ofsynthetic aperture radar (SAR) imagery. For this application, the hashing is performed upon the geometric locations of dominant scatterers. In addition to being a robust model-based matching algorithm -- invariant under translation, scale, and 3D rotations of the target -- hashing is of particular utility because it can still perform effective matching when the target is partially obscured. Moreover, hashing is very amenable to a SIMD parallel processing architecture, and thus potentially realtime implementable.

Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images.

PubMed

Kimori, Yoshitaka; Baba, Norio; Morone, Nobuhiro

2010-07-08

A reliable extraction technique for resolving multiple spots in light or electron microscopic images is essential in investigations of the spatial distribution and dynamics of specific proteins inside cells and tissues. Currently, automatic spot extraction and characterization in complex microscopic images poses many challenges to conventional image processing methods. A new method to extract closely located, small target spots from biological images is proposed. This method starts with a simple but practical operation based on the extended morphological top-hat transformation to subtract an uneven background. The core of our novel approach is the following: first, the original image is rotated in an arbitrary direction and each rotated image is opened with a single straight line-segment structuring element. Second, the opened images are unified and then subtracted from the original image. To evaluate these procedures, model images of simulated spots with closely located targets were created and the efficacy of our method was compared to that of conventional morphological filtering methods. The results showed the better performance of our method. The spots of real microscope images can be quantified to confirm that the method is applicable in a given practice. Our method achieved effective spot extraction under various image conditions, including aggregated target spots, poor signal-to-noise ratio, and large variations in the background intensity. Furthermore, it has no restrictions with respect to the shape of the extracted spots. The features of our method allow its broad application in biological and biomedical image information analysis.

Day, night and all-weather security surveillance automation synergy from combining two powerful technologies

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

Morellas, Vassilios; Johnson, Andrew; Johnston, Chris

2006-07-01

Thermal imaging is rightfully a real-world technology proven to bring confidence to daytime, night-time and all weather security surveillance. Automatic image processing intrusion detection algorithms are also a real world technology proven to bring confidence to system surveillance security solutions. Together, day, night and all weather video imagery sensors and automated intrusion detection software systems create the real power to protect early against crime, providing real-time global homeland protection, rather than simply being able to monitor and record activities for post event analysis. These solutions, whether providing automatic security system surveillance at airports (to automatically detect unauthorized aircraft takeoff andmore » landing activities) or at high risk private, public or government facilities (to automatically detect unauthorized people or vehicle intrusion activities) are on the move to provide end users the power to protect people, capital equipment and intellectual property against acts of vandalism and terrorism. As with any technology, infrared sensors and automatic image intrusion detection systems for global homeland security protection have clear technological strengths and limitations compared to other more common day and night vision technologies or more traditional manual man-in-the-loop intrusion detection security systems. This paper addresses these strength and limitation capabilities. False Alarm (FAR) and False Positive Rate (FPR) is an example of some of the key customer system acceptability metrics and Noise Equivalent Temperature Difference (NETD) and Minimum Resolvable Temperature are examples of some of the sensor level performance acceptability metrics. (authors)« less

A semi-automatic framework of measuring pulmonary arterial metrics at anatomic airway locations using CT imaging

NASA Astrophysics Data System (ADS)

Jin, Dakai; Guo, Junfeng; Dougherty, Timothy M.; Iyer, Krishna S.; Hoffman, Eric A.; Saha, Punam K.

2016-03-01

Pulmonary vascular dysfunction has been implicated in smoking-related susceptibility to emphysema. With the growing interest in characterizing arterial morphology for early evaluation of the vascular role in pulmonary diseases, there is an increasing need for the standardization of a framework for arterial morphological assessment at airway segmental levels. In this paper, we present an effective and robust semi-automatic framework to segment pulmonary arteries at different anatomic airway branches and measure their cross-sectional area (CSA). The method starts with user-specified endpoints of a target arterial segment through a custom-built graphical user interface. It then automatically detect the centerline joining the endpoints, determines the local structure orientation and computes the CSA along the centerline after filtering out the adjacent pulmonary structures, such as veins or airway walls. Several new techniques are presented, including collision-impact based cost function for centerline detection, radial sample-line based CSA computation, and outlier analysis of radial distance to subtract adjacent neighboring structures in the CSA measurement. The method was applied to repeat-scan pulmonary multirow detector CT (MDCT) images from ten healthy subjects (age: 21-48 Yrs, mean: 28.5 Yrs; 7 female) at functional residual capacity (FRC). The reproducibility of computed arterial CSA from four airway segmental regions in middle and lower lobes was analyzed. The overall repeat-scan intra-class correlation (ICC) of the computed CSA from all four airway regions in ten subjects was 96% with maximum ICC found at LB10 and RB4 regions.

Automatic enforcement and highway safety.

DOT National Transportation Integrated Search

2011-05-01

The objectives of this research are to: 1. Identify aspects of the automatic detection of red light running that the public finds offensive or problematical, and quantify the level of opposition on each aspect. 2. Identify aspects of the automatic de...

Automatic detection of electric power troubles (AI application)

NASA Technical Reports Server (NTRS)

Wang, Caroline; Zeanah, Hugh; Anderson, Audie; Patrick, Clint

1987-01-01

The design goals for the Automatic Detection of Electric Power Troubles (ADEPT) were to enhance Fault Diagnosis Techniques in a very efficient way. ADEPT system was designed in two modes of operation: (1) Real time fault isolation, and (2) a local simulator which simulates the models theoretically.

Automatic food detection in egocentric images using artificial intelligence technology

USDA-ARS?s Scientific Manuscript database

Our objective was to develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable devic...

Identifying explosives by dielectric properties obtained through wide-band millimeter-wave illumination

NASA Astrophysics Data System (ADS)

Weatherall, James C.; Barber, Jeffrey; Smith, Barry T.

2015-05-01

A method for extracting dielectric constant from free-space 18 - 40 GHz millimeter-wave reflection data is demonstrated. The reflection coefficient is a function of frequency because of propagation effects, and numerically fitting data to a theoretical model based on geometric optics gives a solution for the complex dielectric constant and target thickness. The discriminative value is illustrated with inert substances and military sheet explosive. In principle, the measurement of reflectivity across multiple frequencies can be incorporated into Advanced Imaging Technology (AIT) systems to automatically identify the composition of anomalies detected on persons at screening checkpoints.

A Real-Time Knowledge Based Expert System For Diagnostic Problem Solving

NASA Astrophysics Data System (ADS)

Esteva, Juan C.; Reynolds, Robert G.

1986-03-01

This paper is a preliminary report of a real-time expert system which is concerned with the detection and diagnosis of electrical deviations in on-board vehicle-based electrical systems. The target systems are being tested at radio frequencies to evaluate their capability to be operated at designed levels of efficiency in an electromagnetic environment. The measurement of this capability is known as ElectroMagnetic Compatibility (EMC). The Intelligent Deviation Diagnosis (IDD) system consists of two basic modules the Automatic Data Acquisition Module (ADAM) and the Diagnosis System (DS). In this paper only the diagnosis system is described.

Nonlinear matching measure for the analysis of on-off type DNA microarray images

NASA Astrophysics Data System (ADS)

Kim, Jong D.; Park, Misun; Kim, Jongwon

2003-07-01

In this paper, we propose a new nonlinear matching measure for automatic analysis of the on-off type DNA microarray images in which the hybridized spots are detected by the template matching method. The targeting spots of HPV DNA chips are designed for genotyping the human papilloma virus(HPV). The proposed measure is obtained by binarythresholding over the whole template region and taking the number of white pixels inside the spotted area. This measure is evaluated in terms of the accuracy of the estimated marker location to show better performance than the normalized covariance.

Comprehensive eye evaluation algorithm

NASA Astrophysics Data System (ADS)

Agurto, C.; Nemeth, S.; Zamora, G.; Vahtel, M.; Soliz, P.; Barriga, S.

2016-03-01

In recent years, several research groups have developed automatic algorithms to detect diabetic retinopathy (DR) in individuals with diabetes (DM), using digital retinal images. Studies have indicated that diabetics have 1.5 times the annual risk of developing primary open angle glaucoma (POAG) as do people without DM. Moreover, DM patients have 1.8 times the risk for age-related macular degeneration (AMD). Although numerous investigators are developing automatic DR detection algorithms, there have been few successful efforts to create an automatic algorithm that can detect other ocular diseases, such as POAG and AMD. Consequently, our aim in the current study was to develop a comprehensive eye evaluation algorithm that not only detects DR in retinal images, but also automatically identifies glaucoma suspects and AMD by integrating other personal medical information with the retinal features. The proposed system is fully automatic and provides the likelihood of each of the three eye disease. The system was evaluated in two datasets of 104 and 88 diabetic cases. For each eye, we used two non-mydriatic digital color fundus photographs (macula and optic disc centered) and, when available, information about age, duration of diabetes, cataracts, hypertension, gender, and laboratory data. Our results show that the combination of multimodal features can increase the AUC by up to 5%, 7%, and 8% in the detection of AMD, DR, and glaucoma respectively. Marked improvement was achieved when laboratory results were combined with retinal image features.

Automatically Detect and Track Multiple Fish Swimming in Shallow Water with Frequent Occlusion

PubMed Central

Qian, Zhi-Ming; Cheng, Xi En; Chen, Yan Qiu

2014-01-01

Due to its universality, swarm behavior in nature attracts much attention of scientists from many fields. Fish schools are examples of biological communities that demonstrate swarm behavior. The detection and tracking of fish in a school are of important significance for the quantitative research on swarm behavior. However, different from other biological communities, there are three problems in the detection and tracking of fish school, that is, variable appearances, complex motion and frequent occlusion. To solve these problems, we propose an effective method of fish detection and tracking. In this method, first, the fish head region is positioned through extremum detection and ellipse fitting; second, The Kalman filtering and feature matching are used to track the target in complex motion; finally, according to the feature information obtained by the detection and tracking, the tracking problems caused by frequent occlusion are processed through trajectory linking. We apply this method to track swimming fish school of different densities. The experimental results show that the proposed method is both accurate and reliable. PMID:25207811

Use of target-date funds in 401(k) plans, 2007.

PubMed

Copeland, Craig

2009-03-01

WHAT THEY ARE: Target-date funds (also called "life-cycle" funds) are a type of mutual fund that automatically rebalances its asset allocation following a predetermined pattern over time. They typically rebalance to more conservative and income-producing assets as the participant's target date of retirement approaches. WHY THEY'RE IMPORTANT AND GROWING: Of the 401(k) plan participants in the EBRI/ICI 401(k) database who were found to be in plans that offeredtarget-date funds, 37 percent had at least some fraction of their account in target-date funds in 2007. Target-date funds held about 7 percent of total assets in 401(k) plans and the use of these funds is expected to increase in the future. The Pension Protection Act of 2006 made it easier for plan sponsors to automatically enroll new workers in a 401(k) plan, and target-date funds were one of the types of approved funds specified for a "default" investment if the participant does not elect a choice. BRI/ICI 401(K) DATABASE: This study uses the unique richness of the data in the EBRI/ICI Participant-Directed Retirement Plan Data Collection Project, which has almost 22 million participants, to examine the choices and characteristics of participants whose plans offer target-date funds. EFFECT OF AGE, SALARY, JOB TENURE, AND ACCOUNT BALANCE: Younger workers are significantly more likely to invest in target-date funds than are older workers: Almost 44 percent of participants under age 30 had assets in a target-date fund, compared with 27 percent of those 60 or older. Target-date funds appeal to those with lower incomes, little time on the job, and with few assets. On average, target-date fund investors are about 2.5 years younger than those who do not invest in target-date funds, have about 3.5 years less tenure, make about $11,000 less in salary, have $25,000 less in their account, and are in smaller plans. EFFECT OF AUTOMATIC ENROLLMENT: While the EBRI/ICI database does not contain specific information on whether a 401(k) plan had automatic enrollment, this analysis was able to proxy for those who could be identified as automatically enrolled. The data show that workers who were considered to be automatically enrolled in their employer's 401(k) plan are significantly more likely to invest all their assets in a target-date fund than those who voluntarily joined, and were also less likely to have extreme all-or-nothing asset allocations to equities. EQUITY ALLOCATIONS AND FUND FAMILIES: One of the major questions surrounding target-date funds is the equity allocations that these funds use over time (the so-called "glide path") as a participant's retirement target date approaches. The glide paths of different target-date funds have significantly different shapes and starting/ending equity allocations. As of 2007, the equity allocation ranges from about 80-90 percent for 2040 funds (for workers about 30 years away from retirement), and from 26-66 percent for 2010 funds (for workers one year away from retirement)--a 40 percentage-point difference. Moreover, the fund families change their relative rank in equity allocation within the different fund years. This analysis finds that the relative rank of the equity allocation within a target-date fund does not appear to affect the percentage of participants investing all their account into that fund. Nevertheless, investors in specific fund families are more likely to invest all their assets in a single target-date fund from that family.

Search strategies

NASA Astrophysics Data System (ADS)

Oliver, B. M.

Attention is given to the approaches which would provide the greatest chance of success in attempts related to the discovery of extraterrestrial advanced cultures in the Galaxy, taking into account the principle of least energy expenditure. The energetics of interstellar contact are explored, giving attention to the use of manned spacecraft, automatic probes, and beacons. The least expensive approach to a search for other civilizations involves a listening program which attempts to detect signals emitted by such civilizations. The optimum part of the spectrum for the considered search is found to be in the range from 1 to 2 GHz. Antenna and transmission formulas are discussed along with the employment of matched gates and filters, the probable characteristics of the signals to be detected, the filter-signal mismatch loss, surveys of the radio sky, the conduction of targeted searches.

Localization accuracy from automatic and semi-automatic rigid registration of locally-advanced lung cancer targets during image-guided radiation therapy

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

Robertson, Scott P.; Weiss, Elisabeth; Hugo, Geoffrey D.

2012-01-15

Purpose: To evaluate localization accuracy resulting from rigid registration of locally-advanced lung cancer targets using fully automatic and semi-automatic protocols for image-guided radiation therapy. Methods: Seventeen lung cancer patients, fourteen also presenting with involved lymph nodes, received computed tomography (CT) scans once per week throughout treatment under active breathing control. A physician contoured both lung and lymph node targets for all weekly scans. Various automatic and semi-automatic rigid registration techniques were then performed for both individual and simultaneous alignments of the primary gross tumor volume (GTV{sub P}) and involved lymph nodes (GTV{sub LN}) to simulate the localization process in image-guidedmore » radiation therapy. Techniques included ''standard'' (direct registration of weekly images to a planning CT), ''seeded'' (manual prealignment of targets to guide standard registration), ''transitive-based'' (alignment of pretreatment and planning CTs through one or more intermediate images), and ''rereferenced'' (designation of a new reference image for registration). Localization error (LE) was assessed as the residual centroid and border distances between targets from planning and weekly CTs after registration. Results: Initial bony alignment resulted in centroid LE of 7.3 {+-} 5.4 mm and 5.4 {+-} 3.4 mm for the GTV{sub P} and GTV{sub LN}, respectively. Compared to bony alignment, transitive-based and seeded registrations significantly reduced GTV{sub P} centroid LE to 4.7 {+-} 3.7 mm (p = 0.011) and 4.3 {+-} 2.5 mm (p < 1 x 10{sup -3}), respectively, but the smallest GTV{sub P} LE of 2.4 {+-} 2.1 mm was provided by rereferenced registration (p < 1 x 10{sup -6}). Standard registration significantly reduced GTV{sub LN} centroid LE to 3.2 {+-} 2.5 mm (p < 1 x 10{sup -3}) compared to bony alignment, with little additional gain offered by the other registration techniques. For simultaneous target alignment, centroid LE as low as 3.9 {+-} 2.7 mm and 3.8 {+-} 2.3 mm were achieved for the GTV{sub P} and GTV{sub LN}, respectively, using rereferenced registration. Conclusions: Target shape, volume, and configuration changes during radiation therapy limited the accuracy of standard rigid registration for image-guided localization in locally-advanced lung cancer. Significant error reductions were possible using other rigid registration techniques, with LE approaching the lower limit imposed by interfraction target variability throughout treatment.« less

Automatic measurement of target crossing speed

NASA Astrophysics Data System (ADS)

Wardell, Mark; Lougheed, James H.

1992-11-01

The motion of ground vehicle targets after a ballistic round is launched can be a major source of inaccuracy for small (handheld) anti-armour weapon systems. A method of automatically measuring the crossing component to compensate the fire control solution has been devised and tested against various targets in a range of environments. A photodetector array aligned with the sight's horizontal reticle obtains scene features, which are digitized and processed to separate target from sight motion. Relative motion of the target against the background is briefly monitored to deduce angular crossing rate and a compensating lead angle is introduced into the aim point. Research to gather quantitative data and optimize algorithm performance is described, and some results from field testing are presented.

46 CFR 76.33-20 - Operation and installation.

Code of Federal Regulations, 2011 CFR

2011-10-01

... EQUIPMENT Smoke Detecting System, Details § 76.33-20 Operation and installation. (a) The system shall be so arranged and installed that the presence of smoke in any of the protected spaces will automatically be... automatically indicate the zone in which the smoke originated. The detecting cabinet shall normally be located...

46 CFR 76.33-20 - Operation and installation.

Code of Federal Regulations, 2014 CFR

2014-10-01

... EQUIPMENT Smoke Detecting System, Details § 76.33-20 Operation and installation. (a) The system shall be so arranged and installed that the presence of smoke in any of the protected spaces will automatically be... automatically indicate the zone in which the smoke originated. The detecting cabinet shall normally be located...

46 CFR 76.33-20 - Operation and installation.

Code of Federal Regulations, 2012 CFR

2012-10-01

... EQUIPMENT Smoke Detecting System, Details § 76.33-20 Operation and installation. (a) The system shall be so arranged and installed that the presence of smoke in any of the protected spaces will automatically be... automatically indicate the zone in which the smoke originated. The detecting cabinet shall normally be located...

46 CFR 76.33-20 - Operation and installation.

Code of Federal Regulations, 2013 CFR

2013-10-01

... EQUIPMENT Smoke Detecting System, Details § 76.33-20 Operation and installation. (a) The system shall be so arranged and installed that the presence of smoke in any of the protected spaces will automatically be... automatically indicate the zone in which the smoke originated. The detecting cabinet shall normally be located...

Automatic Conceptual Encoding of Printed Verbal Material: Assessment of Population Differences.

ERIC Educational Resources Information Center

Kee, Daniel W.; And Others

1984-01-01

The release from proactive interference task as used to investigate categorical encoding of items. Low socioeconomic status Black and middle socioeconomic status White children were compared. Conceptual encoding differences between these populations were not detected in automatic conceptual encoding but were detected when the free recall method…

Multi-layer cube sampling for liver boundary detection in PET-CT images.

PubMed

Liu, Xinxin; Yang, Jian; Song, Shuang; Song, Hong; Ai, Danni; Zhu, Jianjun; Jiang, Yurong; Wang, Yongtian

2018-06-01

Liver metabolic information is considered as a crucial diagnostic marker for the diagnosis of fever of unknown origin, and liver recognition is the basis of automatic diagnosis of metabolic information extraction. However, the poor quality of PET and CT images is a challenge for information extraction and target recognition in PET-CT images. The existing detection method cannot meet the requirement of liver recognition in PET-CT images, which is the key problem in the big data analysis of PET-CT images. A novel texture feature descriptor called multi-layer cube sampling (MLCS) is developed for liver boundary detection in low-dose CT and PET images. The cube sampling feature is proposed for extracting more texture information, which uses a bi-centric voxel strategy. Neighbour voxels are divided into three regions by the centre voxel and the reference voxel in the histogram, and the voxel distribution information is statistically classified as texture feature. Multi-layer texture features are also used to improve the ability and adaptability of target recognition in volume data. The proposed feature is tested on the PET and CT images for liver boundary detection. For the liver in the volume data, mean detection rate (DR) and mean error rate (ER) reached 95.15 and 7.81% in low-quality PET images, and 83.10 and 21.08% in low-contrast CT images. The experimental results demonstrated that the proposed method is effective and robust for liver boundary detection.

Automatic lumbar vertebrae detection based on feature fusion deep learning for partial occluded C-arm X-ray images.

PubMed

Yang Li; Wei Liang; Yinlong Zhang; Haibo An; Jindong Tan

2016-08-01

Automatic and accurate lumbar vertebrae detection is an essential step of image-guided minimally invasive spine surgery (IG-MISS). However, traditional methods still require human intervention due to the similarity of vertebrae, abnormal pathological conditions and uncertain imaging angle. In this paper, we present a novel convolutional neural network (CNN) model to automatically detect lumbar vertebrae for C-arm X-ray images. Training data is augmented by DRR and automatic segmentation of ROI is able to reduce the computational complexity. Furthermore, a feature fusion deep learning (FFDL) model is introduced to combine two types of features of lumbar vertebrae X-ray images, which uses sobel kernel and Gabor kernel to obtain the contour and texture of lumbar vertebrae, respectively. Comprehensive qualitative and quantitative experiments demonstrate that our proposed model performs more accurate in abnormal cases with pathologies and surgical implants in multi-angle views.

Automatic textual annotation of video news based on semantic visual object extraction

NASA Astrophysics Data System (ADS)

Boujemaa, Nozha; Fleuret, Francois; Gouet, Valerie; Sahbi, Hichem

2003-12-01

In this paper, we present our work for automatic generation of textual metadata based on visual content analysis of video news. We present two methods for semantic object detection and recognition from a cross modal image-text thesaurus. These thesaurus represent a supervised association between models and semantic labels. This paper is concerned with two semantic objects: faces and Tv logos. In the first part, we present our work for efficient face detection and recogniton with automatic name generation. This method allows us also to suggest the textual annotation of shots close-up estimation. On the other hand, we were interested to automatically detect and recognize different Tv logos present on incoming different news from different Tv Channels. This work was done jointly with the French Tv Channel TF1 within the "MediaWorks" project that consists on an hybrid text-image indexing and retrieval plateform for video news.

Real time coarse orientation detection in MR scans using multi-planar deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Bhatia, Parmeet S.; Reda, Fitsum; Harder, Martin; Zhan, Yiqiang; Zhou, Xiang Sean

2017-02-01

Automatically detecting anatomy orientation is an important task in medical image analysis. Specifically, the ability to automatically detect coarse orientation of structures is useful to minimize the effort of fine/accurate orientation detection algorithms, to initialize non-rigid deformable registration algorithms or to align models to target structures in model-based segmentation algorithms. In this work, we present a deep convolution neural network (DCNN)-based method for fast and robust detection of the coarse structure orientation, i.e., the hemi-sphere where the principal axis of a structure lies. That is, our algorithm predicts whether the principal orientation of a structure is in the northern hemisphere or southern hemisphere, which we will refer to as UP and DOWN, respectively, in the remainder of this manuscript. The only assumption of our method is that the entire structure is located within the scan's field-of-view (FOV). To efficiently solve the problem in 3D space, we formulated it as a multi-planar 2D deep learning problem. In the training stage, a large number coronal-sagittal slice pairs are constructed as 2-channel images to train a DCNN to classify whether a scan is UP or DOWN. During testing, we randomly sample a small number of coronal-sagittal 2-channel images and pass them through our trained network. Finally, coarse structure orientation is determined using majority voting. We tested our method on 114 Elbow MR Scans. Experimental results suggest that only five 2-channel images are sufficient to achieve a high success rate of 97.39%. Our method is also extremely fast and takes approximately 50 milliseconds per 3D MR scan. Our method is insensitive to the location of the structure in the FOV.

MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection

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

Ghose, Soumya, E-mail: soumya.ghose@case.edu; Mitra, Jhimli; Rivest-Hénault, David

Purpose: The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Methods: Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contoursmore » were selected in a spectral clustering manifold learning framework. This aids in clustering “similar” gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. Results: A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). Conclusions: An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold seeds are either correctly detected or a warning is raised for further manual intervention.« less

MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection.

PubMed

Ghose, Soumya; Mitra, Jhimli; Rivest-Hénault, David; Fazlollahi, Amir; Stanwell, Peter; Pichler, Peter; Sun, Jidi; Fripp, Jurgen; Greer, Peter B; Dowling, Jason A

2016-05-01

The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contours were selected in a spectral clustering manifold learning framework. This aids in clustering "similar" gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold seeds are either correctly detected or a warning is raised for further manual intervention.

Global Positioning System Synchronized Active Light Autonomous Docking System

NASA Technical Reports Server (NTRS)

Howard, Richard T. (Inventor); Book, Michael L. (Inventor); Bryan, Thomas C. (Inventor); Bell, Joseph L. (Inventor)

1996-01-01

A Global Positioning System Synchronized Active Light Autonomous Docking System (GPSSALADS) for automatically docking a chase vehicle with a target vehicle comprising at least one active light emitting target which is operatively attached to the target vehicle. The target includes a three-dimensional array of concomitantly flashing lights which flash at a controlled common frequency. The GPSSALADS further comprises a visual tracking sensor operatively attached to the chase vehicle for detecting and tracking the target vehicle. Its performance is synchronized with the flash frequency of the lights by a synchronization means which is comprised of first and second internal clocks operatively connected to the active light target and visual tracking sensor, respectively, for providing timing control signals thereto, respectively. The synchronization means further includes first and second Global Positioning System receivers operatively connected to the first and second internal clocks, respectively, for repeatedly providing simultaneous synchronization pulses to the internal clocks, respectively. In addition, the GPSSALADS includes a docking process controller means which is operatively attached to the chase vehicle and is responsive to the visual tracking sensor for producing commands for the guidance and propulsion system of the chase vehicle.

Global Positioning System Synchronized Active Light Autonomous Docking System

NASA Technical Reports Server (NTRS)

Howard, Richard (Inventor)

1994-01-01

A Global Positioning System Synchronized Active Light Autonomous Docking System (GPSSALADS) for automatically docking a chase vehicle with a target vehicle comprises at least one active light emitting target which is operatively attached to the target vehicle. The target includes a three-dimensional array of concomitantly flashing lights which flash at a controlled common frequency. The GPSSALADS further comprises a visual tracking sensor operatively attached to the chase vehicle for detecting and tracking the target vehicle. Its performance is synchronized with the flash frequency of the lights by a synchronization means which is comprised of first and second internal clocks operatively connected to the active light target and visual tracking sensor, respectively, for providing timing control signals thereto, respectively. The synchronization means further includes first and second Global Positioning System receivers operatively connected to the first and second internal clocks, respectively, for repeatedly providing simultaneous synchronization pulses to the internal clocks, respectively. In addition, the GPSSALADS includes a docking process controller means which is operatively attached to the chase vehicle and is responsive to the visual tracking sensor for producing commands for the guidance and propulsion system of the chase vehicle.

Automatic recognition of ship types from infrared images using superstructure moment invariants

NASA Astrophysics Data System (ADS)

Li, Heng; Wang, Xinyu

2007-11-01

Automatic object recognition is an active area of interest for military and commercial applications. In this paper, a system addressing autonomous recognition of ship types in infrared images is proposed. Firstly, an approach of segmentation based on detection of salient features of the target with subsequent shadow removing is proposed, as is the base of the subsequent object recognition. Considering the differences between the shapes of various ships mainly lie in their superstructures, we then use superstructure moment functions invariant to translation, rotation and scale differences in input patterns and develop a robust algorithm of obtaining ship superstructure. Subsequently a back-propagation neural network is used as a classifier in the recognition stage and projection images of simulated three-dimensional ship models are used as the training sets. Our recognition model was implemented and experimentally validated using both simulated three-dimensional ship model images and real images derived from video of an AN/AAS-44V Forward Looking Infrared(FLIR) sensor.

Improving throughput for temporal target nomination using existing infrastructure

NASA Astrophysics Data System (ADS)

Raeth, Peter G.

2007-04-01

Earlier, we reported on predictive anomaly detection (PAD) for nominating targets within data streams generated by persistent sensing and surveillance. This technique is purely temporal and does not directly depend on the physics attendant on the sensed environment. Since PAD adapts to evolving data streams, there are no determinacy assumptions. We showed PAD to be general across sensor types, demonstrating it using synthetic chaotic data and in audio, visual, and infrared applications. Defense-oriented demonstrations included explosions, muzzle flashes, and missile and aircraft detection. Experiments were ground-based and air-to-air. As new sensors come on line, PAD offers immediate data filtering and target nomination. Its results can be taken individually, pixel by pixel, for spectral analysis and material detection/identification. They can also be grouped for shape analysis, target identification, and track development. PAD analyses reduce data volume by around 95%, depending on target number and size, while still retaining all target indicators. While PAD's code is simple when compared to physics codes, PAD tends to build a huge model. A PAD model for 512 x 640 frames may contain 19,660,800 Gaussian basis functions. (PAD models grow linearly with the number of pixels and the frequency content, in the FFT sense, of the sensed scenario's background data). PAD's complexity in terms of computational and data intensity is an example of what one sees in new algorithms now in the R&D pipeline, especially as DoD seeks capability that runs fully automatic, with little to no human interaction. Work is needed to improve algorithms' throughput while employing existing infrastructure, yet allowing for growth in the types of hardware employed. In this present paper, we discuss a generic cluster interface for legacy codes that can be partitioned at the data level. The discussion's foundation is the growth of PAD models to accommodate a particular scenario and the need to reduce false alarms while preserving all targets. The discussion closes with a view of future software and hardware opportunities.

46 CFR 76.05-1 - Fire detecting systems.

Code of Federal Regulations, 2010 CFR

2010-10-01

... fitted with an automatic sprinkling system, except in relatively incombustible spaces. 2 Sprinkler heads....1 Offices, lockers, and isolated storerooms Electric, pneumatic, or automatic sprinkling1 Do.1 Public spaces None required with 20-minute patrol. Electric, pneumatic, or automatic sprinkling with 1...

46 CFR 76.05-1 - Fire detecting systems.

Code of Federal Regulations, 2011 CFR

2011-10-01

... fitted with an automatic sprinkling system, except in relatively incombustible spaces. 2 Sprinkler heads....1 Offices, lockers, and isolated storerooms Electric, pneumatic, or automatic sprinkling1 Do.1 Public spaces None required with 20-minute patrol. Electric, pneumatic, or automatic sprinkling with 1...

Use of an automatic resistivity system for detecting abandoned mine workings

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

Peters, W.R.; Burdick, R.G.

1983-01-01

A high-resolution earth resistivity system has been designed and constructed for use as a means of detecting abandoned coal mine workings. The automatic pole-dipole earth resistivity technique has already been applied to the detection of subsurface voids for military applications. The hardware and software of the system are described, together with applications for surveying and mapping abandoned coal mine workings. Field tests are presented to illustrate the detection of both air-filled and water-filled mine workings.

Visual pop-out in barn owls: Human-like behavior in the avian brain.

PubMed

Orlowski, Julius; Beissel, Christian; Rohn, Friederike; Adato, Yair; Wagner, Hermann; Ben-Shahar, Ohad

2015-01-01

Visual pop-out is a phenomenon by which the latency to detect a target in a scene is independent of the number of other elements, the distractors. Pop-out is an effective visual-search guidance that occurs typically when the target is distinct in one feature from the distractors, thus facilitating fast detection of predators or prey. However, apart from studies on primates, pop-out has been examined in few species and demonstrated thus far in rats, archer fish, and pigeons only. To fill this gap, here we study pop-out in barn owls. These birds are a unique model system for such exploration because their lack of eye movements dictates visual behavior dominated by head movements. Head saccades and interspersed fixation periods can therefore be tracked and analyzed with a head-mounted wireless microcamera--the OwlCam. Using this methodology we confronted two owls with scenes containing search arrays of one target among varying numbers (15-63) of similar looking distractors. We tested targets distinct either by orientation (Experiment 1) or luminance contrast (Experiment 2). Search time and the number of saccades until the target was fixated remained largely independent of the number of distractors in both experiments. This suggests that barn owls can exhibit pop-out during visual search, thus expanding the group of species and brain structures that can cope with this fundamental visual behavior. The utility of our automatic analysis method is further discussed for other species and scientific questions.

SU-E-T-362: Automatic Catheter Reconstruction of Flap Applicators in HDR Surface Brachytherapy

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

Buzurovic, I; Devlin, P; Hansen, J

2014-06-01

Purpose: Catheter reconstruction is crucial for the accurate delivery of radiation dose in HDR brachytherapy. The process becomes complicated and time-consuming for large superficial clinical targets with a complex topology. A novel method for the automatic catheter reconstruction of flap applicators is proposed in this study. Methods: We have developed a program package capable of image manipulation, using C++class libraries of The-Visualization-Toolkit(VTK) software system. The workflow for automatic catheter reconstruction is: a)an anchor point is placed in 3D or in the axial view of the first slice at the tip of the first, last and middle points for the curvedmore » surface; b)similar points are placed on the last slice of the image set; c)the surface detection algorithm automatically registers the points to the images and applies the surface reconstruction filter; d)then a structured grid surface is generated through the center of the treatment catheters placed at a distance of 5mm from the patient's skin. As a result, a mesh-style plane is generated with the reconstructed catheters placed 10mm apart. To demonstrate automatic catheter reconstruction, we used CT images of patients diagnosed with cutaneous T-cell-lymphoma and imaged with Freiburg-Flap-Applicators (Nucletron™-Elekta, Netherlands). The coordinates for each catheter were generated and compared to the control points selected during the manual reconstruction for 16catheters and 368control point Results: The variation of the catheter tip positions between the automatically and manually reconstructed catheters was 0.17mm(SD=0.23mm). The position difference between the manually selected catheter control points and the corresponding points obtained automatically was 0.17mm in the x-direction (SD=0.23mm), 0.13mm in the y-direction (SD=0.22mm), and 0.14mm in the z-direction (SD=0.24mm). Conclusion: This study shows the feasibility of the automatic catheter reconstruction of flap applicators with a high level of positioning accuracy. Implementation of this technique has potential to decrease the planning time and may improve overall quality in superficial brachytherapy.« less

Hyperspectral target detection analysis of a cluttered scene from a virtual airborne sensor platform using MuSES

NASA Astrophysics Data System (ADS)

Packard, Corey D.; Viola, Timothy S.; Klein, Mark D.

2017-10-01

The ability to predict spectral electro-optical (EO) signatures for various targets against realistic, cluttered backgrounds is paramount for rigorous signature evaluation. Knowledge of background and target signatures, including plumes, is essential for a variety of scientific and defense-related applications including contrast analysis, camouflage development, automatic target recognition (ATR) algorithm development and scene material classification. The capability to simulate any desired mission scenario with forecast or historical weather is a tremendous asset for defense agencies, serving as a complement to (or substitute for) target and background signature measurement campaigns. In this paper, a systematic process for the physical temperature and visible-through-infrared radiance prediction of several diverse targets in a cluttered natural environment scene is presented. The ability of a virtual airborne sensor platform to detect and differentiate targets from a cluttered background, from a variety of sensor perspectives and across numerous wavelengths in differing atmospheric conditions, is considered. The process described utilizes the thermal and radiance simulation software MuSES and provides a repeatable, accurate approach for analyzing wavelength-dependent background and target (including plume) signatures in multiple band-integrated wavebands (multispectral) or hyperspectrally. The engineering workflow required to combine 3D geometric descriptions, thermal material properties, natural weather boundary conditions, all modes of heat transfer and spectral surface properties is summarized. This procedure includes geometric scene creation, material and optical property attribution, and transient physical temperature prediction. Radiance renderings, based on ray-tracing and the Sandford-Robertson BRDF model, are coupled with MODTRAN for the inclusion of atmospheric effects. This virtual hyperspectral/multispectral radiance prediction methodology has been extensively validated and provides a flexible process for signature evaluation and algorithm development.

An image-based approach for automatic detecting five true-leaves stage of cotton

NASA Astrophysics Data System (ADS)

Li, Yanan; Cao, Zhiguo; Wu, Xi; Yu, Zhenghong; Wang, Yu; Bai, Xiaodong

2013-10-01

Cotton, as one of the four major economic crops, is of great significance to the development of the national economy. Monitoring cotton growth status by automatic image-based detection makes sense due to its low-cost, low-labor and the capability of continuous observations. However, little research has been done to improve close observation of different growth stages of field crops using digital cameras. Therefore, algorithms proposed by us were developed to detect the growth information and predict the starting date of cotton automatically. In this paper, we introduce an approach for automatic detecting five true-leaves stage, which is a critical growth stage of cotton. On account of the drawbacks caused by illumination and the complex background, we cannot use the global coverage as the unique standard of judgment. Consequently, we propose a new method to determine the five true-leaves stage through detecting the node number between the main stem and the side stems, based on the agricultural meteorological observation specification. The error of the results between the predicted starting date with the proposed algorithm and artificial observations is restricted to no more than one day.

Director, Operational Test and Evaluation FY 2004 Annual Report

DTIC Science & Technology

2004-01-01

HIGH) Space Based Radar (SBR) Sensor Fuzed Weapon (SFW) P3I (CBU-97/B) Small Diameter Bomb (SDB) Secure Mobile Anti-Jam Reliable Tactical Terminal...detection, identification, and sampling capability for both fixed-site and mobile operations. The system must automatically detect and identify up to ten...staffing within the Services. SYSTEM DESCRIPTION AND MISSION The Services envision JCAD as a hand-held device that automatically detects, identifies, and

Subliminal enhancement of predictive effects during syntactic processing in the left inferior frontal gyrus: an MEG study

PubMed Central

Iijima, Kazuki; Sakai, Kuniyoshi L.

2014-01-01

Predictive syntactic processing plays an essential role in language comprehension. In our previous study using Japanese object-verb (OV) sentences, we showed that the left inferior frontal gyrus (IFG) responses to a verb increased at 120–140 ms after the verb onset, indicating predictive effects caused by a preceding object. To further elucidate the automaticity of the predictive effects in the present magnetoencephalography study, we examined whether a subliminally presented verb (“subliminal verb”) enhanced the predictive effects on the sentence-final verb (“target verb”) unconsciously, i.e., without awareness. By presenting a subliminal verb after the object, enhanced predictive effects on the target verb would be detected in the OV sentences when the transitivity of the target verb matched with that of the subliminal verb (“congruent condition”), because the subliminal verb just after the object could determine the grammaticality of the sentence. For the OV sentences under the congruent condition, we observed significantly increased left IFG responses at 140–160 ms after the target verb onset. In contrast, responses in the precuneus and midcingulate cortex (MCC) were significantly reduced for the OV sentences under the congruent condition at 110–140 and 280–300 ms, respectively. By using partial Granger causality analyses for the OV sentences under the congruent condition, we revealed a bidirectional interaction between the left IFG and MCC at 60–160 ms, as well as a significant influence from the MCC to the precuneus. These results indicate that a top-down influence from the left IFG to the MCC, and then to the precuneus, is critical in syntactic decisions, whereas the MCC shares its task-set information with the left IFG to achieve automatic and predictive processes of syntax. PMID:25404899

Subliminal enhancement of predictive effects during syntactic processing in the left inferior frontal gyrus: an MEG study.

PubMed

Iijima, Kazuki; Sakai, Kuniyoshi L

2014-01-01

Predictive syntactic processing plays an essential role in language comprehension. In our previous study using Japanese object-verb (OV) sentences, we showed that the left inferior frontal gyrus (IFG) responses to a verb increased at 120-140 ms after the verb onset, indicating predictive effects caused by a preceding object. To further elucidate the automaticity of the predictive effects in the present magnetoencephalography study, we examined whether a subliminally presented verb ("subliminal verb") enhanced the predictive effects on the sentence-final verb ("target verb") unconsciously, i.e., without awareness. By presenting a subliminal verb after the object, enhanced predictive effects on the target verb would be detected in the OV sentences when the transitivity of the target verb matched with that of the subliminal verb ("congruent condition"), because the subliminal verb just after the object could determine the grammaticality of the sentence. For the OV sentences under the congruent condition, we observed significantly increased left IFG responses at 140-160 ms after the target verb onset. In contrast, responses in the precuneus and midcingulate cortex (MCC) were significantly reduced for the OV sentences under the congruent condition at 110-140 and 280-300 ms, respectively. By using partial Granger causality analyses for the OV sentences under the congruent condition, we revealed a bidirectional interaction between the left IFG and MCC at 60-160 ms, as well as a significant influence from the MCC to the precuneus. These results indicate that a top-down influence from the left IFG to the MCC, and then to the precuneus, is critical in syntactic decisions, whereas the MCC shares its task-set information with the left IFG to achieve automatic and predictive processes of syntax.

Toward comprehensive detection of sight threatening retinal disease using a multiscale AM-FM methodology

NASA Astrophysics Data System (ADS)

Agurto, C.; Barriga, S.; Murray, V.; Murillo, S.; Zamora, G.; Bauman, W.; Pattichis, M.; Soliz, P.

2011-03-01

In the United States and most of the western world, the leading causes of vision impairment and blindness are age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma. In the last decade, research in automatic detection of retinal lesions associated with eye diseases has produced several automatic systems for detection and screening of AMD, DR, and glaucoma. However. advanced, sight-threatening stages of DR and AMD can present with lesions not commonly addressed by current approaches to automatic screening. In this paper we present an automatic eye screening system based on multiscale Amplitude Modulation-Frequency Modulation (AM-FM) decompositions that addresses not only the early stages, but also advanced stages of retinal and optic nerve disease. Ten different experiments were performed in which abnormal features such as neovascularization, drusen, exudates, pigmentation abnormalities, geographic atrophy (GA), and glaucoma were classified. The algorithm achieved an accuracy detection range of [0.77 to 0.98] area under the ROC curve for a set of 810 images. When set to a specificity value of 0.60, the sensitivity of the algorithm to the detection of abnormal features ranged between 0.88 and 1.00. Our system demonstrates that, given an appropriate training set, it is possible to use a unique algorithm to detect a broad range of eye diseases.

Adaptive Self-Tuning Networks

NASA Astrophysics Data System (ADS)

Knox, H. A.; Draelos, T.; Young, C. J.; Lawry, B.; Chael, E. P.; Faust, A.; Peterson, M. G.

2015-12-01

The quality of automatic detections from seismic sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. Yet, achieving superior automatic detection of seismic events is closely related to these parameters. We present an automated sensor tuning (AST) system that learns near-optimal parameter settings for each event type using neuro-dynamic programming (reinforcement learning) trained with historic data. AST learns to test the raw signal against all event-settings and automatically self-tunes to an emerging event in real-time. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Reducing false alarms early in the seismic pipeline processing will have a significant impact on this goal. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections in seismic waveforms from a network of stations, we show that AST increases the probability of detection while decreasing false alarms.

Robust Spacecraft Component Detection in Point Clouds.

PubMed

Wei, Quanmao; Jiang, Zhiguo; Zhang, Haopeng

2018-03-21

Automatic component detection of spacecraft can assist in on-orbit operation and space situational awareness. Spacecraft are generally composed of solar panels and cuboidal or cylindrical modules. These components can be simply represented by geometric primitives like plane, cuboid and cylinder. Based on this prior, we propose a robust automatic detection scheme to automatically detect such basic components of spacecraft in three-dimensional (3D) point clouds. In the proposed scheme, cylinders are first detected in the iteration of the energy-based geometric model fitting and cylinder parameter estimation. Then, planes are detected by Hough transform and further described as bounded patches with their minimum bounding rectangles. Finally, the cuboids are detected with pair-wise geometry relations from the detected patches. After successive detection of cylinders, planar patches and cuboids, a mid-level geometry representation of the spacecraft can be delivered. We tested the proposed component detection scheme on spacecraft 3D point clouds synthesized by computer-aided design (CAD) models and those recovered by image-based reconstruction, respectively. Experimental results illustrate that the proposed scheme can detect the basic geometric components effectively and has fine robustness against noise and point distribution density.

Robust Spacecraft Component Detection in Point Clouds

PubMed Central

Wei, Quanmao; Jiang, Zhiguo

2018-01-01

Automatic component detection of spacecraft can assist in on-orbit operation and space situational awareness. Spacecraft are generally composed of solar panels and cuboidal or cylindrical modules. These components can be simply represented by geometric primitives like plane, cuboid and cylinder. Based on this prior, we propose a robust automatic detection scheme to automatically detect such basic components of spacecraft in three-dimensional (3D) point clouds. In the proposed scheme, cylinders are first detected in the iteration of the energy-based geometric model fitting and cylinder parameter estimation. Then, planes are detected by Hough transform and further described as bounded patches with their minimum bounding rectangles. Finally, the cuboids are detected with pair-wise geometry relations from the detected patches. After successive detection of cylinders, planar patches and cuboids, a mid-level geometry representation of the spacecraft can be delivered. We tested the proposed component detection scheme on spacecraft 3D point clouds synthesized by computer-aided design (CAD) models and those recovered by image-based reconstruction, respectively. Experimental results illustrate that the proposed scheme can detect the basic geometric components effectively and has fine robustness against noise and point distribution density. PMID:29561828

Robust fusion-based processing for military polarimetric imaging systems

NASA Astrophysics Data System (ADS)

Hickman, Duncan L.; Smith, Moira I.; Kim, Kyung Su; Choi, Hyun-Jin

2017-05-01

Polarisation information within a scene can be exploited in military systems to give enhanced automatic target detection and recognition (ATD/R) performance. However, the performance gain achieved is highly dependent on factors such as the geometry, viewing conditions, and the surface finish of the target. Such performance sensitivities are highly undesirable in many tactical military systems where operational conditions can vary significantly and rapidly during a mission. Within this paper, a range of processing architectures and fusion methods is considered in terms of their practical viability and operational robustness for systems requiring ATD/R. It is shown that polarisation information can give useful performance gains but, to retained system robustness, the introduction of polarimetric processing should be done in such a way as to not compromise other discriminatory scene information in the spectral and spatial domains. The analysis concludes that polarimetric data can be effectively integrated with conventional intensity-based ATD/R by either adapting the ATD/R processing function based on the scene polarisation or else by detection-level fusion. Both of these approaches avoid the introduction of processing bottlenecks and limit the impact of processing on system latency.

Developing and evaluating a target-background similarity metric for camouflage detection.

PubMed

Lin, Chiuhsiang Joe; Chang, Chi-Chan; Liu, Bor-Shong

2014-01-01

Measurement of camouflage performance is of fundamental importance for military stealth applications. The goal of camouflage assessment algorithms is to automatically assess the effect of camouflage in agreement with human detection responses. In a previous study, we found that the Universal Image Quality Index (UIQI) correlated well with the psychophysical measures, and it could be a potentially camouflage assessment tool. In this study, we want to quantify the camouflage similarity index and psychophysical results. We compare several image quality indexes for computational evaluation of camouflage effectiveness, and present the results of an extensive human visual experiment conducted to evaluate the performance of several camouflage assessment algorithms and analyze the strengths and weaknesses of these algorithms. The experimental data demonstrates the effectiveness of the approach, and the correlation coefficient result of the UIQI was higher than those of other methods. This approach was highly correlated with the human target-searching results. It also showed that this method is an objective and effective camouflage performance evaluation method because it considers the human visual system and image structure, which makes it consistent with the subjective evaluation results.

State Recognition of High Voltage Isolation Switch Based on Background Difference and Iterative Search

NASA Astrophysics Data System (ADS)

Xu, Jiayuan; Yu, Chengtao; Bo, Bin; Xue, Yu; Xu, Changfu; Chaminda, P. R. Dushantha; Hu, Chengbo; Peng, Kai

2018-03-01

The automatic recognition of the high voltage isolation switch by remote video monitoring is an effective means to ensure the safety of the personnel and the equipment. The existing methods mainly include two ways: improving monitoring accuracy and adopting target detection technology through equipment transformation. Such a method is often applied to specific scenarios, with limited application scope and high cost. To solve this problem, a high voltage isolation switch state recognition method based on background difference and iterative search is proposed in this paper. The initial position of the switch is detected in real time through the background difference method. When the switch starts to open and close, the target tracking algorithm is used to track the motion trajectory of the switch. The opening and closing state of the switch is determined according to the angle variation of the switch tracking point and the center line. The effectiveness of the method is verified by experiments on different switched video frames of switching states. Compared with the traditional methods, this method is more robust and effective.

Controlled cooling of an electronic system for reduced energy consumption

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

David, Milnes P.; Iyengar, Madhusudan K.; Schmidt, Roger R.

Energy efficient control of a cooling system cooling an electronic system is provided. The control includes automatically determining at least one adjusted control setting for at least one adjustable cooling component of a cooling system cooling the electronic system. The automatically determining is based, at least in part, on power being consumed by the cooling system and temperature of a heat sink to which heat extracted by the cooling system is rejected. The automatically determining operates to reduce power consumption of the cooling system and/or the electronic system while ensuring that at least one targeted temperature associated with the coolingmore » system or the electronic system is within a desired range. The automatically determining may be based, at least in part, on one or more experimentally obtained models relating the targeted temperature and power consumption of the one or more adjustable cooling components of the cooling system.« less

Controlled cooling of an electronic system based on projected conditions

DOEpatents

David, Milnes P.; Iyengar, Madhusudan K.; Schmidt, Roger R.

2016-05-17

Energy efficient control of a cooling system cooling an electronic system is provided based, in part, on projected conditions. The control includes automatically determining an adjusted control setting(s) for an adjustable cooling component(s) of the cooling system. The automatically determining is based, at least in part, on projected power consumed by the electronic system at a future time and projected temperature at the future time of a heat sink to which heat extracted is rejected. The automatically determining operates to reduce power consumption of the cooling system and/or the electronic system while ensuring that at least one targeted temperature associated with the cooling system or the electronic system is within a desired range. The automatically determining may be based, at least in part, on an experimentally obtained model(s) relating the targeted temperature and power consumption of the adjustable cooling component(s) of the cooling system.

Controlled cooling of an electronic system based on projected conditions

DOEpatents

David, Milnes P.; Iyengar, Madhusudan K.; Schmidt, Roger R.

2015-08-18

Energy efficient control of a cooling system cooling an electronic system is provided based, in part, on projected conditions. The control includes automatically determining an adjusted control setting(s) for an adjustable cooling component(s) of the cooling system. The automatically determining is based, at least in part, on projected power consumed by the electronic system at a future time and projected temperature at the future time of a heat sink to which heat extracted is rejected. The automatically determining operates to reduce power consumption of the cooling system and/or the electronic system while ensuring that at least one targeted temperature associated with the cooling system or the electronic system is within a desired range. The automatically determining may be based, at least in part, on an experimentally obtained model(s) relating the targeted temperature and power consumption of the adjustable cooling component(s) of the cooling system.

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

David, Milnes P.; Iyengar, Madhusudan K.; Schmidt, Roger R.

Energy efficient control of a cooling system cooling an electronic system is provided. The control includes automatically determining at least one adjusted control setting for at least one adjustable cooling component of a cooling system cooling the electronic system. The automatically determining is based, at least in part, on power being consumed by the cooling system and temperature of a heat sink to which heat extracted by the cooling system is rejected. The automatically determining operates to reduce power consumption of the cooling system and/or the electronic system while ensuring that at least one targeted temperature associated with the coolingmore » system or the electronic system is within a desired range. The automatically determining may be based, at least in part, on one or more experimentally obtained models relating the targeted temperature and power consumption of the one or more adjustable cooling components of the cooling system.« less

In-flight automatic detection of vigilance states using a single EEG channel.

PubMed

Sauvet, F; Bougard, C; Coroenne, M; Lely, L; Van Beers, P; Elbaz, M; Guillard, M; Leger, D; Chennaoui, M

2014-12-01

Sleepiness and fatigue can reach particularly high levels during long-haul overnight flights. Under these conditions, voluntary or even involuntary sleep periods may occur, increasing the risk of accidents. The aim of this study was to assess the performance of an in-flight automatic detection system of low-vigilance states using a single electroencephalogram channel. Fourteen healthy pilots voluntarily wore a miniaturized brain electrical activity recording device during long-haul flights ( 10 ±2.0 h, Atlantic 2 and Falcon 50 M, French naval aviation). No subject was disturbed by the equipment. Seven pilots experienced at least a period of voluntary ( 26.8 ±8.0 min, n = 4) or involuntary sleep (N1 sleep stage, 26.6 ±18.7 s, n = 7) during the flight. Automatic classification (wake/sleep) by the algorithm was made for 10-s epochs (O1-M2 or C3-M2 channel), based on comparison of means to detect changes in α, β, and θ relative power, or ratio [( α+θ)/β], or fuzzy logic fusion (α, β). Pertinence and prognostic of the algorithm were determined using epoch-by-epoch comparison with visual-scoring (two blinded readers, AASM rules). The best concordance between automatic detection and visual-scoring was observed within the O1-M2 channel, using the ratio [( α+θ )/β] ( 98.3 ±4.1% of good detection, K = 0.94 ±0.07, with a 0.04 ±0.04 false positive rate and a 0.87 ±0.10 true positive rate). Our results confirm the efficiency of a miniaturized single electroencephalographic channel recording device, associated with an automatic detection algorithm, in order to detect low-vigilance states during real flights.

Automatic internal crack detection from a sequence of infrared images with a triple-threshold Canny edge detector

NASA Astrophysics Data System (ADS)

Wang, Gaochao; Tse, Peter W.; Yuan, Maodan

2018-02-01

Visual inspection and assessment of the condition of metal structures are essential for safety. Pulse thermography produces visible infrared images, which have been widely applied to detect and characterize defects in structures and materials. When active thermography, a non-destructive testing tool, is applied, the necessity of considerable manual checking can be avoided. However, detecting an internal crack with active thermography remains difficult, since it is usually invisible in the collected sequence of infrared images, which makes the automatic detection of internal cracks even harder. In addition, the detection of an internal crack can be hindered by a complicated inspection environment. With the purpose of putting forward a robust and automatic visual inspection method, a computer vision-based thresholding method is proposed. In this paper, the image signals are a sequence of infrared images collected from the experimental setup with a thermal camera and two flash lamps as stimulus. The contrast of pixels in each frame is enhanced by the Canny operator and then reconstructed by a triple-threshold system. Two features, mean value in the time domain and maximal amplitude in the frequency domain, are extracted from the reconstructed signal to help distinguish the crack pixels from others. Finally, a binary image indicating the location of the internal crack is generated by a K-means clustering method. The proposed procedure has been applied to an iron pipe, which contains two internal cracks and surface abrasion. Some improvements have been made for the computer vision-based automatic crack detection methods. In the future, the proposed method can be applied to realize the automatic detection of internal cracks from many infrared images for the industry.

Accelerometer-based automatic voice onset detection in speech mapping with navigated repetitive transcranial magnetic stimulation.

PubMed

Vitikainen, Anne-Mari; Mäkelä, Elina; Lioumis, Pantelis; Jousmäki, Veikko; Mäkelä, Jyrki P

2015-09-30

The use of navigated repetitive transcranial magnetic stimulation (rTMS) in mapping of speech-related brain areas has recently shown to be useful in preoperative workflow of epilepsy and tumor patients. However, substantial inter- and intraobserver variability and non-optimal replicability of the rTMS results have been reported, and a need for additional development of the methodology is recognized. In TMS motor cortex mappings the evoked responses can be quantitatively monitored by electromyographic recordings; however, no such easily available setup exists for speech mappings. We present an accelerometer-based setup for detection of vocalization-related larynx vibrations combined with an automatic routine for voice onset detection for rTMS speech mapping applying naming. The results produced by the automatic routine were compared with the manually reviewed video-recordings. The new method was applied in the routine navigated rTMS speech mapping for 12 consecutive patients during preoperative workup for epilepsy or tumor surgery. The automatic routine correctly detected 96% of the voice onsets, resulting in 96% sensitivity and 71% specificity. Majority (63%) of the misdetections were related to visible throat movements, extra voices before the response, or delayed naming of the previous stimuli. The no-response errors were correctly detected in 88% of events. The proposed setup for automatic detection of voice onsets provides quantitative additional data for analysis of the rTMS-induced speech response modifications. The objectively defined speech response latencies increase the repeatability, reliability and stratification of the rTMS results. Copyright © 2015 Elsevier B.V. All rights reserved.

Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

NASA Astrophysics Data System (ADS)

Li, Ke; Ye, Chuyang; Yang, Zhen; Carass, Aaron; Ying, Sarah H.; Prince, Jerry L.

2016-03-01

Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.

Automatic detection and visualisation of MEG ripple oscillations in epilepsy.

PubMed

van Klink, Nicole; van Rosmalen, Frank; Nenonen, Jukka; Burnos, Sergey; Helle, Liisa; Taulu, Samu; Furlong, Paul Lawrence; Zijlmans, Maeike; Hillebrand, Arjan

2017-01-01

High frequency oscillations (HFOs, 80-500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80-250 Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~ 2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting.

Automatic Earthquake Detection and Location by Waveform coherency in Alentejo (South Portugal) Using CatchPy

NASA Astrophysics Data System (ADS)

Custodio, S.; Matos, C.; Grigoli, F.; Cesca, S.; Heimann, S.; Rio, I.

2015-12-01

Seismic data processing is currently undergoing a step change, benefitting from high-volume datasets and advanced computer power. In the last decade, a permanent seismic network of 30 broadband stations, complemented by dense temporary deployments, covered mainland Portugal. This outstanding regional coverage currently enables the computation of a high-resolution image of the seismicity of Portugal, which contributes to fitting together the pieces of the regional seismo-tectonic puzzle. Although traditional manual inspections are valuable to refine automatic results they are impracticable with the big data volumes now available. When conducted alone they are also less objective since the criteria is defined by the analyst. In this work we present CatchPy, a scanning algorithm to detect earthquakes in continuous datasets. Our main goal is to implement an automatic earthquake detection and location routine in order to have a tool to quickly process large data sets, while at the same time detecting low magnitude earthquakes (i.e. lowering the detection threshold). CatchPY is designed to produce an event database that could be easily located using existing location codes (e.g.: Grigoli et al. 2013, 2014). We use CatchPy to perform automatic detection and location of earthquakes that occurred in Alentejo region (South Portugal), taking advantage of a dense seismic network deployed in the region for two years during the DOCTAR experiment. Results show that our automatic procedure is particularly suitable for small aperture networks. The event detection is performed by continuously computing the short-term-average/long-term-average of two different characteristic functions (CFs). For the P phases we used a CF based on the vertical energy trace while for S phases we used a CF based on the maximum eigenvalue of the instantaneous covariance matrix (Vidale 1991). Seismic event location is performed by waveform coherence analysis, scanning different hypocentral coordinates (Grigoli et al. 2013, 2014). The reliability of automatic detections, phase pickings and locations are tested trough the quantitative comparison with manual results. This work is supported by project QuakeLoc, reference: PTDC/GEO-FIQ/3522/2012

Automatic classification of unexploded ordnance applied to Spencer Range live site for 5x5 TEMTADS sensor

NASA Astrophysics Data System (ADS)

Sigman, John B.; Barrowes, Benjamin E.; O'Neill, Kevin; Shubitidze, Fridon

2013-06-01

This paper details methods for automatic classification of Unexploded Ordnance (UXO) as applied to sensor data from the Spencer Range live site. The Spencer Range is a former military weapons range in Spencer, Tennessee. Electromagnetic Induction (EMI) sensing is carried out using the 5x5 Time-domain Electromagnetic Multi-sensor Towed Array Detection System (5x5 TEMTADS), which has 25 receivers and 25 co-located transmitters. Every transmitter is activated sequentially, each followed by measuring the magnetic field in all 25 receivers, from 100 microseconds to 25 milliseconds. From these data target extrinsic and intrinsic parameters are extracted using the Differential Evolution (DE) algorithm and the Ortho-Normalized Volume Magnetic Source (ONVMS) algorithms, respectively. Namely, the inversion provides x, y, and z locations and a time series of the total ONVMS principal eigenvalues, which are intrinsic properties of the objects. The eigenvalues are fit to a power-decay empirical model, the Pasion-Oldenburg model, providing 3 coefficients (k, b, and g) for each object. The objects are grouped geometrically into variably-sized clusters, in the k-b-g space, using clustering algorithms. Clusters matching a priori characteristics are identified as Targets of Interest (TOI), and larger clusters are automatically subclustered. Ground Truths (GT) at the center of each class are requested, and probability density functions are created for clusters that have centroid TOI using a Gaussian Mixture Model (GMM). The probability functions are applied to all remaining anomalies. All objects of UXO probability higher than a chosen threshold are placed in a ranked dig list. This prioritized list is scored and the results are demonstrated and analyzed.

Study on the Automatic Detection Method and System of Multifunctional Hydrocephalus Shunt

NASA Astrophysics Data System (ADS)

Sun, Xuan; Wang, Guangzhen; Dong, Quancheng; Li, Yuzhong

2017-07-01

Aiming to the difficulty of micro pressure detection and the difficulty of micro flow control in the testing process of hydrocephalus shunt, the principle of the shunt performance detection was analyzed.In this study, the author analyzed the principle of several items of shunt performance detection,and used advanced micro pressure sensor and micro flow peristaltic pump to overcome the micro pressure detection and micro flow control technology.At the same time,This study also puted many common experimental projects integrated, and successfully developed the automatic detection system for a shunt performance detection function, to achieve a test with high precision, high efficiency and automation.

WE-G-BRF-09: Force- and Image-Adaptive Strategies for Robotised Placement of 4D Ultrasound Probes

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

Kuhlemann, I; Graduate School for Computing in Life Science, University of Luebeck, Luebeck; Bruder, R

2014-06-15

Purpose: To allow continuous acquisition of high quality 4D ultrasound images for non-invasive live tracking of tumours for IGRT, image- and force-adaptive strategies for robotised placement of 4D ultrasound probes are developed and evaluated. Methods: The developed robotised ultrasound system is based on a 6-axes industrial robot (adept Viper s850) carrying a 4D ultrasound transducer with a mounted force-torque sensor. The force-adaptive placement strategies include probe position control using artificial potential fields and contact pressure regulation by a PD controller strategy. The basis for live target tracking is a continuous minimum contact pressure to ensure good image quality and highmore » patient comfort. This contact pressure can be significantly disturbed by respiratory movements and has to be compensated. All measurements were performed on human subjects under realistic conditions. When performing cardiac ultrasound, rib- and lung shadows are a common source of interference and can disrupt the tracking. To ensure continuous tracking, these artefacts had to be detected to automatically realign the probe. The detection is realised by multiple algorithms based on entropy calculations as well as a determination of the image quality. Results: Through active contact pressure regulation it was possible to reduce the variance of the contact pressure by 89.79% despite respiratory motion of the chest. The results regarding the image processing clearly demonstrate the feasibility to detect image artefacts like rib shadows in real-time. Conclusion: In all cases, it was possible to stabilise the image quality by active contact pressure control and automatically detected image artefacts. This fact enables the possibility to compensate for such interferences by realigning the probe and thus continuously optimising the ultrasound images. This is a huge step towards fully automated transducer positioning and opens the possibility for stable target tracking in ultrasoundguided radiation therapy requiring contact pressure of 5–10 N. This work was supported by the Graduate School for Computing in Medicine and Life Sciences funded by Germany's Excellence Initiative [DFG GSC 235/1].« less

An automatic lightning detection and photographic system

NASA Technical Reports Server (NTRS)

Wojtasinski, R. J.; Holley, L. D.; Gray, J. L.; Hoover, R. B.

1973-01-01

Conventional 35-mm camera is activated by an electronic signal every time lightning strikes in general vicinity. Electronic circuit detects lightning by means of antenna which picks up atmospheric radio disturbances. Camera is equipped with fish-eye lense, automatic shutter advance, and small 24-hour clock to indicate time when exposures are made.

46 CFR 161.002-9 - Automatic fire detecting system, power supply.

Code of Federal Regulations, 2013 CFR

2013-10-01

... system must meet the requirements of § 113.10-9 of subchapter J (Electrical Engineering Regulations) of... 46 Shipping 6 2013-10-01 2013-10-01 false Automatic fire detecting system, power supply. 161.002-9..., CONSTRUCTION, AND MATERIALS: SPECIFICATIONS AND APPROVAL ELECTRICAL EQUIPMENT Fire-Protective Systems § 161.002...

46 CFR 161.002-9 - Automatic fire detecting system, power supply.

Code of Federal Regulations, 2014 CFR

2014-10-01

... system must meet the requirements of § 113.10-9 of subchapter J (Electrical Engineering Regulations) of... 46 Shipping 6 2014-10-01 2014-10-01 false Automatic fire detecting system, power supply. 161.002-9..., CONSTRUCTION, AND MATERIALS: SPECIFICATIONS AND APPROVAL ELECTRICAL EQUIPMENT Fire-Protective Systems § 161.002...

46 CFR 161.002-9 - Automatic fire detecting system, power supply.

Code of Federal Regulations, 2012 CFR

2012-10-01

... system must meet the requirements of § 113.10-9 of subchapter J (Electrical Engineering Regulations) of... 46 Shipping 6 2012-10-01 2012-10-01 false Automatic fire detecting system, power supply. 161.002-9..., CONSTRUCTION, AND MATERIALS: SPECIFICATIONS AND APPROVAL ELECTRICAL EQUIPMENT Fire-Protective Systems § 161.002...

Histogram-based automatic thresholding for bruise detection of apples by structured-illumination reflectance imaging

USDA-ARS?s Scientific Manuscript database

Thresholding is an important step in the segmentation of image features, and the existing methods are not all effective when the image histogram exhibits a unimodal pattern, which is common in defect detection of fruit. This study was aimed at developing a general automatic thresholding methodology ...

Automatic bone detection and soft tissue aware ultrasound-CT registration for computer-aided orthopedic surgery.

PubMed

Wein, Wolfgang; Karamalis, Athanasios; Baumgartner, Adrian; Navab, Nassir

2015-06-01

The transfer of preoperative CT data into the tracking system coordinates within an operating room is of high interest for computer-aided orthopedic surgery. In this work, we introduce a solution for intra-operative ultrasound-CT registration of bones. We have developed methods for fully automatic real-time bone detection in ultrasound images and global automatic registration to CT. The bone detection algorithm uses a novel bone-specific feature descriptor and was thoroughly evaluated on both in-vivo and ex-vivo data. A global optimization strategy aligns the bone surface, followed by a soft tissue aware intensity-based registration to provide higher local registration accuracy. We evaluated the system on femur, tibia and fibula anatomy in a cadaver study with human legs, where magnetically tracked bone markers were implanted to yield ground truth information. An overall median system error of 3.7 mm was achieved on 11 datasets. Global and fully automatic registration of bones aquired with ultrasound to CT is feasible, with bone detection and tracking operating in real time for immediate feedback to the surgeon.

SKYWARD: the next generation airborne infrared search and track

NASA Astrophysics Data System (ADS)

Fortunato, L.; Colombi, G.; Ondini, A.; Quaranta, C.; Giunti, C.; Sozzi, B.; Balzarotti, G.

2016-05-01

Infrared Search and Track systems are an essential element of the modern and future combat aircrafts. Passive automatic search, detection and tracking functions, are key points for silent operations or jammed tactical scenarios. SKYWARD represents the latest evolution of IRST technology in which high quality electro-optical components, advanced algorithms, efficient hardware and software solutions are harmonically integrated to provide high-end affordable performances. Additionally, the reduction of critical opto-mechanical elements optimises weight and volume and increases the overall reliability. Multiple operative modes dedicated to different situations are available; many options can be selected among multiple or single target tracking, for surveillance or engagement, and imaging, for landing or navigation aid, assuring the maximum system flexibility. The high quality 2D-IR sensor is exploited by multiple parallel processing chains, based on linear and non-linear techniques, to extract the possible targets from background, in different conditions, with false alarm rate control. A widely tested track processor manages a large amount of candidate targets simultaneously and allows discriminating real targets from noise whilst operating with low target to background contrasts. The capability of providing reliable passive range estimation is an additional qualifying element of the system. Particular care has been dedicated to the detector non-uniformities, a possible limiting factor for distant targets detection, as well as to the design of the electro-optics for a harsh airborne environment. The system can be configured for LWIR or MWIR waveband according to the customer operational requirements. An embedded data recorder saves all the necessary images and data for mission debriefing, particularly useful during inflight system integration and tuning.

AssayR: A Simple Mass Spectrometry Software Tool for Targeted Metabolic and Stable Isotope Tracer Analyses.

PubMed

Wills, Jimi; Edwards-Hicks, Joy; Finch, Andrew J

2017-09-19

Metabolic analyses generally fall into two classes: unbiased metabolomic analyses and analyses that are targeted toward specific metabolites. Both techniques have been revolutionized by the advent of mass spectrometers with detectors that afford high mass accuracy and resolution, such as time-of-flights (TOFs) and Orbitraps. One particular area where this technology is key is in the field of metabolic flux analysis because the resolution of these spectrometers allows for discrimination between 13 C-containing isotopologues and those containing 15 N or other isotopes. While XCMS-based software is freely available for untargeted analysis of mass spectrometric data sets, it does not always identify metabolites of interest in a targeted assay. Furthermore, there is a paucity of vendor-independent software that deals with targeted analyses of metabolites and of isotopologues in particular. Here, we present AssayR, an R package that takes high resolution wide-scan liquid chromatography-mass spectrometry (LC-MS) data sets and tailors peak detection for each metabolite through a simple, iterative user interface. It automatically integrates peak areas for all isotopologues and outputs extracted ion chromatograms (EICs), absolute and relative stacked bar charts for all isotopologues, and a .csv data file. We demonstrate several examples where AssayR provides more accurate and robust quantitation than XCMS, and we propose that tailored peak detection should be the preferred approach for targeted assays. In summary, AssayR provides easy and robust targeted metabolite and stable isotope analyses on wide-scan data sets from high resolution mass spectrometers.

AssayR: A Simple Mass Spectrometry Software Tool for Targeted Metabolic and Stable Isotope Tracer Analyses

PubMed Central

2017-01-01

Metabolic analyses generally fall into two classes: unbiased metabolomic analyses and analyses that are targeted toward specific metabolites. Both techniques have been revolutionized by the advent of mass spectrometers with detectors that afford high mass accuracy and resolution, such as time-of-flights (TOFs) and Orbitraps. One particular area where this technology is key is in the field of metabolic flux analysis because the resolution of these spectrometers allows for discrimination between 13C-containing isotopologues and those containing 15N or other isotopes. While XCMS-based software is freely available for untargeted analysis of mass spectrometric data sets, it does not always identify metabolites of interest in a targeted assay. Furthermore, there is a paucity of vendor-independent software that deals with targeted analyses of metabolites and of isotopologues in particular. Here, we present AssayR, an R package that takes high resolution wide-scan liquid chromatography–mass spectrometry (LC-MS) data sets and tailors peak detection for each metabolite through a simple, iterative user interface. It automatically integrates peak areas for all isotopologues and outputs extracted ion chromatograms (EICs), absolute and relative stacked bar charts for all isotopologues, and a .csv data file. We demonstrate several examples where AssayR provides more accurate and robust quantitation than XCMS, and we propose that tailored peak detection should be the preferred approach for targeted assays. In summary, AssayR provides easy and robust targeted metabolite and stable isotope analyses on wide-scan data sets from high resolution mass spectrometers. PMID:28850215

Long Term Suboxone™ Emotional Reactivity As Measured by Automatic Detection in Speech

PubMed Central

Hill, Edward; Han, David; Dumouchel, Pierre; Dehak, Najim; Quatieri, Thomas; Moehs, Charles; Oscar-Berman, Marlene; Giordano, John; Simpatico, Thomas; Blum, Kenneth

2013-01-01

Addictions to illicit drugs are among the nation’s most critical public health and societal problems. The current opioid prescription epidemic and the need for buprenorphine/naloxone (Suboxone®; SUBX) as an opioid maintenance substance, and its growing street diversion provided impetus to determine affective states (“true ground emotionality”) in long-term SUBX patients. Toward the goal of effective monitoring, we utilized emotion-detection in speech as a measure of “true” emotionality in 36 SUBX patients compared to 44 individuals from the general population (GP) and 33 members of Alcoholics Anonymous (AA). Other less objective studies have investigated emotional reactivity of heroin, methadone and opioid abstinent patients. These studies indicate that current opioid users have abnormal emotional experience, characterized by heightened response to unpleasant stimuli and blunted response to pleasant stimuli. However, this is the first study to our knowledge to evaluate “true ground” emotionality in long-term buprenorphine/naloxone combination (Suboxone™). We found in long-term SUBX patients a significantly flat affect (p<0.01), and they had less self-awareness of being happy, sad, and anxious compared to both the GP and AA groups. We caution definitive interpretation of these seemingly important results until we compare the emotional reactivity of an opioid abstinent control using automatic detection in speech. These findings encourage continued research strategies in SUBX patients to target the specific brain regions responsible for relapse prevention of opioid addiction. PMID:23874860

Automatic guidance of attention during real-world visual search.

PubMed

Seidl-Rathkopf, Katharina N; Turk-Browne, Nicholas B; Kastner, Sabine

2015-08-01

Looking for objects in cluttered natural environments is a frequent task in everyday life. This process can be difficult, because the features, locations, and times of appearance of relevant objects often are not known in advance. Thus, a mechanism by which attention is automatically biased toward information that is potentially relevant may be helpful. We tested for such a mechanism across five experiments by engaging participants in real-world visual search and then assessing attentional capture for information that was related to the search set but was otherwise irrelevant. Isolated objects captured attention while preparing to search for objects from the same category embedded in a scene, as revealed by lower detection performance (Experiment 1A). This capture effect was driven by a central processing bottleneck rather than the withdrawal of spatial attention (Experiment 1B), occurred automatically even in a secondary task (Experiment 2A), and reflected enhancement of matching information rather than suppression of nonmatching information (Experiment 2B). Finally, attentional capture extended to objects that were semantically associated with the target category (Experiment 3). We conclude that attention is efficiently drawn towards a wide range of information that may be relevant for an upcoming real-world visual search. This mechanism may be adaptive, allowing us to find information useful for our behavioral goals in the face of uncertainty.

Automatic data processing and analysis system for monitoring region around a planned nuclear power plant

NASA Astrophysics Data System (ADS)

Kortström, Jari; Tiira, Timo; Kaisko, Outi

2016-03-01

The Institute of Seismology of University of Helsinki is building a new local seismic network, called OBF network, around planned nuclear power plant in Northern Ostrobothnia, Finland. The network will consist of nine new stations and one existing station. The network should be dense enough to provide azimuthal coverage better than 180° and automatic detection capability down to ML -0.1 within a radius of 25 km from the site.The network construction work began in 2012 and the first four stations started operation at the end of May 2013. We applied an automatic seismic signal detection and event location system to a network of 13 stations consisting of the four new stations and the nearest stations of Finnish and Swedish national seismic networks. Between the end of May and December 2013 the network detected 214 events inside the predefined area of 50 km radius surrounding the planned nuclear power plant site. Of those detections, 120 were identified as spurious events. A total of 74 events were associated with known quarries and mining areas. The average location error, calculated as a difference between the announced location from environment authorities and companies and the automatic location, was 2.9 km. During the same time period eight earthquakes between magnitude range 0.1-1.0 occurred within the area. Of these seven could be automatically detected. The results from the phase 1 stations of the OBF network indicates that the planned network can achieve its goals.

A new airborne laser rangefinder dynamic target simulator for non-stationary environment

NASA Astrophysics Data System (ADS)

Ma, Pengge; Pang, Dongdong; Yi, Yang

2017-11-01

For the non-stationary environment simulation in laser range finder product testing, a new dynamic target simulation system is studied. First of all, the three-pulsed laser ranging principle, laser target signal composition and mathematical representation are introduced. Then, the actual nonstationary working environment of laser range finder is analyzed, and points out that the real sunshine background light clutter and target shielding effect in laser echo become the main influencing factors. After that, the dynamic laser target signal simulation method is given. Eventlly, the implementation of automatic test system based on arbitrary waveform generator is described. Practical application shows that the new echo signal automatic test system can simulate the real laser ranging environment of laser range finder, and is suitable for performance test of products.

Simulation and ground testing with the Advanced Video Guidance Sensor

NASA Technical Reports Server (NTRS)

Howard, Richard T.; Johnston, Albert S.; Bryan, Thomas C.; Book, Michael L.

2005-01-01

The Advanced Video Guidance Sensor (AVGS), an active sensor system that provides near-range 6-degree-of-freedom sensor data, has been developed as part of an automatic rendezvous and docking system for the Demonstration of Autonomous Rendezvous Technology (DART). The sensor determines the relative positions and attitudes between the active sensor and the passive target at ranges up to 300 meters. The AVGS uses laser diodes to illuminate retro-reflectors in the target, a solid-state imager to detect the light returned from the target, and image capture electronics and a digital signal processor to convert the video information into the relative positions and attitudes. The development of the sensor, through initial prototypes, final prototypes, and three flight units, has required a great deal of testing at every phase, and the different types of testing, their effectiveness, and their results, are presented in this paper, focusing on the testing of the flight units. Testing has improved the sensor's performance.

Square tracking sensor for autonomous helicopter hover stabilization

NASA Astrophysics Data System (ADS)

Oertel, Carl-Henrik

1995-06-01

Sensors for synthetic vision are needed to extend the mission profiles of helicopters. A special task for various applications is the autonomous position hold of a helicopter above a ground fixed or moving target. As a proof of concept for a general synthetic vision solution a restricted machine vision system, which is capable of locating and tracking a special target, was developed by the Institute of Flight Mechanics of Deutsche Forschungsanstalt fur Luft- und Raumfahrt e.V. (i.e., German Aerospace Research Establishment). This sensor, which is specialized to detect and track a square, was integrated in the fly-by-wire helicopter ATTHeS (i.e., Advanced Technology Testing Helicopter System). An existing model following controller for the forward flight condition was adapted for the hover and low speed requirements of the flight vehicle. The special target, a black square with a length of one meter, was mounted on top of a car. Flight tests demonstrated the automatic stabilization of the helicopter above the moving car by synthetic vision.

Evaluation of the maximum-likelihood adaptive neural system (MLANS) applications to noncooperative IFF

NASA Astrophysics Data System (ADS)

Chernick, Julian A.; Perlovsky, Leonid I.; Tye, David M.

1994-06-01

This paper describes applications of maximum likelihood adaptive neural system (MLANS) to the characterization of clutter in IR images and to the identification of targets. The characterization of image clutter is needed to improve target detection and to enhance the ability to compare performance of different algorithms using diverse imagery data. Enhanced unambiguous IFF is important for fratricide reduction while automatic cueing and targeting is becoming an ever increasing part of operations. We utilized MLANS which is a parametric neural network that combines optimal statistical techniques with a model-based approach. This paper shows that MLANS outperforms classical classifiers, the quadratic classifier and the nearest neighbor classifier, because on the one hand it is not limited to the usual Gaussian distribution assumption and can adapt in real time to the image clutter distribution; on the other hand MLANS learns from fewer samples and is more robust than the nearest neighbor classifiers. Future research will address uncooperative IFF using fused IR and MMW data.

Stereotypical rapid source level regulation in the harbour porpoise biosonar

NASA Astrophysics Data System (ADS)

Linnenschmidt, Meike; Kloepper, Laura N.; Wahlberg, Magnus; Nachtigall, Paul E.

2012-09-01

Some odontocetes and bats vary both click intensity and receiver sensitivity during echolocation, depending on target range. It is not known how this so-called automatic gain control is regulated by the animal. The source level of consecutive echolocation clicks from a harbour porpoise was measured with a hydrophone array while the animal detected an aluminium cylinder at 2, 4 or 8 m distance in a go/no-go paradigm. On-axis clicks had source levels of 145-174 dB re 1 μPa peak-to-peak. During target-present trials the click trains reached comparable source levels independent of the range to the target after three clicks. After an additional click, the source level was reduced for the 2 and 4 m trials until it equalled the one-way transmission loss. During target-absent trials, the source level remained high throughout the entire click train. Given typical values of harbour porpoise inter-click intervals, the source level reduction commenced within a few 100 ms from the first click in the click train. This may indicate a sub-cortically regulated source level regulation in the harbour porpoise.

A piezo-ring-on-chip microfluidic device for simple and low-cost mass spectrometry interfacing.

PubMed

Tsao, Chia-Wen; Lei, I-Chao; Chen, Pi-Yu; Yang, Yu-Liang

2018-02-12

Mass spectrometry (MS) interfacing technology provides the means for incorporating microfluidic processing with post MS analysis. In this study, we propose a simple piezo-ring-on-chip microfluidic device for the controlled spraying of MALDI-MS targets. This device uses a low-cost, commercially-available ring-shaped piezoelectric acoustic atomizer (piezo-ring) directly integrated into a polydimethylsiloxane microfluidic device to spray the sample onto the MS target substrate. The piezo-ring-on-chip microfluidic device's design, fabrication, and actuation, and its pulsatile pumping effects were evaluated. The spraying performance was examined by depositing organic matrix samples onto the MS target substrate by using both an automatic linear motion motor, and manual deposition. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) was performed to analyze the peptide samples on the MALDI target substrates. Using our technique, model peptides with 10 -6 M concentration can be successfully detected. The results also indicate that the piezo-ring-on-chip approach forms finer matrix crystals and presents better MS signal uniformity with little sample consumption compared to the conventional pipetting method.

Automatic Docking System Sensor Design, Test, and Mission Performance

NASA Technical Reports Server (NTRS)

Jackson, John L.; Howard, Richard T.; Cole, Helen J.

1998-01-01

The Video Guidance Sensor is a key element of an automatic rendezvous and docking program administered by NASA that was flown on STS-87 in November of 1997. The system used laser illumination of a passive target in the field of view of an on-board camera and processed the video image to determine the relative position and attitude between the target and the sensor. Comparisons of mission results with theoretical models and laboratory measurements will be discussed.

A Flexible Workflow for Automated Bioluminescent Kinase Selectivity Profiling.

PubMed

Worzella, Tracy; Butzler, Matt; Hennek, Jacquelyn; Hanson, Seth; Simdon, Laura; Goueli, Said; Cowan, Cris; Zegzouti, Hicham

2017-04-01

Kinase profiling during drug discovery is a necessary process to confirm inhibitor selectivity and assess off-target activities. However, cost and logistical limitations prevent profiling activities from being performed in-house. We describe the development of an automated and flexible kinase profiling workflow that combines ready-to-use kinase enzymes and substrates in convenient eight-tube strips, a bench-top liquid handling device, ADP-Glo Kinase Assay (Promega, Madison, WI) technology to quantify enzyme activity, and a multimode detection instrument. Automated methods were developed for kinase reactions and quantification reactions to be assembled on a Gilson (Middleton, WI) PIPETMAX, following standardized plate layouts for single- and multidose compound profiling. Pipetting protocols were customized at runtime based on user-provided information, including compound number, increment for compound titrations, and number of kinase families to use. After the automated liquid handling procedures, a GloMax Discover (Promega) microplate reader preloaded with SMART protocols was used for luminescence detection and automatic data analysis. The functionality of the automated workflow was evaluated with several compound-kinase combinations in single-dose or dose-response profiling formats. Known target-specific inhibitions were confirmed. Novel small molecule-kinase interactions, including off-target inhibitions, were identified and confirmed in secondary studies. By adopting this streamlined profiling process, researchers can quickly and efficiently profile compounds of interest on site.

Automatic graphene transfer system for improved material quality and efficiency

PubMed Central

Boscá, Alberto; Pedrós, Jorge; Martínez, Javier; Palacios, Tomás; Calle, Fernando

2016-01-01

In most applications based on chemical vapor deposition (CVD) graphene, the transfer from the growth to the target substrate is a critical step for the final device performance. Manual procedures are time consuming and depend on handling skills, whereas existing automatic roll-to-roll methods work well for flexible substrates but tend to induce mechanical damage in rigid ones. A new system that automatically transfers CVD graphene to an arbitrary target substrate has been developed. The process is based on the all-fluidic manipulation of the graphene to avoid mechanical damage, strain and contamination, and on the combination of capillary action and electrostatic repulsion between the graphene and its container to ensure a centered sample on top of the target substrate. The improved carrier mobility and yield of the automatically transferred graphene, as compared to that manually transferred, is demonstrated by the optical and electrical characterization of field-effect transistors fabricated on both materials. In particular, 70% higher mobility values, with a 30% decrease in the unintentional doping and a 10% strain reduction are achieved. The system has been developed for lab-scale transfer and proved to be scalable for industrial applications. PMID:26860260

Matched filter based detection of floating mines in IR spacetime

NASA Astrophysics Data System (ADS)

Borghgraef, Alexander; Lapierre, Fabian; Philips, Wilfried; Acheroy, Marc

2009-09-01

Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways during the first Gulf War, but applications include salvaging,search-and-rescue and perimeter or harbour defense. IR video was chosen for its day-and-night imaging capability, and its availability on military vessels. Detection is difficult because a rough sea is seen as a dynamic background of moving objects with size order, shape and temperature similar to those of the floating mine. We do find a determinant characteristic in the target's periodic motion, which differs from that of the propagating surface waves composing the background. The classical detection and tracking approaches give bad results when applied to this problem. While background detection algorithms assume a quasi-static background, the sea surface is actually very dynamic, causing this category of algorithms to fail. Kalman or particle filter algorithms on the other hand, which stress temporal coherence, suffer from tracking loss due to occlusions and the great noise level of the image. We propose an innovative approach. This approach uses the periodicity of the objects movement and thus its temporal coherence. The principle is to consider the video data as a spacetime volume similar to a hyperspectral data cube by replacing the spectral axis with a temporal axis. We can then apply algorithms developed for hyperspectral detection problems to the detection of small floating objects. We treat the detection problem using multilinear algebra, designing a number of finite impulse response filters (FIR) maximizing the target response. The algorithm was applied to test footage of practice mines in the infrared.

Terrain Commander: a next-generation remote surveillance system

NASA Astrophysics Data System (ADS)

Finneral, Henry J.

2003-09-01

Terrain Commander is a fully automated forward observation post that provides the most advanced capability in surveillance and remote situational awareness. The Terrain Commander system was selected by the Australian Government for its NINOX Phase IIB Unattended Ground Sensor Program with the first systems delivered in August of 2002. Terrain Commander offers next generation target detection using multi-spectral peripheral sensors coupled with autonomous day/night image capture and processing. Subsequent intelligence is sent back through satellite communications with unlimited range to a highly sophisticated central monitoring station. The system can "stakeout" remote locations clandestinely for 24 hours a day for months at a time. With its fully integrated SATCOM system, almost any site in the world can be monitored from virtually any other location in the world. Terrain Commander automatically detects and discriminates intruders by precisely cueing its advanced EO subsystem. The system provides target detection capabilities with minimal nuisance alarms combined with the positive visual identification that authorities demand before committing a response. Terrain Commander uses an advanced beamforming acoustic sensor and a distributed array of seismic, magnetic and passive infrared sensors to detect, capture images and accurately track vehicles and personnel. Terrain Commander has a number of emerging military and non-military applications including border control, physical security, homeland defense, force protection and intelligence gathering. This paper reviews the development, capabilities and mission applications of the Terrain Commander system.

VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls

PubMed Central

Kim, Byoungjip; Kang, Seungwoo; Ha, Jin-Young; Song, Junehwa

2015-01-01

In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user’s place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense. PMID:26193275

TU-H-CAMPUS-JeP1-02: Fully Automatic Verification of Automatically Contoured Normal Tissues in the Head and Neck

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

McCarroll, R; UT Health Science Center, Graduate School of Biomedical Sciences, Houston, TX; Beadle, B

Purpose: To investigate and validate the use of an independent deformable-based contouring algorithm for automatic verification of auto-contoured structures in the head and neck towards fully automated treatment planning. Methods: Two independent automatic contouring algorithms [(1) Eclipse’s Smart Segmentation followed by pixel-wise majority voting, (2) an in-house multi-atlas based method] were used to create contours of 6 normal structures of 10 head-and-neck patients. After rating by a radiation oncologist, the higher performing algorithm was selected as the primary contouring method, the other used for automatic verification of the primary. To determine the ability of the verification algorithm to detect incorrectmore » contours, contours from the primary method were shifted from 0.5 to 2cm. Using a logit model the structure-specific minimum detectable shift was identified. The models were then applied to a set of twenty different patients and the sensitivity and specificity of the models verified. Results: Per physician rating, the multi-atlas method (4.8/5 point scale, with 3 rated as generally acceptable for planning purposes) was selected as primary and the Eclipse-based method (3.5/5) for verification. Mean distance to agreement and true positive rate were selected as covariates in an optimized logit model. These models, when applied to a group of twenty different patients, indicated that shifts could be detected at 0.5cm (brain), 0.75cm (mandible, cord), 1cm (brainstem, cochlea), or 1.25cm (parotid), with sensitivity and specificity greater than 0.95. If sensitivity and specificity constraints are reduced to 0.9, detectable shifts of mandible and brainstem were reduced by 0.25cm. These shifts represent additional safety margins which might be considered if auto-contours are used for automatic treatment planning without physician review. Conclusion: Automatically contoured structures can be automatically verified. This fully automated process could be used to flag auto-contours for special review or used with safety margins in a fully automatic treatment planning system.« less

Support vector machine for automatic pain recognition

NASA Astrophysics Data System (ADS)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

Detection of small surface vessels in near, medium, and far infrared spectral bands

NASA Astrophysics Data System (ADS)

Dulski, R.; Milewski, S.; Kastek, M.; Trzaskawka, P.; Szustakowski, M.; Ciurapinski, W.; Zyczkowski, M.

2011-11-01

Protection of naval bases and harbors requires close co-operation between security and access control systems covering land areas and those monitoring sea approach routes. The typical location of naval bases and harbors - usually next to a large city - makes it difficult to detect and identify a threat in the dense regular traffic of various sea vessels (i.e. merchant ships, fishing boats, tourist ships). Due to the properties of vessel control systems, such as AIS (Automatic Identification System), and the effectiveness of radar and optoelectronic systems against different targets it seems that fast motor boats called RIB (Rigid Inflatable Boat) could be the most serious threat to ships and harbor infrastructure. In the paper the process and conditions for the detection and identification of high-speed boats in the areas of ports and naval bases in the near, medium and far infrared is presented. Based on the results of measurements and recorded thermal images the actual temperature contrast delta T (RIB / sea) will be determined, which will further allow to specify the theoretical ranges of detection and identification of the RIB-type targets for an operating security system. The data will also help to determine the possible advantages of image fusion where the component images are taken in different spectral ranges. This will increase the probability of identifying the object by the multi-sensor security system equipped additionally with the appropriate algorithms for detecting, tracking and performing the fusion of images from the visible and infrared cameras.

Automatic Co-Registration of QuickBird Data for Change Detection Applications

NASA Technical Reports Server (NTRS)

Bryant, Nevin A.; Logan, Thomas L.; Zobrist, Albert L.

2006-01-01

This viewgraph presentation reviews the use Automatic Fusion of Image Data System (AFIDS) for Automatic Co-Registration of QuickBird Data to ascertain if changes have occurred in images. The process is outlined, and views from Iraq and Los Angelels are shown to illustrate the process.

A Multiple Sensor Machine Vision System for Automatic Hardwood Feature Detection

Treesearch

D. Earl Kline; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman; Robert L. Brisbin

1993-01-01

A multiple sensor machine vision prototype is being developed to scan full size hardwood lumber at industrial speeds for automatically detecting features such as knots holes, wane, stain, splits, checks, and color. The prototype integrates a multiple sensor imaging system, a materials handling system, a computer system, and application software. The prototype provides...

Volitional and automatic control of the hand when reaching to grasp objects.

PubMed

Chen, Zhongting; Saunders, Jeffrey Allen

2018-06-01

When picking up an object, we tend to grasp at contact points that allow a stable grip. Recent studies have demonstrated that appropriate grasp points can be selected during an ongoing movement in response to unexpected perturbations of the target object. In this study, we tested whether such online grip adjustments are automatic responses or can be controlled volitionally. Subjects performed virtual grasping movements toward target 2D shapes that sometimes changed shape or orientation during movement. Unlike in previous studies, the conditions and task requirements discouraged any online adjustments toward the perturbed shapes. In Experiment 1, target shapes were perturbed briefly (200 ms) during movement before reverting to the original shape, and subjects were instructed to ignore the transient perturbations. Despite subjects' intentions, we observed online adjustments of grip orientation that were toward the expected grip axis of the briefly presented shape. In Experiment 2, we added a stop-signal to the grasping task, with target perturbation as the stop cue. We again observed unnecessary online adjustments toward the grip axis of the perturbed shape, with similar latency. Furthermore, the grip adjustments continued after the forward motion of the hand had stopped, indicating that the automatic response to the perturbed target shape co-occurred with the volitional response to the perturbation onset. Our results provide evidence that automatic control mechanisms are used to guide the fingers to appropriate grasp points and suggest that these mechanisms are distinct from those involved with volitional control. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Investigation of measureable parameters that correlate with automatic target recognition performance in synthetic aperture sonar

NASA Astrophysics Data System (ADS)

Gazagnaire, Julia; Cobb, J. T.; Isaacs, Jason

2015-05-01

There is a desire in the Mine Counter Measure community to develop a systematic method to predict and/or estimate the performance of Automatic Target Recognition (ATR) algorithms that are detecting and classifying mine-like objects within sonar data. Ideally, parameters exist that can be measured directly from the sonar data that correlate with ATR performance. In this effort, two metrics were analyzed for their predictive potential using high frequency synthetic aperture sonar (SAS) images. The first parameter is a measure of contrast. It is essentially the variance in pixel intensity over a fixed partition of relatively small size. An analysis was performed to determine the optimum block size for this contrast calculation. These blocks were then overlapped in the horizontal and vertical direction over the entire image. The second parameter is the one-dimensional K-shape parameter. The K-distribution is commonly used to describe sonar backscatter return from range cells that contain a finite number of scatterers. An Ada-Boosted Decision Tree classifier was used to calculate the probability of classification (Pc) and false alarm rate (FAR) for several types of targets in SAS images from three different data sets. ROC curves as a function of the measured parameters were generated and the correlation between the measured parameters in the vicinity of each of the contacts and the ATR performance was investigated. The contrast and K-shape parameters were considered separately. Additionally, the contrast and K-shape parameter were associated with background texture types using previously labeled high frequency SAS images.

Automatic Registration of Terrestrial Laser Scanner Point Clouds Using Natural Planar Surfaces

NASA Astrophysics Data System (ADS)

Theiler, P. W.; Schindler, K.

2012-07-01

Terrestrial laser scanners have become a standard piece of surveying equipment, used in diverse fields like geomatics, manufacturing and medicine. However, the processing of today's large point clouds is time-consuming, cumbersome and not automated enough. A basic step of post-processing is the registration of scans from different viewpoints. At present this is still done using artificial targets or tie points, mostly by manual clicking. The aim of this registration step is a coarse alignment, which can then be improved with the existing algorithm for fine registration. The focus of this paper is to provide such a coarse registration in a fully automatic fashion, and without placing any target objects in the scene. The basic idea is to use virtual tie points generated by intersecting planar surfaces in the scene. Such planes are detected in the data with RANSAC and optimally fitted using least squares estimation. Due to the huge amount of recorded points, planes can be determined very accurately, resulting in well-defined tie points. Given two sets of potential tie points recovered in two different scans, registration is performed by searching for the assignment which preserves the geometric configuration of the largest possible subset of all tie points. Since exhaustive search over all possible assignments is intractable even for moderate numbers of points, the search is guided by matching individual pairs of tie points with the help of a novel descriptor based on the properties of a point's parent planes. Experiments show that the proposed method is able to successfully coarse register TLS point clouds without the need for artificial targets.

Robust vehicle detection under various environmental conditions using an infrared thermal camera and its application to road traffic flow monitoring.

PubMed

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2013-06-17

We have already proposed a method for detecting vehicle positions and their movements (henceforth referred to as "our previous method") using thermal images taken with an infrared thermal camera. Our experiments have shown that our previous method detects vehicles robustly under four different environmental conditions which involve poor visibility conditions in snow and thick fog. Our previous method uses the windshield and its surroundings as the target of the Viola-Jones detector. Some experiments in winter show that the vehicle detection accuracy decreases because the temperatures of many windshields approximate those of the exterior of the windshields. In this paper, we propose a new vehicle detection method (henceforth referred to as "our new method"). Our new method detects vehicles based on tires' thermal energy reflection. We have done experiments using three series of thermal images for which the vehicle detection accuracies of our previous method are low. Our new method detects 1,417 vehicles (92.8%) out of 1,527 vehicles, and the number of false detection is 52 in total. Therefore, by combining our two methods, high vehicle detection accuracies are maintained under various environmental conditions. Finally, we apply the traffic information obtained by our two methods to traffic flow automatic monitoring, and show the effectiveness of our proposal.

Automatic detection and notification of "wrong patient-wrong location'' errors in the operating room.

PubMed

Sandberg, Warren S; Häkkinen, Matti; Egan, Marie; Curran, Paige K; Fairbrother, Pamela; Choquette, Ken; Daily, Bethany; Sarkka, Jukka-Pekka; Rattner, David

2005-09-01

When procedures and processes to assure patient location based on human performance do not work as expected, patients are brought incrementally closer to a possible "wrong patient-wrong procedure'' error. We developed a system for automated patient location monitoring and management. Real-time data from an active infrared/radio frequency identification tracking system provides patient location data that are robust and can be compared with an "expected process'' model to automatically flag wrong-location events as soon as they occur. The system also generates messages that are automatically sent to process managers via the hospital paging system, thus creating an active alerting function to annunciate errors. We deployed the system to detect and annunciate "patient-in-wrong-OR'' events. The system detected all "wrong-operating room (OR)'' events, and all "wrong-OR'' locations were correctly assigned within 0.50+/-0.28 minutes (mean+/-SD). This corresponded to the measured latency of the tracking system. All wrong-OR events were correctly annunciated via the paging function. This experiment demonstrates that current technology can automatically collect sufficient data to remotely monitor patient flow through a hospital, provide decision support based on predefined rules, and automatically notify stakeholders of errors.

Toward a noninvasive automatic seizure control system in rats with transcranial focal stimulations via tripolar concentric ring electrodes

PubMed Central

Makeyev, Oleksandr; Liu, Xiang; Luna-Munguía, Hiram; Rogel-Salazar, Gabriela; Mucio-Ramirez, Samuel; Liu, Yuhong; Sun, Yan L.; Kay, Steven M.; Besio, Walter G.

2012-01-01

Epilepsy affects approximately one percent of the world population. Antiepileptic drugs are ineffective in approximately 30% of patients and have side effects. We are developing a noninvasive, or minimally invasive, transcranial focal electrical stimulation system through our novel tripolar concentric ring electrodes to control seizures. In this study we demonstrate feasibility of an automatic seizure control system in rats with pentylenetetrazole-induced seizures through single and multiple stimulations. These stimulations are automatically triggered by a real-time electrographic seizure activity detector based on a disjunctive combination of detections from a cumulative sum algorithm and a generalized likelihood ratio test. An average seizure onset detection accuracy of 76.14% was obtained for the test set (n = 13). Detection of electrographic seizure activity was accomplished in advance of the early behavioral seizure activity in 76.92% of the cases. Automatically triggered stimulation significantly (p = 0.001) reduced the electrographic seizure activity power in the once stimulated group compared to controls in 70% of the cases. To the best of our knowledge this is the first closed-loop automatic seizure control system based on noninvasive electrical brain stimulation using tripolar concentric ring electrode electrographic seizure activity as feedback. PMID:22772373

Toward a noninvasive automatic seizure control system in rats with transcranial focal stimulations via tripolar concentric ring electrodes.

PubMed

Makeyev, Oleksandr; Liu, Xiang; Luna-Munguía, Hiram; Rogel-Salazar, Gabriela; Mucio-Ramirez, Samuel; Liu, Yuhong; Sun, Yan L; Kay, Steven M; Besio, Walter G

2012-07-01

Epilepsy affects approximately 1% of the world population. Antiepileptic drugs are ineffective in approximately 30% of patients and have side effects. We are developing a noninvasive, or minimally invasive, transcranial focal electrical stimulation system through our novel tripolar concentric ring electrodes to control seizures. In this study, we demonstrate feasibility of an automatic seizure control system in rats with pentylenetetrazole-induced seizures through single and multiple stimulations. These stimulations are automatically triggered by a real-time electrographic seizure activity detector based on a disjunctive combination of detections from a cumulative sum algorithm and a generalized likelihood ratio test. An average seizure onset detection accuracy of 76.14% was obtained for the test set (n = 13). Detection of electrographic seizure activity was accomplished in advance of the early behavioral seizure activity in 76.92% of the cases. Automatically triggered stimulation significantly (p = 0.001) reduced the electrographic seizure activity power in the once stimulated group compared to controls in 70% of the cases. To the best of our knowledge this is the first closed-loop automatic seizure control system based on noninvasive electrical brain stimulation using tripolar concentric ring electrode electrographic seizure activity as feedback.

The advanced linked extended reconnaissance and targeting technology demonstration project

NASA Astrophysics Data System (ADS)

Cruickshank, James; de Villers, Yves; Maheux, Jean; Edwards, Mark; Gains, David; Rea, Terry; Banbury, Simon; Gauthier, Michelle

2007-06-01

The Advanced Linked Extended Reconnaissance & Targeting (ALERT) Technology Demonstration (TD) project is addressing key operational needs of the future Canadian Army's Surveillance and Reconnaissance forces by fusing multi-sensor and tactical data, developing automated processes, and integrating beyond line-of-sight sensing. We discuss concepts for displaying and fusing multi-sensor and tactical data within an Enhanced Operator Control Station (EOCS). The sensor data can originate from the Coyote's own visible-band and IR cameras, laser rangefinder, and ground-surveillance radar, as well as beyond line-of-sight systems such as a mini-UAV and unattended ground sensors. The authors address technical issues associated with the use of fully digital IR and day video cameras and discuss video-rate image processing developed to assist the operator to recognize poorly visible targets. Automatic target detection and recognition algorithms processing both IR and visible-band images have been investigated to draw the operator's attention to possible targets. The machine generated information display requirements are presented with the human factors engineering aspects of the user interface in this complex environment, with a view to establishing user trust in the automation. The paper concludes with a summary of achievements to date and steps to project completion.

Automatic detection of DNA double strand breaks after irradiation using an γH2AX assay.

PubMed

Hohmann, Tim; Kessler, Jacqueline; Grabiec, Urszula; Bache, Matthias; Vordermark, Dyrk; Dehghani, Faramarz

2018-05-01

Radiation therapy belongs to the most common approaches for cancer therapy leading amongst others to DNA damage like double strand breaks (DSB). DSB can be used as a marker for the effect of radiation on cells. For visualization and assessing the extent of DNA damage the γH2AX foci assay is frequently used. The analysis of the γH2AX foci assay remains complicated as the number of γH2AX foci has to be counted. The quantification is mostly done manually, being time consuming and leading to person-dependent variations. Therefore, we present a method to automatically analyze the number of foci inside nuclei, facilitating and quickening the analysis of DSBs with high reliability in fluorescent images. First nuclei were detected in fluorescent images. Afterwards, the nuclei were analyzed independently from each other with a local thresholding algorithm. This approach allowed accounting for different levels of noise and detection of the foci inside the respective nucleus, using Hough transformation searching for circles. The presented algorithm was able to correctly classify most foci in cases of "high" and "average" image quality (sensitivity>0.8) with a low rate of false positive detections (positive predictive value (PPV)>0.98). In cases of "low" image quality the approach had a decreased sensitivity (0.7-0.9), depending on the manual control counter. The PPV remained high (PPV>0.91). Compared to other automatic approaches the presented algorithm had a higher sensitivity and PPV. The used automatic foci detection algorithm was capable of detecting foci with high sensitivity and PPV. Thus it can be used for automatic analysis of images of varying quality.

Intra- and Inter-database Study for Arabic, English, and German Databases: Do Conventional Speech Features Detect Voice Pathology?

PubMed

Ali, Zulfiqar; Alsulaiman, Mansour; Muhammad, Ghulam; Elamvazuthi, Irraivan; Al-Nasheri, Ahmed; Mesallam, Tamer A; Farahat, Mohamed; Malki, Khalid H

2017-05-01

A large population around the world has voice complications. Various approaches for subjective and objective evaluations have been suggested in the literature. The subjective approach strongly depends on the experience and area of expertise of a clinician, and human error cannot be neglected. On the other hand, the objective or automatic approach is noninvasive. Automatic developed systems can provide complementary information that may be helpful for a clinician in the early screening of a voice disorder. At the same time, automatic systems can be deployed in remote areas where a general practitioner can use them and may refer the patient to a specialist to avoid complications that may be life threatening. Many automatic systems for disorder detection have been developed by applying different types of conventional speech features such as the linear prediction coefficients, linear prediction cepstral coefficients, and Mel-frequency cepstral coefficients (MFCCs). This study aims to ascertain whether conventional speech features detect voice pathology reliably, and whether they can be correlated with voice quality. To investigate this, an automatic detection system based on MFCC was developed, and three different voice disorder databases were used in this study. The experimental results suggest that the accuracy of the MFCC-based system varies from database to database. The detection rate for the intra-database ranges from 72% to 95%, and that for the inter-database is from 47% to 82%. The results conclude that conventional speech features are not correlated with voice, and hence are not reliable in pathology detection. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Blotch removal for old movie restoration using epitome analysis

NASA Astrophysics Data System (ADS)

Rashwan, Abdullah M.

2011-10-01

Automatic blotch removal in old movies is important in film restoration. Blotches are black or white spots randomly occurring along the movie frames. Removing these spots are obtained by first automatically detecting the blotches then interpolating them using the spatial and temporal information in current, succeeding, and preceding frames. In this paper, simplified Rank Order Detector (sROD) is used with tweaked parameters to over detect the blotches, Epitome Analysis is used for interpolating the detected blotches.

The Infrared Automatic Mass Screening (IRAMS) System For Printed Circuit Board Fault Detection

NASA Astrophysics Data System (ADS)

Hugo, Perry W.

1987-05-01

Office of the Program Manager for TMDE (OPM TMDE) has initiated a program to develop techniques for evaluating the performance of printed circuit boards (PCB's) using infrared thermal imaging. It is OPM TMDE's expectation that the standard thermal profile (STP) will become the basis for the future rapid automatic detection and isolation of gross failure mechanisms on units under test (UUT's). To accomplish this OPM TMDE has purchased two Infrared Automatic Mass Screening ( I RAMS) systems which are scheduled for delivery in 1987. The IRAMS system combines a high resolution infrared thermal imager with a test bench and diagnostic computer hardware and software. Its purpose is to rapidly and automatically compare the thermal profiles of a UUT with the STP of that unit, recalled from memory, in order to detect thermally responsive failure mechanisms in PCB's. This paper will review the IRAMS performance requirements, outline the plan for implementing the two systems and report on progress to date.

Automatic detection of cardiac cycle and measurement of the mitral annulus diameter in 4D TEE images

NASA Astrophysics Data System (ADS)

Graser, Bastian; Hien, Maximilian; Rauch, Helmut; Meinzer, Hans-Peter; Heimann, Tobias

2012-02-01

Mitral regurgitation is a wide spread problem. For successful surgical treatment quantification of the mitral annulus, especially its diameter, is essential. Time resolved 3D transesophageal echocardiography (TEE) is suitable for this task. Yet, manual measurement in four dimensions is extremely time consuming, which confirms the need for automatic quantification methods. The method we propose is capable of automatically detecting the cardiac cycle (systole or diastole) for each time step and measuring the mitral annulus diameter. This is done using total variation noise filtering, the graph cut segmentation algorithm and morphological operators. An evaluation took place using expert measurements on 4D TEE data of 13 patients. The cardiac cycle was detected correctly on 78% of all images and the mitral annulus diameter was measured with an average error of 3.08 mm. Its full automatic processing makes the method easy to use in the clinical workflow and it provides the surgeon with helpful information.

Development of an Automatic Testing Platform for Aviator's Night Vision Goggle Honeycomb Defect Inspection.

PubMed

Jian, Bo-Lin; Peng, Chao-Chung

2017-06-15

Due to the direct influence of night vision equipment availability on the safety of night-time aerial reconnaissance, maintenance needs to be carried out regularly. Unfortunately, some defects are not easy to observe or are not even detectable by human eyes. As a consequence, this study proposed a novel automatic defect detection system for aviator's night vision imaging systems AN/AVS-6(V)1 and AN/AVS-6(V)2. An auto-focusing process consisting of a sharpness calculation and a gradient-based variable step search method is applied to achieve an automatic detection system for honeycomb defects. This work also developed a test platform for sharpness measurement. It demonstrates that the honeycomb defects can be precisely recognized and the number of the defects can also be determined automatically during the inspection. Most importantly, the proposed approach significantly reduces the time consumption, as well as human assessment error during the night vision goggle inspection procedures.

Morphological self-organizing feature map neural network with applications to automatic target recognition

NASA Astrophysics Data System (ADS)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks.

PubMed

López-Linares, Karen; Aranjuelo, Nerea; Kabongo, Luis; Maclair, Gregory; Lete, Nerea; Ceresa, Mario; García-Familiar, Ainhoa; Macía, Iván; González Ballester, Miguel A

2018-05-01

Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation. The DetecNet detection network is adapted to perform region of interest extraction from a complete CTA and a new segmentation network architecture, based on Fully Convolutional Networks and a Holistically-Nested Edge Detection Network, is presented. These networks are trained, validated and tested in 13 post-operative CTA volumes of different patients using a 4-fold cross-validation approach to provide more robustness to the results. Our pipeline achieves a Dice score of more than 82% for post-operative thrombus segmentation and provides a mean relative volume difference between ground truth and automatic segmentation that lays within the experienced human observer variance without the need of human intervention in most common cases. Copyright © 2018 Elsevier B.V. All rights reserved.

ROBIN: a platform for evaluating automatic target recognition algorithms: I. Overview of the project and presentation of the SAGEM DS competition

NASA Astrophysics Data System (ADS)

Duclos, D.; Lonnoy, J.; Guillerm, Q.; Jurie, F.; Herbin, S.; D'Angelo, E.

2008-04-01

The last five years have seen a renewal of Automatic Target Recognition applications, mainly because of the latest advances in machine learning techniques. In this context, large collections of image datasets are essential for training algorithms as well as for their evaluation. Indeed, the recent proliferation of recognition algorithms, generally applied to slightly different problems, make their comparisons through clean evaluation campaigns necessary. The ROBIN project tries to fulfil these two needs by putting unclassified datasets, ground truths, competitions and metrics for the evaluation of ATR algorithms at the disposition of the scientific community. The scope of this project includes single and multi-class generic target detection and generic target recognition, in military and security contexts. From our knowledge, it is the first time that a database of this importance (several hundred thousands of visible and infrared hand annotated images) has been publicly released. Funded by the French Ministry of Defence (DGA) and by the French Ministry of Research, ROBIN is one of the ten Techno-vision projects. Techno-vision is a large and ambitious government initiative for building evaluation means for computer vision technologies, for various application contexts. ROBIN's consortium includes major companies and research centres involved in Computer Vision R&D in the field of defence: Bertin Technologies, CNES, ECA, DGA, EADS, INRIA, ONERA, MBDA, SAGEM, THALES. This paper, which first gives an overview of the whole project, is focused on one of ROBIN's key competitions, the SAGEM Defence Security database. This dataset contains more than eight hundred ground and aerial infrared images of six different vehicles in cluttered scenes including distracters. Two different sets of data are available for each target. The first set includes different views of each vehicle at close range in a "simple" background, and can be used to train algorithms. The second set contains many views of the same vehicle in different contexts and situations simulating operational scenarios.

Automated feature detection and identification in digital point-ordered signals

DOEpatents

Oppenlander, Jane E.; Loomis, Kent C.; Brudnoy, David M.; Levy, Arthur J.

1998-01-01

A computer-based automated method to detect and identify features in digital point-ordered signals. The method is used for processing of non-destructive test signals, such as eddy current signals obtained from calibration standards. The signals are first automatically processed to remove noise and to determine a baseline. Next, features are detected in the signals using mathematical morphology filters. Finally, verification of the features is made using an expert system of pattern recognition methods and geometric criteria. The method has the advantage that standard features can be, located without prior knowledge of the number or sequence of the features. Further advantages are that standard features can be differentiated from irrelevant signal features such as noise, and detected features are automatically verified by parameters extracted from the signals. The method proceeds fully automatically without initial operator set-up and without subjective operator feature judgement.

Automatic detection and severity measurement of eczema using image processing.

PubMed

Alam, Md Nafiul; Munia, Tamanna Tabassum Khan; Tavakolian, Kouhyar; Vasefi, Fartash; MacKinnon, Nick; Fazel-Rezai, Reza

2016-08-01

Chronic skin diseases like eczema may lead to severe health and financial consequences for patients if not detected and controlled early. Early measurement of disease severity, combined with a recommendation for skin protection and use of appropriate medication can prevent the disease from worsening. Current diagnosis can be costly and time-consuming. In this paper, an automatic eczema detection and severity measurement model are presented using modern image processing and computer algorithm. The system can successfully detect regions of eczema and classify the identified region as mild or severe based on image color and texture feature. Then the model automatically measures skin parameters used in the most common assessment tool called "Eczema Area and Severity Index (EASI)," by computing eczema affected area score, eczema intensity score, and body region score of eczema allowing both patients and physicians to accurately assess the affected skin.

Ultrasound-guided three-dimensional needle steering in biological tissue with curved surfaces

PubMed Central

Abayazid, Momen; Moreira, Pedro; Shahriari, Navid; Patil, Sachin; Alterovitz, Ron; Misra, Sarthak

2015-01-01

In this paper, we present a system capable of automatically steering a bevel-tipped flexible needle under ultrasound guidance toward a physical target while avoiding a physical obstacle embedded in gelatin phantoms and biological tissue with curved surfaces. An ultrasound pre-operative scan is performed for three-dimensional (3D) target localization and shape reconstruction. A controller based on implicit force control is developed to align the transducer with curved surfaces to assure the maximum contact area, and thus obtain an image of sufficient quality. We experimentally investigate the effect of needle insertion system parameters such as insertion speed, needle diameter and bevel angle on target motion to adjust the parameters that minimize the target motion during insertion. A fast sampling-based path planner is used to compute and periodically update a feasible path to the target that avoids obstacles. We present experimental results for target reconstruction and needle insertion procedures in gelatin-based phantoms and biological tissue. Mean targeting errors of 1.46 ± 0.37 mm, 1.29 ± 0.29 mm and 1.82 ± 0.58 mm are obtained for phantoms with inclined, curved and combined (inclined and curved) surfaces, respectively, for insertion distance of 86–103 mm. The achieved targeting errors suggest that our approach is sufficient for targeting lesions of 3 mm radius that can be detected using clinical ultrasound imaging systems. PMID:25455165

Automated target recognition using passive radar and coordinated flight models

NASA Astrophysics Data System (ADS)

Ehrman, Lisa M.; Lanterman, Aaron D.

2003-09-01

Rather than emitting pulses, passive radar systems rely on illuminators of opportunity, such as TV and FM radio, to illuminate potential targets. These systems are particularly attractive since they allow receivers to operate without emitting energy, rendering them covert. Many existing passive radar systems estimate the locations and velocities of targets. This paper focuses on adding an automatic target recognition (ATR) component to such systems. Our approach to ATR compares the Radar Cross Section (RCS) of targets detected by a passive radar system to the simulated RCS of known targets. To make the comparison as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. The estimated positions become inputs for an algorithm that uses a coordinated flight model to compute probable aircraft orientation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of several potential target classes as they execute the estimated maneuvers. The RCS is then scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. The Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern, so that the RCS can be further scaled. The Rician model compares the RCS of the illuminated aircraft with those of the potential targets. This comparison results in target identification.

Multisource oil spill detection

NASA Astrophysics Data System (ADS)

Salberg, Arnt B.; Larsen, Siri O.; Zortea, Maciel

2013-10-01

In this paper we discuss how multisource data (wind, ocean-current, optical, bathymetric, automatic identification systems (AIS)) may be used to improve oil spill detection in SAR images, with emphasis on the use of automatic oil spill detection algorithms. We focus particularly on AIS, optical, and bathymetric data. For the AIS data we propose an algorithm for integrating AIS ship tracks into automatic oil spill detection in order to improve the confidence estimate of a potential oil spill. We demonstrate the use of ancillary data on a set of SAR images. Regarding the use of optical data, we did not observe a clear correspondence between high chlorophyll values (estimated from products derived from optical data) and observed slicks in the SAR image. Bathymetric data was shown to be a good data source for removing false detections caused by e.g. sand banks on low tide. For the AIS data we observed that a polluter could be identified for some dark slicks, however, a precise oil drift model is needed in order to identify the polluter with high certainty.

Automatic construction of a recurrent neural network based classifier for vehicle passage detection

NASA Astrophysics Data System (ADS)

Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

2017-03-01

Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

Error Rates in Users of Automatic Face Recognition Software

PubMed Central

White, David; Dunn, James D.; Schmid, Alexandra C.; Kemp, Richard I.

2015-01-01

In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated ‘candidate lists’ selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers–who use the system in their daily work–and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced “facial examiners” outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems–potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems. PMID:26465631

The DEEP-South: Preliminary Photometric Results from the KMTNet-CTIO

NASA Astrophysics Data System (ADS)

Kim, Myung-Jin; Moon, Hong-Kyu; Choi, Young-Jun; Yim, Hong-Suh; Bae, Youngho; Roh, Dong-Goo; the DEEP-South Team

2015-08-01

The DEep Ecliptic Patrol of the Southern sky (DEEP-South) will not only conduct characterization of targeted asteroids and blind survey at the sweet spots, but also utilize data mining of small Solar System bodies in the whole KMTNet archive. As round-the-clock observation with the KMTNet is optimized for spin characterization of tumbling and slow-rotating bodies as it facilitates debiasing previously reported lightcurve observations. It is also most suitable for detection and rapid follow-up of Atens and Atiras, the “difficult objects” that are being discovered at lower solar elongations.For the sake of efficiency, we implemented an observation scheduler, SMART (Scheduler for Measuring Asteroids RoTation), designed to conduct follow-up observations in a timely manner. It automatically updates catalogs, generates ephemerides, checks priorities, prepares target lists, and sends a suite of scripts to site operators. We also developed photometric analysis software called ASAP (Asteroid Spin Analysis Package) that aids to find a set of appropriate comparison stars in an image, to derive spin parameters and reconstruct lightcurve simultaneously in a semi-automatic manner. In this presentation, we will show our preliminary results of time series analyses of a number of km-sized Potentially Hazardous Asteroids (PHAs), 5189 (1990 UQ), 12923 (1999 GK4), 53426 (1999 SL5), 136614 (1993 VA6), 385186 (1994 AW1), and 2000 OH from test runs in February and March 2015 at the KMTNet-CTIO.

Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree.

PubMed

Carneiro, Gustavo; Georgescu, Bogdan; Good, Sara; Comaniciu, Dorin

2008-09-01

We propose a novel method for the automatic detection and measurement of fetal anatomical structures in ultrasound images. This problem offers a myriad of challenges, including: difficulty of modeling the appearance variations of the visual object of interest, robustness to speckle noise and signal dropout, and large search space of the detection procedure. Previous solutions typically rely on the explicit encoding of prior knowledge and formulation of the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are constrained by the validity of the underlying assumptions and usually are not enough to capture the complex appearances of fetal anatomies. We propose a novel system for fast automatic detection and measurement of fetal anatomies that directly exploits a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns automatically to distinguish between the appearance of the object of interest and background by training a constrained probabilistic boosting tree classifier. This system is able to produce the automatic segmentation of several fetal anatomies using the same basic detection algorithm. We show results on fully automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), humerus length (HL), and crown rump length (CRL). Notice that our approach is the first in the literature to deal with the HL and CRL measurements. Extensive experiments (with clinical validation) show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer.

The role of the P3 and CNV components in voluntary and automatic temporal orienting: A high spatial-resolution ERP study.

PubMed

Mento, Giovanni

2017-12-01

A main distinction has been proposed between voluntary and automatic mechanisms underlying temporal orienting (TO) of selective attention. Voluntary TO implies the endogenous directing of attention induced by symbolic cues. Conversely, automatic TO is exogenously instantiated by the physical properties of stimuli. A well-known example of automatic TO is sequential effects (SEs), which refer to the adjustments in participants' behavioral performance as a function of the trial-by-trial sequential distribution of the foreperiod between two stimuli. In this study a group of healthy adults underwent a cued reaction time task purposely designed to assess both voluntary and automatic TO. During the task, both post-cue and post-target event-related potentials (ERPs) were recorded by means of a high spatial resolution EEG system. In the results of the post-cue analysis, the P3a and P3b were identified as two distinct ERP markers showing distinguishable spatiotemporal features and reflecting automatic and voluntary a priori expectancy generation, respectively. The brain source reconstruction further revealed that distinct cortical circuits supported these two temporally dissociable components. Namely, the voluntary P3b was supported by a left sensorimotor network, while the automatic P3a was generated by a more distributed frontoparietal circuit. Additionally, post-cue contingent negative variation (CNV) and post-target P3 modulations were observed as common markers of voluntary and automatic expectancy implementation and response selection, although partially dissociable neural networks subserved these two mechanisms. Overall, these results provide new electrophysiological evidence suggesting that distinct neural substrates can be recruited depending on the voluntary or automatic cognitive nature of the cognitive mechanisms subserving TO. Copyright © 2017 Elsevier Ltd. All rights reserved.

Colorimetric detection with aptamer-gold nanoparticle conjugates coupled to an android-based color analysis application for use in the field.

PubMed

Smith, Joshua E; Griffin, Daniel K; Leny, Juliann K; Hagen, Joshua A; Chávez, Jorge L; Kelley-Loughnane, Nancy

2014-04-01

The feasibility of using aptamer-gold nanoparticle conjugates (Apt-AuNPs) to design colorimetric assays for in the field detection of small molecules was investigated. An assay to detect cocaine was designed using two clones of a known cocaine-binding aptamer. The assay was based on the AuNPs difference in affinity for single-stranded DNA (non-binding) and double stranded DNA (target bound). In the first assay, a commonly used design was followed, in which the aptamer and target were incubated to allow binding followed by exposure to the AuNPs. Interactions between the non-bound analytes and the AuNPs surface resulted in a number of false positives. The assay was redesigned by incubating the AuNPs and the aptamer prior to target addition to passivate the AuNPs surface. The adsorbed aptamer was able to bind the target while preventing non-specific interactions. The assay was validated with a number of masking and cutting agents and other controlled substances showing minimal false positives. Studies to improve the assay performance in the field were performed, showing that assay activity could be preserved for up to 2 months. To facilitate the assay analysis, an android application for automatic colorimetric characterization was developed. The application was validated by challenging the assay with cocaine standards of different concentrations, and comparing the results to a conventional plate reader, showing outstanding agreement. Finally, the rapid identification of cocaine in mixtures mimicking street samples was demonstrated. This work established that Apt-AuNPs can be used to design robust assays to be used in the field. Copyright © 2013 Elsevier B.V. All rights reserved.

Integrating Remote Sensing and Disease Surveillance to Forecast Malaria Epidemics

NASA Astrophysics Data System (ADS)

Wimberly, M. C.; Beyane, B.; DeVos, M.; Liu, Y.; Merkord, C. L.; Mihretie, A.

2015-12-01

Advance information about the timing and locations of malaria epidemics can facilitate the targeting of resources for prevention and emergency response. Early detection methods can detect incipient outbreaks by identifying deviations from expected seasonal patterns, whereas early warning approaches typically forecast future malaria risk based on lagged responses to meteorological factors. A critical limiting factor for implementing either of these approaches is the need for timely and consistent acquisition, processing and analysis of both environmental and epidemiological data. To address this need, we have developed EPIDEMIA - an integrated system for surveillance and forecasting of malaria epidemics. The EPIDEMIA system includes a public health interface for uploading and querying weekly surveillance reports as well as algorithms for automatically validating incoming data and updating the epidemiological surveillance database. The newly released EASTWeb 2.0 software application automatically downloads, processes, and summaries remotely-sensed environmental data from multiple earth science data archives. EASTWeb was implemented as a component of the EPIDEMIA system, which combines the environmental monitoring data and epidemiological surveillance data into a unified database that supports both early detection and early warning models. Dynamic linear models implemented with Kalman filtering were used to carry out forecasting and model updating. Preliminary forecasts have been disseminated to public health partners in the Amhara Region of Ethiopia and will be validated and refined as the EPIDEMIA system ingests new data. In addition to continued model development and testing, future work will involve updating the public health interface to provide a broader suite of outbreak alerts and data visualization tools that are useful to our public health partners. The EPIDEMIA system demonstrates a feasible approach to synthesizing the information from epidemiological surveillance systems and remotely-sensed environmental monitoring systems to improve malaria epidemic detection and forecasting.

Automatic Contour Tracking in Ultrasound Images

ERIC Educational Resources Information Center

Li, Min; Kambhamettu, Chandra; Stone, Maureen

2005-01-01

In this paper, a new automatic contour tracking system, EdgeTrak, for the ultrasound image sequences of human tongue is presented. The images are produced by a head and transducer support system (HATS). The noise and unrelated high-contrast edges in ultrasound images make it very difficult to automatically detect the correct tongue surfaces. In…

The Potential of Automatic Word Comparison for Historical Linguistics.

PubMed

List, Johann-Mattis; Greenhill, Simon J; Gray, Russell D

2017-01-01

The amount of data from languages spoken all over the world is rapidly increasing. Traditional manual methods in historical linguistics need to face the challenges brought by this influx of data. Automatic approaches to word comparison could provide invaluable help to pre-analyze data which can be later enhanced by experts. In this way, computational approaches can take care of the repetitive and schematic tasks leaving experts to concentrate on answering interesting questions. Here we test the potential of automatic methods to detect etymologically related words (cognates) in cross-linguistic data. Using a newly compiled database of expert cognate judgments across five different language families, we compare how well different automatic approaches distinguish related from unrelated words. Our results show that automatic methods can identify cognates with a very high degree of accuracy, reaching 89% for the best-performing method Infomap. We identify the specific strengths and weaknesses of these different methods and point to major challenges for future approaches. Current automatic approaches for cognate detection-although not perfect-could become an important component of future research in historical linguistics.

Automatic Mexico Gulf Oil Spill Detection from Radarsat-2 SAR Satellite Data Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Marghany, Maged

2016-10-01

In this work, a genetic algorithm is exploited for automatic detection of oil spills of small and large size. The route is achieved using arrays of RADARSAT-2 SAR ScanSAR Narrow single beam data obtained in the Gulf of Mexico. The study shows that genetic algorithm has automatically segmented the dark spot patches related to small and large oil spill pixels. This conclusion is confirmed by the receiveroperating characteristic (ROC) curve and ground data which have been documented. The ROC curve indicates that the existence of oil slick footprints can be identified with the area under the curve between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. The small oil spill sizes represented 30% of the discriminated oil spill pixels in ROC curve. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills of either small or large size and the ScanSAR Narrow single beam mode serves as an excellent sensor for oil spill patterns detection and surveying in the Gulf of Mexico.

[A wavelet-transform-based method for the automatic detection of late-type stars].

PubMed

Liu, Zhong-tian; Zhao, Rrui-zhen; Zhao, Yong-heng; Wu, Fu-chao

2005-07-01

The LAMOST project, the world largest sky survey project, urgently needs an automatic late-type stars detection system. However, to our knowledge, no effective methods for automatic late-type stars detection have been reported in the literature up to now. The present study work is intended to explore possible ways to deal with this issue. Here, by "late-type stars" we mean those stars with strong molecule absorption bands, including oxygen-rich M, L and T type stars and carbon-rich C stars. Based on experimental results, the authors find that after a wavelet transform with 5 scales on the late-type stars spectra, their frequency spectrum of the transformed coefficient on the 5th scale consistently manifests a unimodal distribution, and the energy of frequency spectrum is largely concentrated on a small neighborhood centered around the unique peak. However, for the spectra of other celestial bodies, the corresponding frequency spectrum is of multimodal and the energy of frequency spectrum is dispersible. Based on such a finding, the authors presented a wavelet-transform-based automatic late-type stars detection method. The proposed method is shown by extensive experiments to be practical and of good robustness.

Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN.

PubMed

Fowler, Anna; Mahamdallie, Shazia; Ruark, Elise; Seal, Sheila; Ramsay, Emma; Clarke, Matthew; Uddin, Imran; Wylie, Harriet; Strydom, Ann; Lunter, Gerton; Rahman, Nazneen

2016-11-25

Background: Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, 'exon CNVs') in exon-targeted NGS panels has proved challenging, particularly for single exon CNVs.  Methods: We developed a tool for the Detection of Exon Copy Number variants (DECoN), which is optimised for analysis of exon-targeted NGS panels in the clinical setting. We evaluated DECoN performance using 96 samples with independently validated exon CNV data. We performed simulations to evaluate DECoN detection performance of single exon CNVs and to evaluate performance using different coverage levels and sample numbers. Finally, we implemented DECoN in a clinical laboratory that tests BRCA1 and BRCA2 with the TruSight Cancer Panel (TSCP). We used DECoN to analyse 1,919 samples, validating exon CNV detections by multiplex ligation-dependent probe amplification (MLPA).  Results: In the evaluation set, DECoN achieved 100% sensitivity and 99% specificity for BRCA exon CNVs, including identification of 8 single exon CNVs. DECoN also identified 14/15 exon CNVs in 8 other genes. Simulations of all possible BRCA single exon CNVs gave a mean sensitivity of 98% for deletions and 95% for duplications. DECoN performance remained excellent with different levels of coverage and sample numbers; sensitivity and specificity was >98% with the typical NGS run parameters. In the clinical pipeline, DECoN automatically analyses pools of 48 samples at a time, taking 24 minutes per pool, on average. DECoN detected 24 BRCA exon CNVs, of which 23 were confirmed by MLPA, giving a false discovery rate of 4%. Specificity was 99.7%.  Conclusions: DECoN is a fast, accurate, exon CNV detection tool readily implementable in research and clinical NGS pipelines. It has high sensitivity and specificity and acceptable false discovery rate. DECoN is freely available at www.icr.ac.uk/decon.

Evaluating the effectiveness of wildlife detection and observation technologies at a solar power tower facility

USGS Publications Warehouse

Diehl, Robert H.; Valdez, Ernest W.; Preston, Todd M.; Wellik, Mike J.; Cryan, Paul

2016-01-01

Solar power towers produce electrical energy from sunlight at an industrial scale. Little is known about the effects of this technology on flying animals and few methods exist for automatically detecting or observing wildlife at solar towers and other tall anthropogenic structures. Smoking objects are sometimes observed co-occurring with reflected, concentrated light (“solar flux”) in the airspace around solar towers, but the identity and origins of such objects can be difficult to determine. In this observational pilot study at the world’s largest solar tower facility, we assessed the efficacy of using radar, surveillance video, and insect trapping to detect and observe animals flying near the towers. During site visits in May and September 2014, we monitored the airspace surrounding towers and observed insects, birds, and bats under a variety of environmental and operational conditions. We detected and broadly differentiated animals or objects moving through the airspace generally using radar and near solar towers using several video imaging methods. Video revealed what appeared to be mostly small insects burning in the solar flux. Also, we occasionally detected birds flying in the solar flux but could not accurately identify birds to species or the types of insects and small objects composing the vast majority of smoking targets. Insect trapping on the ground was somewhat effective at sampling smaller insects around the tower, and presence and abundance of insects in the traps generally trended with radar and video observations. Traps did not tend to sample the larger insects we sometimes observed flying in the solar flux or found dead on the ground beneath the towers. Some of the methods we tested (e.g., video surveillance) could be further assessed and potentially used to automatically detect and observe flying animals in the vicinity of solar towers to advance understanding about their effects on wildlife.

Evaluating the Effectiveness of Wildlife Detection and Observation Technologies at a Solar Power Tower Facility.

PubMed

Diehl, Robert H; Valdez, Ernest W; Preston, Todd M; Wellik, Michael J; Cryan, Paul M

2016-01-01

Solar power towers produce electrical energy from sunlight at an industrial scale. Little is known about the effects of this technology on flying animals and few methods exist for automatically detecting or observing wildlife at solar towers and other tall anthropogenic structures. Smoking objects are sometimes observed co-occurring with reflected, concentrated light ("solar flux") in the airspace around solar towers, but the identity and origins of such objects can be difficult to determine. In this observational pilot study at the world's largest solar tower facility, we assessed the efficacy of using radar, surveillance video, and insect trapping to detect and observe animals flying near the towers. During site visits in May and September 2014, we monitored the airspace surrounding towers and observed insects, birds, and bats under a variety of environmental and operational conditions. We detected and broadly differentiated animals or objects moving through the airspace generally using radar and near solar towers using several video imaging methods. Video revealed what appeared to be mostly small insects burning in the solar flux. Also, we occasionally detected birds flying in the solar flux but could not accurately identify birds to species or the types of insects and small objects composing the vast majority of smoking targets. Insect trapping on the ground was somewhat effective at sampling smaller insects around the tower, and presence and abundance of insects in the traps generally trended with radar and video observations. Traps did not tend to sample the larger insects we sometimes observed flying in the solar flux or found dead on the ground beneath the towers. Some of the methods we tested (e.g., video surveillance) could be further assessed and potentially used to automatically detect and observe flying animals in the vicinity of solar towers to advance understanding about their effects on wildlife.

Evaluating the Effectiveness of Wildlife Detection and Observation Technologies at a Solar Power Tower Facility

PubMed Central

Diehl, Robert H.; Valdez, Ernest W.; Preston, Todd M.; Wellik, Michael J.; Cryan, Paul M.

2016-01-01

Solar power towers produce electrical energy from sunlight at an industrial scale. Little is known about the effects of this technology on flying animals and few methods exist for automatically detecting or observing wildlife at solar towers and other tall anthropogenic structures. Smoking objects are sometimes observed co-occurring with reflected, concentrated light (“solar flux”) in the airspace around solar towers, but the identity and origins of such objects can be difficult to determine. In this observational pilot study at the world’s largest solar tower facility, we assessed the efficacy of using radar, surveillance video, and insect trapping to detect and observe animals flying near the towers. During site visits in May and September 2014, we monitored the airspace surrounding towers and observed insects, birds, and bats under a variety of environmental and operational conditions. We detected and broadly differentiated animals or objects moving through the airspace generally using radar and near solar towers using several video imaging methods. Video revealed what appeared to be mostly small insects burning in the solar flux. Also, we occasionally detected birds flying in the solar flux but could not accurately identify birds to species or the types of insects and small objects composing the vast majority of smoking targets. Insect trapping on the ground was somewhat effective at sampling smaller insects around the tower, and presence and abundance of insects in the traps generally trended with radar and video observations. Traps did not tend to sample the larger insects we sometimes observed flying in the solar flux or found dead on the ground beneath the towers. Some of the methods we tested (e.g., video surveillance) could be further assessed and potentially used to automatically detect and observe flying animals in the vicinity of solar towers to advance understanding about their effects on wildlife. PMID:27462989

Evaluation of an automatic MR-based gold fiducial marker localisation method for MR-only prostate radiotherapy

NASA Astrophysics Data System (ADS)

Maspero, Matteo; van den Berg, Cornelis A. T.; Zijlstra, Frank; Sikkes, Gonda G.; de Boer, Hans C. J.; Meijer, Gert J.; Kerkmeijer, Linda G. W.; Viergever, Max A.; Lagendijk, Jan J. W.; Seevinck, Peter R.

2017-10-01

An MR-only radiotherapy planning (RTP) workflow would reduce the cost, radiation exposure and uncertainties introduced by CT-MRI registrations. In the case of prostate treatment, one of the remaining challenges currently holding back the implementation of an RTP workflow is the MR-based localisation of intraprostatic gold fiducial markers (FMs), which is crucial for accurate patient positioning. Currently, MR-based FM localisation is clinically performed manually. This is sub-optimal, as manual interaction increases the workload. Attempts to perform automatic FM detection often rely on being able to detect signal voids induced by the FMs in magnitude images. However, signal voids may not always be sufficiently specific, hampering accurate and robust automatic FM localisation. Here, we present an approach that aims at automatic MR-based FM localisation. This method is based on template matching using a library of simulated complex-valued templates, and exploiting the behaviour of the complex MR signal in the vicinity of the FM. Clinical evaluation was performed on seventeen prostate cancer patients undergoing external beam radiotherapy treatment. Automatic MR-based FM localisation was compared to manual MR-based and semi-automatic CT-based localisation (the current gold standard) in terms of detection rate and the spatial accuracy and precision of localisation. The proposed method correctly detected all three FMs in 15/17 patients. The spatial accuracy (mean) and precision (STD) were 0.9 mm and 0.5 mm respectively, which is below the voxel size of 1.1 × 1.1 × 1.2 mm3 and comparable to MR-based manual localisation. FM localisation failed (3/51 FMs) in the presence of bleeding or calcifications in the direct vicinity of the FM. The method was found to be spatially accurate and precise, which is essential for clinical use. To overcome any missed detection, we envision the use of the proposed method along with verification by an observer. This will result in a semi-automatic workflow facilitating the introduction of an MR-only workflow.

Automatic detection and recognition of signs from natural scenes.

PubMed

Chen, Xilin; Yang, Jie; Zhang, Jing; Waibel, Alex

2004-01-01

In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.

SU-G-JeP4-03: Anomaly Detection of Respiratory Motion by Use of Singular Spectrum Analysis

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

Kotoku, J; Kumagai, S; Nakabayashi, S

Purpose: The implementation and realization of automatic anomaly detection of respiratory motion is a very important technique to prevent accidental damage during radiation therapy. Here, we propose an automatic anomaly detection method using singular value decomposition analysis. Methods: The anomaly detection procedure consists of four parts:1) measurement of normal respiratory motion data of a patient2) calculation of a trajectory matrix representing normal time-series feature3) real-time monitoring and calculation of a trajectory matrix of real-time data.4) calculation of an anomaly score from the similarity of the two feature matrices. Patient motion was observed by a marker-less tracking system using a depthmore » camera. Results: Two types of motion e.g. cough and sudden stop of breathing were successfully detected in our real-time application. Conclusion: Automatic anomaly detection of respiratory motion using singular spectrum analysis was successful in the cough and sudden stop of breathing. The clinical use of this algorithm will be very hopeful. This work was supported by JSPS KAKENHI Grant Number 15K08703.« less

Advances in segmentation modeling for health communication and social marketing campaigns.

PubMed

Albrecht, T L; Bryant, C

1996-01-01

Large-scale communication campaigns for health promotion and disease prevention involve analysis of audience demographic and psychographic factors for effective message targeting. A variety of segmentation modeling techniques, including tree-based methods such as Chi-squared Automatic Interaction Detection and logistic regression, are used to identify meaningful target groups within a large sample or population (N = 750-1,000+). Such groups are based on statistically significant combinations of factors (e.g., gender, marital status, and personality predispositions). The identification of groups or clusters facilitates message design in order to address the particular needs, attention patterns, and concerns of audience members within each group. We review current segmentation techniques, their contributions to conceptual development, and cost-effective decision making. Examples from a major study in which these strategies were used are provided from the Texas Women, Infants and Children Program's Comprehensive Social Marketing Program.

Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition

PubMed Central

Cui, Zhiming; Zhao, Pengpeng

2014-01-01

A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity. PMID:24605045

Photonics: From target recognition to lesion detection

NASA Technical Reports Server (NTRS)

Henry, E. Michael

1994-01-01

Since 1989, Martin Marietta has invested in the development of an innovative concept for robust real-time pattern recognition for any two-dimensioanal sensor. This concept has been tested in simulation, and in laboratory and field hardware, for a number of DOD and commercial uses from automatic target recognition to manufacturing inspection. We have now joined Rose Health Care Systems in developing its use for medical diagnostics. The concept is based on determining regions of interest by using optical Fourier bandpassing as a scene segmentation technique, enhancing those regions using wavelet filters, passing the enhanced regions to a neural network for analysis and initial pattern identification, and following this initial identification with confirmation by optical correlation. The optical scene segmentation and pattern confirmation are performed by the same optical module. The neural network is a recursive error minimization network with a small number of connections and nodes that rapidly converges to a global minimum.

Neural networks: Alternatives to conventional techniques for automatic docking

NASA Technical Reports Server (NTRS)

Vinz, Bradley L.

1994-01-01

Automatic docking of orbiting spacecraft is a crucial operation involving the identification of vehicle orientation as well as complex approach dynamics. The chaser spacecraft must be able to recognize the target spacecraft within a scene and achieve accurate closing maneuvers. In a video-based system, a target scene must be captured and transformed into a pattern of pixels. Successful recognition lies in the interpretation of this pattern. Due to their powerful pattern recognition capabilities, artificial neural networks offer a potential role in interpretation and automatic docking processes. Neural networks can reduce the computational time required by existing image processing and control software. In addition, neural networks are capable of recognizing and adapting to changes in their dynamic environment, enabling enhanced performance, redundancy, and fault tolerance. Most neural networks are robust to failure, capable of continued operation with a slight degradation in performance after minor failures. This paper discusses the particular automatic docking tasks neural networks can perform as viable alternatives to conventional techniques.

Quantification of regional fat volume in rat MRI

NASA Astrophysics Data System (ADS)

Sacha, Jaroslaw P.; Cockman, Michael D.; Dufresne, Thomas E.; Trokhan, Darren

2003-05-01

Multiple initiatives in the pharmaceutical and beauty care industries are directed at identifying therapies for weight management. Body composition measurements are critical for such initiatives. Imaging technologies that can be used to measure body composition noninvasively include DXA (dual energy x-ray absorptiometry) and MRI (magnetic resonance imaging). Unlike other approaches, MRI provides the ability to perform localized measurements of fat distribution. Several factors complicate the automatic delineation of fat regions and quantification of fat volumes. These include motion artifacts, field non-uniformity, brightness and contrast variations, chemical shift misregistration, and ambiguity in delineating anatomical structures. We have developed an approach to deal practically with those challenges. The approach is implemented in a package, the Fat Volume Tool, for automatic detection of fat tissue in MR images of the rat abdomen, including automatic discrimination between abdominal and subcutaneous regions. We suppress motion artifacts using masking based on detection of implicit landmarks in the images. Adaptive object extraction is used to compensate for intensity variations. This approach enables us to perform fat tissue detection and quantification in a fully automated manner. The package can also operate in manual mode, which can be used for verification of the automatic analysis or for performing supervised segmentation. In supervised segmentation, the operator has the ability to interact with the automatic segmentation procedures to touch-up or completely overwrite intermediate segmentation steps. The operator's interventions steer the automatic segmentation steps that follow. This improves the efficiency and quality of the final segmentation. Semi-automatic segmentation tools (interactive region growing, live-wire, etc.) improve both the accuracy and throughput of the operator when working in manual mode. The quality of automatic segmentation has been evaluated by comparing the results of fully automated analysis to manual analysis of the same images. The comparison shows a high degree of correlation that validates the quality of the automatic segmentation approach.

Fetal head detection and measurement in ultrasound images by an iterative randomized Hough transform

NASA Astrophysics Data System (ADS)

Lu, Wei; Tan, Jinglu; Floyd, Randall C.

2004-05-01

This paper describes an automatic method for measuring the biparietal diameter (BPD) and head circumference (HC) in ultrasound fetal images. A total of 217 ultrasound images were segmented by using a K-Mean classifier, and the head skull was detected in 214 of the 217 cases by an iterative randomized Hough transform developed for detection of incomplete curves in images with strong noise without user intervention. The automatic measurements were compared with conventional manual measurements by sonographers and a trained panel. The inter-run variations and differences between the automatic and conventional measurements were small compared with published inter-observer variations. The results showed that the automated measurements were as reliable as the expert measurements and more consistent. This method has great potential in clinical applications.

Novel dual-mode nanobubbles as potential targeted contrast agents for female tumors exploration.

PubMed

Yang, Hengli; Zhou, Tian; Cai, Wenbin; Yi, Xiaomin; Liu, Xi; Wang, Yixiao; Zhang, Li; Duan, Yunyou

2016-10-01

The purpose of this study was to prepare tumor-specific dual-mode nanobubbles as both ultrasound contrast agents (UCAs) and near-infrared fluorescence (NIRF) imaging agents for female tumors. Recent studies have demonstrated the conjugation of anti-tumor ligands on the surface of nanobubbles for use as molecule-targeting ultrasound contrast agents for tumor visualization. However, this complicated procedure has also posed a challenge to nanobubble stability. Thus, in the present study, we combined the fluorescent dye, NIRF IR-780 iodide, which has lipid solubility and tumor-targeting characteristics, with the phospholipid film of nanobubbles that we constructed. We then characterized the physical features of the IR-780-nanobubbles, observed their tumor-targeting capacity in multiple female tumor cell types in vitro, and verified their capability for use in tumor-specific ultrasound contrast imaging and NIRF imaging in vivo. The results showed that the new IR-780-nanobubbles had a uniform nano-size (442.5 ± 48.6 nm) and stability and that they were safe and effective at NIRF imaging and ultrasound imaging in vitro. The IR-780-nanobubbles were found to automatically accumulate on different female tumor cells in vitro with a considerable targeting rate (close to 40 %) but did not accumulate on cardiac muscle cells used as a negative control. Importantly, the IR-780-nanobubbles can detect female tumors precisely via dual-mode imaging in vivo. In conclusion, the new dual-mode IR-780-nanobubbles are stable and have potential advantages in non-invasive tumor-specific detection for female tumors via contrast-enhanced ultrasound and NIRF imaging.

Comparing Automatic CME Detections in Multiple LASCO and SECCHI Catalogs

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

Hess, Phillip; Colaninno, Robin C., E-mail: phillip.hess.ctr@nrl.navy.mil, E-mail: robin.colaninno@nrl.navy.mil

With the creation of numerous automatic detection algorithms, a number of different catalogs of coronal mass ejections (CMEs) spanning the entirety of the Solar and Heliospheric Observatory ( SOHO ) Large Angle Spectrometric Coronagraph (LASCO) mission have been created. Some of these catalogs have been further expanded for use on data from the Solar Terrestrial Earth Observatory ( STEREO ) Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) as well. We compare the results from different automatic detection catalogs (Solar Eruption Event Detection System (SEEDS), Computer Aided CME Tracking (CACTus), and Coronal Image Processing (CORIMP)) to ensure the consistency ofmore » detections in each. Over the entire span of the LASCO catalogs, the automatic catalogs are well correlated with one another, to a level greater than 0.88. Focusing on just periods of higher activity, these correlations remain above 0.7. We establish the difficulty in comparing detections over the course of LASCO observations due to the change in the instrument image cadence in 2010. Without adjusting catalogs for the cadence, CME detection rates show a large spike in cycle 24, despite a notable drop in other indices of solar activity. The output from SEEDS, using a consistent image cadence, shows that the CME rate has not significantly changed relative to sunspot number in cycle 24. These data, and mass calculations from CORIMP, lead us to conclude that any apparent increase in CME rate is a result of the change in cadence. We study detection characteristics of CMEs, discussing potential physical changes in events between cycles 23 and 24. We establish that, for detected CMEs, physical parameters can also be sensitive to the cadence.« less

Battlefield decision aid for acoustical ground sensors with interface to meteorological data sources

NASA Astrophysics Data System (ADS)

Wilson, D. Keith; Noble, John M.; VanAartsen, Bruce H.; Szeto, Gregory L.

2001-08-01

The performance of acoustical ground sensors depends heavily on the local atmospheric and terrain conditions. This paper describes a prototype physics-based decision aid, called the Acoustic Battlefield Aid (ABFA), for predicting these environ-mental effects. ABFA integrates advanced models for acoustic propagation, atmospheric structure, and array signal process-ing into a convenient graphical user interface. The propagation calculations are performed in the frequency domain on user-definable target spectra. The solution method involves a parabolic approximation to the wave equation combined with a ter-rain diffraction model. Sensor performance is characterized with Cramer-Rao lower bounds (CRLBs). The CRLB calcula-tions include randomization of signal energy and wavefront orientation resulting from atmospheric turbulence. Available performance characterizations include signal-to-noise ratio, probability of detection, direction-finding accuracy for isolated receiving arrays, and location-finding accuracy for networked receiving arrays. A suite of integrated tools allows users to create new target descriptions from standard digitized audio files and to design new sensor array layouts. These tools option-ally interface with the ARL Database/Automatic Target Recognition (ATR) Laboratory, providing access to an extensive library of target signatures. ABFA also includes a Java-based capability for network access of near real-time data from sur-face weather stations or forecasts from the Army's Integrated Meteorological System. As an example, the detection footprint of an acoustical sensor, as it evolves over a 13-hour period, is calculated.

Subliminal repetition primes help detection of phonemes in a picture: Evidence for a phonological level of the priming effects.

PubMed

Manoiloff, Laura; Segui, Juan; Hallé, Pierre

2016-01-01

In this research, we combine a cross-form word-picture visual masked priming procedure with an internal phoneme monitoring task to examine repetition priming effects. In this paradigm, participants have to respond to pictures whose names begin with a prespecified target phoneme. This task unambiguously requires retrieving the word-form of the target picture's name and implicitly orients participants' attention towards a phonological level of representation. The experiments were conducted within Spanish, whose highly transparent orthography presumably promotes fast and automatic phonological recoding of subliminal, masked visual word primes. Experiments 1 and 2 show that repetition primes speed up internal phoneme monitoring in the target, compared to primes beginning with a different phoneme from the target, or sharing only their first phoneme with the target. This suggests that repetition primes preactivate the phonological code of the entire target picture's name, hereby speeding up internal monitoring, which is necessarily based on such a code. To further qualify the nature of the phonological code underlying internal phoneme monitoring, a concurrent articulation task was used in Experiment 3. This task did not affect the repetition priming effect. We propose that internal phoneme monitoring is based on an abstract phonological code, prior to its translation into articulation.

Automatic reference selection for quantitative EEG interpretation: identification of diffuse/localised activity and the active earlobe reference, iterative detection of the distribution of EEG rhythms.

PubMed

Wang, Bei; Wang, Xingyu; Ikeda, Akio; Nagamine, Takashi; Shibasaki, Hiroshi; Nakamura, Masatoshi

2014-01-01

EEG (Electroencephalograph) interpretation is important for the diagnosis of neurological disorders. The proper adjustment of the montage can highlight the EEG rhythm of interest and avoid false interpretation. The aim of this study was to develop an automatic reference selection method to identify a suitable reference. The results may contribute to the accurate inspection of the distribution of EEG rhythms for quantitative EEG interpretation. The method includes two pre-judgements and one iterative detection module. The diffuse case is initially identified by pre-judgement 1 when intermittent rhythmic waveforms occur over large areas along the scalp. The earlobe reference or averaged reference is adopted for the diffuse case due to the effect of the earlobe reference depending on pre-judgement 2. An iterative detection algorithm is developed for the localised case when the signal is distributed in a small area of the brain. The suitable averaged reference is finally determined based on the detected focal and distributed electrodes. The presented technique was applied to the pathological EEG recordings of nine patients. One example of the diffuse case is introduced by illustrating the results of the pre-judgements. The diffusely intermittent rhythmic slow wave is identified. The effect of active earlobe reference is analysed. Two examples of the localised case are presented, indicating the results of the iterative detection module. The focal and distributed electrodes are detected automatically during the repeating algorithm. The identification of diffuse and localised activity was satisfactory compared with the visual inspection. The EEG rhythm of interest can be highlighted using a suitable selected reference. The implementation of an automatic reference selection method is helpful to detect the distribution of an EEG rhythm, which can improve the accuracy of EEG interpretation during both visual inspection and automatic interpretation. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.