Robust human detection, tracking, and recognition in crowded urban areas

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike

2014-06-01

In this paper, we present algorithms we recently developed to support an automated security surveillance system for very crowded urban areas. In our approach for human detection, the color features are obtained by taking the difference of R, G, B spectrum and converting R, G, B to HSV (Hue, Saturation, Value) space. Morphological patch filtering and regional minimum and maximum segmentation on the extracted features are applied for target detection. The human tracking process approach includes: 1) Color and intensity feature matching track candidate selection; 2) Separate three parallel trackers for color, bright (above mean intensity), and dim (below mean intensity) detections, respectively; 3) Adaptive track gate size selection for reducing false tracking probability; and 4) Forward position prediction based on previous moving speed and direction for continuing tracking even when detections are missed from frame to frame. The Human target recognition is improved with a Super-Resolution Image Enhancement (SRIE) process. This process can improve target resolution by 3-5 times and can simultaneously process many targets that are tracked. Our approach can project tracks from one camera to another camera with a different perspective viewing angle to obtain additional biometric features from different perspective angles, and to continue tracking the same person from the 2nd camera even though the person moved out of the Field of View (FOV) of the 1st camera with `Tracking Relay'. Finally, the multiple cameras at different view poses have been geo-rectified to nadir view plane and geo-registered with Google- Earth (or other GIS) to obtain accurate positions (latitude, longitude, and altitude) of the tracked human for pin-point targeting and for a large area total human motion activity top-view. Preliminary tests of our algorithms indicate than high probability of detection can be achieved for both moving and stationary humans. Our algorithms can simultaneously track more than 100 human targets with averaged tracking period (time length) longer than the performance of the current state-of-the-art.

English Listeners Use Suprasegmental Cues to Lexical Stress Early During Spoken-Word Recognition

PubMed Central

Poellmann, Katja; Kong, Ying-Yee

2017-01-01

Purpose We used an eye-tracking technique to investigate whether English listeners use suprasegmental information about lexical stress to speed up the recognition of spoken words in English. Method In a visual world paradigm, 24 young English listeners followed spoken instructions to choose 1 of 4 printed referents on a computer screen (e.g., “Click on the word admiral”). Displays contained a critical pair of words (e.g., ˈadmiral–ˌadmiˈration) that were segmentally identical for their first 2 syllables but differed suprasegmentally in their 1st syllable: One word began with primary lexical stress, and the other began with secondary lexical stress. All words had phrase-level prominence. Listeners' relative proportion of eye fixations on these words indicated their ability to differentiate them over time. Results Before critical word pairs became segmentally distinguishable in their 3rd syllables, participants fixated target words more than their stress competitors, but only if targets had initial primary lexical stress. The degree to which stress competitors were fixated was independent of their stress pattern. Conclusions Suprasegmental information about lexical stress modulates the time course of spoken-word recognition. Specifically, suprasegmental information on the primary-stressed syllable of words with phrase-level prominence helps in distinguishing the word from phonological competitors with secondary lexical stress. PMID:28056135

Lip reading using neural networks

NASA Astrophysics Data System (ADS)

Kalbande, Dhananjay; Mishra, Akassh A.; Patil, Sanjivani; Nirgudkar, Sneha; Patel, Prashant

2011-10-01

Computerized lip reading, or speech reading, is concerned with the difficult task of converting a video signal of a speaking person to written text. It has several applications like teaching deaf and dumb to speak and communicate effectively with the other people, its crime fighting potential and invariance to acoustic environment. We convert the video of the subject speaking vowels into images and then images are further selected manually for processing. However, several factors like fast speech, bad pronunciation, and poor illumination, movement of face, moustaches and beards make lip reading difficult. Contour tracking methods and Template matching are used for the extraction of lips from the face. K Nearest Neighbor algorithm is then used to classify the 'speaking' images and the 'silent' images. The sequence of images is then transformed into segments of utterances. Feature vector is calculated on each frame for all the segments and is stored in the database with properly labeled class. Character recognition is performed using modified KNN algorithm which assigns more weight to nearer neighbors. This paper reports the recognition of vowels using KNN algorithms

Inertial Sensor-Based Touch and Shake Metaphor for Expressive Control of 3D Virtual Avatars

PubMed Central

Patil, Shashidhar; Chintalapalli, Harinadha Reddy; Kim, Dubeom; Chai, Youngho

2015-01-01

In this paper, we present an inertial sensor-based touch and shake metaphor for expressive control of a 3D virtual avatar in a virtual environment. An intuitive six degrees-of-freedom wireless inertial motion sensor is used as a gesture and motion control input device with a sensor fusion algorithm. The algorithm enables user hand motions to be tracked in 3D space via magnetic, angular rate, and gravity sensors. A quaternion-based complementary filter is implemented to reduce noise and drift. An algorithm based on dynamic time-warping is developed for efficient recognition of dynamic hand gestures with real-time automatic hand gesture segmentation. Our approach enables the recognition of gestures and estimates gesture variations for continuous interaction. We demonstrate the gesture expressivity using an interactive flexible gesture mapping interface for authoring and controlling a 3D virtual avatar and its motion by tracking user dynamic hand gestures. This synthesizes stylistic variations in a 3D virtual avatar, producing motions that are not present in the motion database using hand gesture sequences from a single inertial motion sensor. PMID:26094629

Tracking and recognition face in videos with incremental local sparse representation model

NASA Astrophysics Data System (ADS)

Wang, Chao; Wang, Yunhong; Zhang, Zhaoxiang

2013-10-01

This paper addresses the problem of tracking and recognizing faces via incremental local sparse representation. First a robust face tracking algorithm is proposed via employing local sparse appearance and covariance pooling method. In the following face recognition stage, with the employment of a novel template update strategy, which combines incremental subspace learning, our recognition algorithm adapts the template to appearance changes and reduces the influence of occlusion and illumination variation. This leads to a robust video-based face tracking and recognition with desirable performance. In the experiments, we test the quality of face recognition in real-world noisy videos on YouTube database, which includes 47 celebrities. Our proposed method produces a high face recognition rate at 95% of all videos. The proposed face tracking and recognition algorithms are also tested on a set of noisy videos under heavy occlusion and illumination variation. The tracking results on challenging benchmark videos demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. In the case of the challenging dataset in which faces undergo occlusion and illumination variation, and tracking and recognition experiments under significant pose variation on the University of California, San Diego (Honda/UCSD) database, our proposed method also consistently demonstrates a high recognition rate.

Robust Pedestrian Tracking and Recognition from FLIR Video: A Unified Approach via Sparse Coding

PubMed Central

Li, Xin; Guo, Rui; Chen, Chao

2014-01-01

Sparse coding is an emerging method that has been successfully applied to both robust object tracking and recognition in the vision literature. In this paper, we propose to explore a sparse coding-based approach toward joint object tracking-and-recognition and explore its potential in the analysis of forward-looking infrared (FLIR) video to support nighttime machine vision systems. A key technical contribution of this work is to unify existing sparse coding-based approaches toward tracking and recognition under the same framework, so that they can benefit from each other in a closed-loop. On the one hand, tracking the same object through temporal frames allows us to achieve improved recognition performance through dynamical updating of template/dictionary and combining multiple recognition results; on the other hand, the recognition of individual objects facilitates the tracking of multiple objects (i.e., walking pedestrians), especially in the presence of occlusion within a crowded environment. We report experimental results on both the CASIAPedestrian Database and our own collected FLIR video database to demonstrate the effectiveness of the proposed joint tracking-and-recognition approach. PMID:24961216

Microoptical compound lens

DOEpatents

Sweatt, William C.; Gill, David D.

2007-10-23

An apposition microoptical compound lens comprises a plurality of lenslets arrayed around a segment of a hollow, three-dimensional optical shell. The lenslets collect light from an object and focus the light rays onto the concentric, curved front surface of a coherent fiber bundle. The fiber bundle transports the light rays to a planar detector, forming a plurality of sub-images that can be reconstructed as a full image. The microoptical compound lens can have a small size (millimeters), wide field of view (up to 180.degree.), and adequate resolution for object recognition and tracking.

Image processing and recognition for biological images

PubMed Central

Uchida, Seiichi

2013-01-01

This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the visibility of an input image and acquire some valuable information from it. As the main tasks of image processing, this paper introduces gray-level transformation, binarization, image filtering, image segmentation, visual object tracking, optical flow and image registration. Image pattern recognition is the technique to classify an input image into one of the predefined classes and also has a large research area. This paper overviews its two main modules, that is, feature extraction module and classification module. Throughout the paper, it will be emphasized that bioimage is a very difficult target for even state-of-the-art image processing and pattern recognition techniques due to noises, deformations, etc. This paper is expected to be one tutorial guide to bridge biology and image processing researchers for their further collaboration to tackle such a difficult target. PMID:23560739

An Interactive Image Segmentation Method in Hand Gesture Recognition

PubMed Central

Chen, Disi; Li, Gongfa; Sun, Ying; Kong, Jianyi; Jiang, Guozhang; Tang, Heng; Ju, Zhaojie; Yu, Hui; Liu, Honghai

2017-01-01

In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy. PMID:28134818

Development of Portable Automatic Number Plate Recognition System on Android Mobile Phone

NASA Astrophysics Data System (ADS)

Mutholib, Abdul; Gunawan, Teddy S.; Chebil, Jalel; Kartiwi, Mira

2013-12-01

The Automatic Number Plate Recognition (ANPR) System has performed as the main role in various access control and security, such as: tracking of stolen vehicles, traffic violations (speed trap) and parking management system. In this paper, the portable ANPR implemented on android mobile phone is presented. The main challenges in mobile application are including higher coding efficiency, reduced computational complexity, and improved flexibility. Significance efforts are being explored to find suitable and adaptive algorithm for implementation of ANPR on mobile phone. ANPR system for mobile phone need to be optimize due to its limited CPU and memory resources, its ability for geo-tagging image captured using GPS coordinates and its ability to access online database to store the vehicle's information. In this paper, the design of portable ANPR on android mobile phone will be described as follows. First, the graphical user interface (GUI) for capturing image using built-in camera was developed to acquire vehicle plate number in Malaysia. Second, the preprocessing of raw image was done using contrast enhancement. Next, character segmentation using fixed pitch and an optical character recognition (OCR) using neural network were utilized to extract texts and numbers. Both character segmentation and OCR were using Tesseract library from Google Inc. The proposed portable ANPR algorithm was implemented and simulated using Android SDK on a computer. Based on the experimental results, the proposed system can effectively recognize the license plate number at 90.86%. The required processing time to recognize a license plate is only 2 seconds on average. The result is consider good in comparison with the results obtained from previous system that was processed in a desktop PC with the range of result from 91.59% to 98% recognition rate and 0.284 second to 1.5 seconds recognition time.

Automatic gang graffiti recognition and interpretation

NASA Astrophysics Data System (ADS)

Parra, Albert; Boutin, Mireille; Delp, Edward J.

2017-09-01

One of the roles of emergency first responders (e.g., police and fire departments) is to prevent and protect against events that can jeopardize the safety and well-being of a community. In the case of criminal gang activity, tools are needed for finding, documenting, and taking the necessary actions to mitigate the problem or issue. We describe an integrated mobile-based system capable of using location-based services, combined with image analysis, to track and analyze gang activity through the acquisition, indexing, and recognition of gang graffiti images. This approach uses image analysis methods for color recognition, image segmentation, and image retrieval and classification. A database of gang graffiti images is described that includes not only the images but also metadata related to the images, such as date and time, geoposition, gang, gang member, colors, and symbols. The user can then query the data in a useful manner. We have implemented these features both as applications for Android and iOS hand-held devices and as a web-based interface.

Video-assisted segmentation of speech and audio track

NASA Astrophysics Data System (ADS)

Pandit, Medha; Yusoff, Yusseri; Kittler, Josef; Christmas, William J.; Chilton, E. H. S.

1999-08-01

Video database research is commonly concerned with the storage and retrieval of visual information invovling sequence segmentation, shot representation and video clip retrieval. In multimedia applications, video sequences are usually accompanied by a sound track. The sound track contains potential cues to aid shot segmentation such as different speakers, background music, singing and distinctive sounds. These different acoustic categories can be modeled to allow for an effective database retrieval. In this paper, we address the problem of automatic segmentation of audio track of multimedia material. This audio based segmentation can be combined with video scene shot detection in order to achieve partitioning of the multimedia material into semantically significant segments.

Research and Implementation of Tibetan Word Segmentation Based on Syllable Methods

NASA Astrophysics Data System (ADS)

Jiang, Jing; Li, Yachao; Jiang, Tao; Yu, Hongzhi

2018-03-01

Tibetan word segmentation (TWS) is an important problem in Tibetan information processing, while abbreviated word recognition is one of the key and most difficult problems in TWS. Most of the existing methods of Tibetan abbreviated word recognition are rule-based approaches, which need vocabulary support. In this paper, we propose a method based on sequence tagging model for abbreviated word recognition, and then implement in TWS systems with sequence labeling models. The experimental results show that our abbreviated word recognition method is fast and effective and can be combined easily with the segmentation model. This significantly increases the effect of the Tibetan word segmentation.

Assessing Counter-Terrorism field training with multiple behavioral measures.

PubMed

Spiker, V Alan; Johnston, Joan H

2013-09-01

Development of behavioral pattern recognition and analysis skills is an essential element of Counter-Terrorism training, particularly in the field. Three classes of behavioral measures were collected in an assessment of skill acquisition during a US Joint Forces Command-sponsored course consisting of Combat Tracking and Combat Profiling segments. Measures included situational judgment tests, structured behavioral observation checklists, and qualitative assessments of the emergence of specific knowledge-skills-attitudes over the course of the training. The paper describes statistical evidence across the three types of measures that indicate that behavioral pattern recognition and analysis skills were successfully acquired by most students (a mix of Army and civilian law enforcement personnel) during the field training exercises. Implications for broader training of these critical skills are also discussed. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Identification of Matra Region and Overlapping Characters for OCR of Printed Bengali Scripts

NASA Astrophysics Data System (ADS)

Goswami, Subhra Sundar

One of the important reasons for poor recognition rate in optical character recognition (OCR) system is the error in character segmentation. In case of Bangla scripts, the errors occur due to several reasons, which include incorrect detection of matra (headline), over-segmentation and under-segmentation. We have proposed a robust method for detecting the headline region. Existence of overlapping characters (in under-segmented parts) in scanned printed documents is a major problem in designing an effective character segmentation procedure for OCR systems. In this paper, a predictive algorithm is developed for effectively identifying overlapping characters and then selecting the cut-borders for segmentation. Our method can be successfully used in achieving high recognition result.

New approach for segmentation and recognition of handwritten numeral strings

NASA Astrophysics Data System (ADS)

Sadri, Javad; Suen, Ching Y.; Bui, Tien D.

2004-12-01

In this paper, we propose a new system for segmentation and recognition of unconstrained handwritten numeral strings. The system uses a combination of foreground and background features for segmentation of touching digits. The method introduces new algorithms for traversing the top/bottom-foreground-skeletons of the touched digits, and for finding feature points on these skeletons, and matching them to build all the segmentation paths. For the first time a genetic representation is used to show all the segmentation hypotheses. Our genetic algorithm tries to search and evolve the population of candidate segmentations and finds the one with the highest confidence for its segmentation and recognition. We have also used a new method for feature extraction which lowers the variations in the shapes of the digits, and then a MLP neural network is utilized to produce the labels and confidence values for those digits. The NIST SD19 and CENPARMI databases are used for evaluating the system. Our system can get a correct segmentation-recognition rate of 96.07% with rejection rate of 2.61% which compares favorably with those that exist in the literature.

New approach for segmentation and recognition of handwritten numeral strings

NASA Astrophysics Data System (ADS)

Sadri, Javad; Suen, Ching Y.; Bui, Tien D.

2005-01-01

In this paper, we propose a new system for segmentation and recognition of unconstrained handwritten numeral strings. The system uses a combination of foreground and background features for segmentation of touching digits. The method introduces new algorithms for traversing the top/bottom-foreground-skeletons of the touched digits, and for finding feature points on these skeletons, and matching them to build all the segmentation paths. For the first time a genetic representation is used to show all the segmentation hypotheses. Our genetic algorithm tries to search and evolve the population of candidate segmentations and finds the one with the highest confidence for its segmentation and recognition. We have also used a new method for feature extraction which lowers the variations in the shapes of the digits, and then a MLP neural network is utilized to produce the labels and confidence values for those digits. The NIST SD19 and CENPARMI databases are used for evaluating the system. Our system can get a correct segmentation-recognition rate of 96.07% with rejection rate of 2.61% which compares favorably with those that exist in the literature.

Segmental Rescoring in Text Recognition

DTIC Science & Technology

2014-02-04

description relates to rescoring text hypotheses in text recognition based on segmental features. Offline printed text and handwriting recognition (OHR) can... Handwriting , College Park, Md., 2006, which is incorporated by reference here. For the set of training images 202, a character modeler 208 receives

Implicit and Explicit Knowledge Both Improve Dual Task Performance in a Continuous Pursuit Tracking Task.

PubMed

Ewolds, Harald E; Bröker, Laura; de Oliveira, Rita F; Raab, Markus; Künzell, Stefan

2017-01-01

The goal of this study was to investigate the effect of predictability on dual-task performance in a continuous tracking task. Participants practiced either informed (explicit group) or uninformed (implicit group) about a repeated segment in the curves they had to track. In Experiment 1 participants practices the tracking task only, dual-task performance was assessed after by combining the tracking task with an auditory reaction time task. Results showed both groups learned equally well and tracking performance on a predictable segment in the dual-task condition was better than on random segments. However, reaction times did not benefit from a predictable tracking segment. To investigate the effect of learning under dual-task situation participants in Experiment 2 practiced the tracking task while simultaneously performing the auditory reaction time task. No learning of the repeated segment could be demonstrated for either group during the training blocks, in contrast to the test-block and retention test, where participants performed better on the repeated segment in both dual-task and single-task conditions. Only the explicit group improved from test-block to retention test. As in Experiment 1, reaction times while tracking a predictable segment were no better than reaction times while tracking a random segment. We concluded that predictability has a positive effect only on the predictable task itself possibly because of a task-shielding mechanism. For dual-task training there seems to be an initial negative effect of explicit instructions, possibly because of fatigue, but the advantage of explicit instructions was demonstrated in a retention test. This might be due to the explicit memory system informing or aiding the implicit memory system.

Implicit and Explicit Knowledge Both Improve Dual Task Performance in a Continuous Pursuit Tracking Task

PubMed Central

Ewolds, Harald E.; Bröker, Laura; de Oliveira, Rita F.; Raab, Markus; Künzell, Stefan

2017-01-01

The goal of this study was to investigate the effect of predictability on dual-task performance in a continuous tracking task. Participants practiced either informed (explicit group) or uninformed (implicit group) about a repeated segment in the curves they had to track. In Experiment 1 participants practices the tracking task only, dual-task performance was assessed after by combining the tracking task with an auditory reaction time task. Results showed both groups learned equally well and tracking performance on a predictable segment in the dual-task condition was better than on random segments. However, reaction times did not benefit from a predictable tracking segment. To investigate the effect of learning under dual-task situation participants in Experiment 2 practiced the tracking task while simultaneously performing the auditory reaction time task. No learning of the repeated segment could be demonstrated for either group during the training blocks, in contrast to the test-block and retention test, where participants performed better on the repeated segment in both dual-task and single-task conditions. Only the explicit group improved from test-block to retention test. As in Experiment 1, reaction times while tracking a predictable segment were no better than reaction times while tracking a random segment. We concluded that predictability has a positive effect only on the predictable task itself possibly because of a task-shielding mechanism. For dual-task training there seems to be an initial negative effect of explicit instructions, possibly because of fatigue, but the advantage of explicit instructions was demonstrated in a retention test. This might be due to the explicit memory system informing or aiding the implicit memory system. PMID:29312083

Image processing and recognition for biological images.

PubMed

Uchida, Seiichi

2013-05-01

This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the visibility of an input image and acquire some valuable information from it. As the main tasks of image processing, this paper introduces gray-level transformation, binarization, image filtering, image segmentation, visual object tracking, optical flow and image registration. Image pattern recognition is the technique to classify an input image into one of the predefined classes and also has a large research area. This paper overviews its two main modules, that is, feature extraction module and classification module. Throughout the paper, it will be emphasized that bioimage is a very difficult target for even state-of-the-art image processing and pattern recognition techniques due to noises, deformations, etc. This paper is expected to be one tutorial guide to bridge biology and image processing researchers for their further collaboration to tackle such a difficult target. © 2013 The Author Development, Growth & Differentiation © 2013 Japanese Society of Developmental Biologists.

Crowd motion segmentation and behavior recognition fusing streak flow and collectiveness

NASA Astrophysics Data System (ADS)

Gao, Mingliang; Jiang, Jun; Shen, Jin; Zou, Guofeng; Fu, Guixia

2018-04-01

Crowd motion segmentation and crowd behavior recognition are two hot issues in computer vision. A number of methods have been proposed to tackle these two problems. Among the methods, flow dynamics is utilized to model the crowd motion, with little consideration of collective property. Moreover, the traditional crowd behavior recognition methods treat the local feature and dynamic feature separately and overlook the interconnection of topological and dynamical heterogeneity in complex crowd processes. A crowd motion segmentation method and a crowd behavior recognition method are proposed based on streak flow and crowd collectiveness. The streak flow is adopted to reveal the dynamical property of crowd motion, and the collectiveness is incorporated to reveal the structure property. Experimental results show that the proposed methods improve the crowd motion segmentation accuracy and the crowd recognition rates compared with the state-of-the-art methods.

Active Multimodal Sensor System for Target Recognition and Tracking

PubMed Central

Zhang, Guirong; Zou, Zhaofan; Liu, Ziyue; Mao, Jiansen

2017-01-01

High accuracy target recognition and tracking systems using a single sensor or a passive multisensor set are susceptible to external interferences and exhibit environmental dependencies. These difficulties stem mainly from limitations to the available imaging frequency bands, and a general lack of coherent diversity of the available target-related data. This paper proposes an active multimodal sensor system for target recognition and tracking, consisting of a visible, an infrared, and a hyperspectral sensor. The system makes full use of its multisensor information collection abilities; furthermore, it can actively control different sensors to collect additional data, according to the needs of the real-time target recognition and tracking processes. This level of integration between hardware collection control and data processing is experimentally shown to effectively improve the accuracy and robustness of the target recognition and tracking system. PMID:28657609

Computerized analysis of coronary artery disease: Performance evaluation of segmentation and tracking of coronary arteries in CT angiograms

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

Zhou, Chuan, E-mail: chuan@umich.edu; Chan, Heang-Ping; Chughtai, Aamer

2014-08-15

Purpose: The authors are developing a computer-aided detection system to assist radiologists in analysis of coronary artery disease in coronary CT angiograms (cCTA). This study evaluated the accuracy of the authors’ coronary artery segmentation and tracking method which are the essential steps to define the search space for the detection of atherosclerotic plaques. Methods: The heart region in cCTA is segmented and the vascular structures are enhanced using the authors’ multiscale coronary artery response (MSCAR) method that performed 3D multiscale filtering and analysis of the eigenvalues of Hessian matrices. Starting from seed points at the origins of the left andmore » right coronary arteries, a 3D rolling balloon region growing (RBG) method that adapts to the local vessel size segmented and tracked each of the coronary arteries and identifies the branches along the tracked vessels. The branches are queued and subsequently tracked until the queue is exhausted. With Institutional Review Board approval, 62 cCTA were collected retrospectively from the authors’ patient files. Three experienced cardiothoracic radiologists manually tracked and marked center points of the coronary arteries as reference standard following the 17-segment model that includes clinically significant coronary arteries. Two radiologists visually examined the computer-segmented vessels and marked the mistakenly tracked veins and noisy structures as false positives (FPs). For the 62 cases, the radiologists marked a total of 10191 center points on 865 visible coronary artery segments. Results: The computer-segmented vessels overlapped with 83.6% (8520/10191) of the center points. Relative to the 865 radiologist-marked segments, the sensitivity reached 91.9% (795/865) if a true positive is defined as a computer-segmented vessel that overlapped with at least 10% of the reference center points marked on the segment. When the overlap threshold is increased to 50% and 100%, the sensitivities were 86.2% and 53.4%, respectively. For the 62 test cases, a total of 55 FPs were identified by radiologist in 23 of the cases. Conclusions: The authors’ MSCAR-RBG method achieved high sensitivity for coronary artery segmentation and tracking. Studies are underway to further improve the accuracy for the arterial segments affected by motion artifacts, severe calcified and noncalcified soft plaques, and to reduce the false tracking of the veins and other noisy structures. Methods are also being developed to detect coronary artery disease along the tracked vessels.« less

Pupil Tracking for Real-Time Motion Corrected Anterior Segment Optical Coherence Tomography

PubMed Central

Carrasco-Zevallos, Oscar M.; Nankivil, Derek; Viehland, Christian; Keller, Brenton; Izatt, Joseph A.

2016-01-01

Volumetric acquisition with anterior segment optical coherence tomography (ASOCT) is necessary to obtain accurate representations of the tissue structure and to account for asymmetries of the anterior eye anatomy. Additionally, recent interest in imaging of anterior segment vasculature and aqueous humor flow resulted in application of OCT angiography techniques to generate en face and 3D micro-vasculature maps of the anterior segment. Unfortunately, ASOCT structural and vasculature imaging systems do not capture volumes instantaneously and are subject to motion artifacts due to involuntary eye motion that may hinder their accuracy and repeatability. Several groups have demonstrated real-time tracking for motion-compensated in vivo OCT retinal imaging, but these techniques are not applicable in the anterior segment. In this work, we demonstrate a simple and low-cost pupil tracking system integrated into a custom swept-source OCT system for real-time motion-compensated anterior segment volumetric imaging. Pupil oculography hardware coaxial with the swept-source OCT system enabled fast detection and tracking of the pupil centroid. The pupil tracking ASOCT system with a field of view of 15 x 15 mm achieved diffraction-limited imaging over a lateral tracking range of +/- 2.5 mm and was able to correct eye motion at up to 22 Hz. Pupil tracking ASOCT offers a novel real-time motion compensation approach that may facilitate accurate and reproducible anterior segment imaging. PMID:27574800

Gait recognition based on Gabor wavelets and modified gait energy image for human identification

NASA Astrophysics Data System (ADS)

Huang, Deng-Yuan; Lin, Ta-Wei; Hu, Wu-Chih; Cheng, Chih-Hsiang

2013-10-01

This paper proposes a method for recognizing human identity using gait features based on Gabor wavelets and modified gait energy images (GEIs). Identity recognition by gait generally involves gait representation, extraction, and classification. In this work, a modified GEI convolved with an ensemble of Gabor wavelets is proposed as a gait feature. Principal component analysis is then used to project the Gabor-wavelet-based gait features into a lower-dimension feature space for subsequent classification. Finally, support vector machine classifiers based on a radial basis function kernel are trained and utilized to recognize human identity. The major contributions of this paper are as follows: (1) the consideration of the shadow effect to yield a more complete segmentation of gait silhouettes; (2) the utilization of motion estimation to track people when walkers overlap; and (3) the derivation of modified GEIs to extract more useful gait information. Extensive performance evaluation shows a great improvement of recognition accuracy due to the use of shadow removal, motion estimation, and gait representation using the modified GEIs and Gabor wavelets.

On Integral Invariants for Effective 3-D Motion Trajectory Matching and Recognition.

PubMed

Shao, Zhanpeng; Li, Youfu

2016-02-01

Motion trajectories tracked from the motions of human, robots, and moving objects can provide an important clue for motion analysis, classification, and recognition. This paper defines some new integral invariants for a 3-D motion trajectory. Based on two typical kernel functions, we design two integral invariants, the distance and area integral invariants. The area integral invariants are estimated based on the blurred segment of noisy discrete curve to avoid the computation of high-order derivatives. Such integral invariants for a motion trajectory enjoy some desirable properties, such as computational locality, uniqueness of representation, and noise insensitivity. Moreover, our formulation allows the analysis of motion trajectories at a range of scales by varying the scale of kernel function. The features of motion trajectories can thus be perceived at multiscale levels in a coarse-to-fine manner. Finally, we define a distance function to measure the trajectory similarity to find similar trajectories. Through the experiments, we examine the robustness and effectiveness of the proposed integral invariants and find that they can capture the motion cues in trajectory matching and sign recognition satisfactorily.

Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition

PubMed Central

Shenoy, Varun N.; Aalami, Oliver O.

2017-01-01

Biometric measurements captured from medical devices, such as blood pressure gauges, glucose monitors, and weighing scales, are essential to tracking a patient’s health. Trends in these measurements can accurately track diabetes, cardiovascular issues, and assist medication management for patients. Currently, patients record their results and date of measurement in a physical notebook. It may be weeks before a doctor sees a patient’s records and can assess the health of the patient. With a predicted 6.8 billion smartphones in the world by 20221, health monitoring platforms, such as Apple’s HealthKit2, can be leveraged to provide the right care at the right time. This research presents a mobile application that enables users to capture medical monitor data and send it to their doctor swiftly. A key contribution of this paper is a robust engine that can recognize digits from medical monitors with an accuracy of 98.2%. PMID:29854226

Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition.

PubMed

Shenoy, Varun N; Aalami, Oliver O

2017-01-01

Biometric measurements captured from medical devices, such as blood pressure gauges, glucose monitors, and weighing scales, are essential to tracking a patient's health. Trends in these measurements can accurately track diabetes, cardiovascular issues, and assist medication management for patients. Currently, patients record their results and date of measurement in a physical notebook. It may be weeks before a doctor sees a patient's records and can assess the health of the patient. With a predicted 6.8 billion smartphones in the world by 2022 1 , health monitoring platforms, such as Apple's HealthKit 2 , can be leveraged to provide the right care at the right time. This research presents a mobile application that enables users to capture medical monitor data and send it to their doctor swiftly. A key contribution of this paper is a robust engine that can recognize digits from medical monitors with an accuracy of 98.2%.

Attentional focus affects how events are segmented and updated in narrative reading.

PubMed

Bailey, Heather R; Kurby, Christopher A; Sargent, Jesse Q; Zacks, Jeffrey M

2017-08-01

Readers generate situation models representing described events, but the nature of these representations may differ depending on the reading goals. We assessed whether instructions to pay attention to different situational dimensions affect how individuals structure their situation models (Exp. 1) and how they update these models when situations change (Exp. 2). In Experiment 1, participants read and segmented narrative texts into events. Some readers were oriented to pay specific attention to characters or space. Sentences containing character or spatial-location changes were perceived as event boundaries-particularly if the reader was oriented to characters or space, respectively. In Experiment 2, participants read narratives and responded to recognition probes throughout the texts. Readers who were oriented to the spatial dimension were more likely to update their situation models at spatial changes; all readers tracked the character dimension. The results from both experiments indicated that attention to individual situational dimensions influences how readers segment and update their situation models. More broadly, the results provide evidence for a global situation model updating mechanism that serves to set up new models at important narrative changes.

Text Detection, Tracking and Recognition in Video: A Comprehensive Survey.

PubMed

Yin, Xu-Cheng; Zuo, Ze-Yu; Tian, Shu; Liu, Cheng-Lin

2016-04-14

Intelligent analysis of video data is currently in wide demand because video is a major source of sensory data in our lives. Text is a prominent and direct source of information in video, while recent surveys of text detection and recognition in imagery [1], [2] focus mainly on text extraction from scene images. Here, this paper presents a comprehensive survey of text detection, tracking and recognition in video with three major contributions. First, a generic framework is proposed for video text extraction that uniformly describes detection, tracking, recognition, and their relations and interactions. Second, within this framework, a variety of methods, systems and evaluation protocols of video text extraction are summarized, compared, and analyzed. Existing text tracking techniques, tracking based detection and recognition techniques are specifically highlighted. Third, related applications, prominent challenges, and future directions for video text extraction (especially from scene videos and web videos) are also thoroughly discussed.

Post processing for offline Chinese handwritten character string recognition

NASA Astrophysics Data System (ADS)

Wang, YanWei; Ding, XiaoQing; Liu, ChangSong

2012-01-01

Offline Chinese handwritten character string recognition is one of the most important research fields in pattern recognition. Due to the free writing style, large variability in character shapes and different geometric characteristics, Chinese handwritten character string recognition is a challenging problem to deal with. However, among the current methods over-segmentation and merging method which integrates geometric information, character recognition information and contextual information, shows a promising result. It is found experimentally that a large part of errors are segmentation error and mainly occur around non-Chinese characters. In a Chinese character string, there are not only wide characters namely Chinese characters, but also narrow characters like digits and letters of the alphabet. The segmentation error is mainly caused by uniform geometric model imposed on all segmented candidate characters. To solve this problem, post processing is employed to improve recognition accuracy of narrow characters. On one hand, multi-geometric models are established for wide characters and narrow characters respectively. Under multi-geometric models narrow characters are not prone to be merged. On the other hand, top rank recognition results of candidate paths are integrated to boost final recognition of narrow characters. The post processing method is investigated on two datasets, in total 1405 handwritten address strings. The wide character recognition accuracy has been improved lightly and narrow character recognition accuracy has been increased up by 10.41% and 10.03% respectively. It indicates that the post processing method is effective to improve recognition accuracy of narrow characters.

Towards online iris and periocular recognition under relaxed imaging constraints.

PubMed

Tan, Chun-Wei; Kumar, Ajay

2013-10-01

Online iris recognition using distantly acquired images in a less imaging constrained environment requires the development of a efficient iris segmentation approach and recognition strategy that can exploit multiple features available for the potential identification. This paper presents an effective solution toward addressing such a problem. The developed iris segmentation approach exploits a random walker algorithm to efficiently estimate coarsely segmented iris images. These coarsely segmented iris images are postprocessed using a sequence of operations that can effectively improve the segmentation accuracy. The robustness of the proposed iris segmentation approach is ascertained by providing comparison with other state-of-the-art algorithms using publicly available UBIRIS.v2, FRGC, and CASIA.v4-distance databases. Our experimental results achieve improvement of 9.5%, 4.3%, and 25.7% in the average segmentation accuracy, respectively, for the UBIRIS.v2, FRGC, and CASIA.v4-distance databases, as compared with most competing approaches. We also exploit the simultaneously extracted periocular features to achieve significant performance improvement. The joint segmentation and combination strategy suggest promising results and achieve average improvement of 132.3%, 7.45%, and 17.5% in the recognition performance, respectively, from the UBIRIS.v2, FRGC, and CASIA.v4-distance databases, as compared with the related competing approaches.

Cooperative multisensor system for real-time face detection and tracking in uncontrolled conditions

NASA Astrophysics Data System (ADS)

Marchesotti, Luca; Piva, Stefano; Turolla, Andrea; Minetti, Deborah; Regazzoni, Carlo S.

2005-03-01

The presented work describes an innovative architecture for multi-sensor distributed video surveillance applications. The aim of the system is to track moving objects in outdoor environments with a cooperative strategy exploiting two video cameras. The system also exhibits the capacity of focusing its attention on the faces of detected pedestrians collecting snapshot frames of face images, by segmenting and tracking them over time at different resolution. The system is designed to employ two video cameras in a cooperative client/server structure: the first camera monitors the entire area of interest and detects the moving objects using change detection techniques. The detected objects are tracked over time and their position is indicated on a map representing the monitored area. The objects" coordinates are sent to the server sensor in order to point its zooming optics towards the moving object. The second camera tracks the objects at high resolution. As well as the client camera, this sensor is calibrated and the position of the object detected on the image plane reference system is translated in its coordinates referred to the same area map. In the map common reference system, data fusion techniques are applied to achieve a more precise and robust estimation of the objects" track and to perform face detection and tracking. The work novelties and strength reside in the cooperative multi-sensor approach, in the high resolution long distance tracking and in the automatic collection of biometric data such as a person face clip for recognition purposes.

Surgical gesture segmentation and recognition.

PubMed

Tao, Lingling; Zappella, Luca; Hager, Gregory D; Vidal, René

2013-01-01

Automatic surgical gesture segmentation and recognition can provide useful feedback for surgical training in robotic surgery. Most prior work in this field relies on the robot's kinematic data. Although recent work [1,2] shows that the robot's video data can be equally effective for surgical gesture recognition, the segmentation of the video into gestures is assumed to be known. In this paper, we propose a framework for joint segmentation and recognition of surgical gestures from kinematic and video data. Unlike prior work that relies on either frame-level kinematic cues, or segment-level kinematic or video cues, our approach exploits both cues by using a combined Markov/semi-Markov conditional random field (MsM-CRF) model. Our experiments show that the proposed model improves over a Markov or semi-Markov CRF when using video data alone, gives results that are comparable to state-of-the-art methods on kinematic data alone, and improves over state-of-the-art methods when combining kinematic and video data.

An Algorithm to Automate Yeast Segmentation and Tracking

PubMed Central

Doncic, Andreas; Eser, Umut; Atay, Oguzhan; Skotheim, Jan M.

2013-01-01

Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation. PMID:23520484

New approach for logo recognition

NASA Astrophysics Data System (ADS)

Chen, Jingying; Leung, Maylor K. H.; Gao, Yongsheng

2000-03-01

The problem of logo recognition is of great interest in the document domain, especially for document database. By recognizing the logo we obtain semantic information about the document which may be useful in deciding whether or not to analyze the textual components. In order to develop a logo recognition method that is efficient to compute and product intuitively reasonable results, we investigate the Line Segment Hausdorff Distance on logo recognition. Researchers apply Hausdorff Distance to measure the dissimilarity of two point sets. It has been extended to match two sets of line segments. The new approach has the advantage to incorporate structural and spatial information to compute the dissimilarity. The added information can conceptually provide more and better distinctive capability for recognition. The proposed technique has been applied on line segments of logos with encouraging results that support the concept experimentally. This might imply a new way for logo recognition.

Leveraging Automatic Speech Recognition Errors to Detect Challenging Speech Segments in TED Talks

ERIC Educational Resources Information Center

Mirzaei, Maryam Sadat; Meshgi, Kourosh; Kawahara, Tatsuya

2016-01-01

This study investigates the use of Automatic Speech Recognition (ASR) systems to epitomize second language (L2) listeners' problems in perception of TED talks. ASR-generated transcripts of videos often involve recognition errors, which may indicate difficult segments for L2 listeners. This paper aims to discover the root-causes of the ASR errors…

The Roles of Tonal and Segmental Information in Mandarin Spoken Word Recognition: An Eyetracking Study

ERIC Educational Resources Information Center

Malins, Jeffrey G.; Joanisse, Marc F.

2010-01-01

We used eyetracking to examine how tonal versus segmental information influence spoken word recognition in Mandarin Chinese. Participants heard an auditory word and were required to identify its corresponding picture from an array that included the target item ("chuang2" "bed"), a phonological competitor (segmental: chuang1 "window"; cohort:…

View-Invariant Gait Recognition Through Genetic Template Segmentation

NASA Astrophysics Data System (ADS)

Isaac, Ebenezer R. H. P.; Elias, Susan; Rajagopalan, Srinivasan; Easwarakumar, K. S.

2017-08-01

Template-based model-free approach provides by far the most successful solution to the gait recognition problem in literature. Recent work discusses how isolating the head and leg portion of the template increase the performance of a gait recognition system making it robust against covariates like clothing and carrying conditions. However, most involve a manual definition of the boundaries. The method we propose, the genetic template segmentation (GTS), employs the genetic algorithm to automate the boundary selection process. This method was tested on the GEI, GEnI and AEI templates. GEI seems to exhibit the best result when segmented with our approach. Experimental results depict that our approach significantly outperforms the existing implementations of view-invariant gait recognition.

A unified framework for gesture recognition and spatiotemporal gesture segmentation.

PubMed

Alon, Jonathan; Athitsos, Vassilis; Yuan, Quan; Sclaroff, Stan

2009-09-01

Within the context of hand gesture recognition, spatiotemporal gesture segmentation is the task of determining, in a video sequence, where the gesturing hand is located and when the gesture starts and ends. Existing gesture recognition methods typically assume either known spatial segmentation or known temporal segmentation, or both. This paper introduces a unified framework for simultaneously performing spatial segmentation, temporal segmentation, and recognition. In the proposed framework, information flows both bottom-up and top-down. A gesture can be recognized even when the hand location is highly ambiguous and when information about when the gesture begins and ends is unavailable. Thus, the method can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds. The proposed method consists of three novel contributions: a spatiotemporal matching algorithm that can accommodate multiple candidate hand detections in every frame, a classifier-based pruning framework that enables accurate and early rejection of poor matches to gesture models, and a subgesture reasoning algorithm that learns which gesture models can falsely match parts of other longer gestures. The performance of the approach is evaluated on two challenging applications: recognition of hand-signed digits gestured by users wearing short-sleeved shirts, in front of a cluttered background, and retrieval of occurrences of signs of interest in a video database containing continuous, unsegmented signing in American Sign Language (ASL).

SU-D-201-05: On the Automatic Recognition of Patient Safety Hazards in a Radiotherapy Setup Using a Novel 3D Camera System and a Deep Learning Framework

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

Santhanam, A; Min, Y; Beron, P

Purpose: Patient safety hazards such as a wrong patient/site getting treated can lead to catastrophic results. The purpose of this project is to automatically detect potential patient safety hazards during the radiotherapy setup and alert the therapist before the treatment is initiated. Methods: We employed a set of co-located and co-registered 3D cameras placed inside the treatment room. Each camera provided a point-cloud of fraxels (fragment pixels with 3D depth information). Each of the cameras were calibrated using a custom-built calibration target to provide 3D information with less than 2 mm error in the 500 mm neighborhood around the isocenter.more » To identify potential patient safety hazards, the treatment room components and the patient’s body needed to be identified and tracked in real-time. For feature recognition purposes, we used a graph-cut based feature recognition with principal component analysis (PCA) based feature-to-object correlation to segment the objects in real-time. Changes in the object’s position were tracked using the CamShift algorithm. The 3D object information was then stored for each classified object (e.g. gantry, couch). A deep learning framework was then used to analyze all the classified objects in both 2D and 3D and was then used to fine-tune a convolutional network for object recognition. The number of network layers were optimized to identify the tracked objects with >95% accuracy. Results: Our systematic analyses showed that, the system was effectively able to recognize wrong patient setups and wrong patient accessories. The combined usage of 2D camera information (color + depth) enabled a topology-preserving approach to verify patient safety hazards in an automatic manner and even in scenarios where the depth information is partially available. Conclusion: By utilizing the 3D cameras inside the treatment room and a deep learning based image classification, potential patient safety hazards can be effectively avoided.« less

Multi-font printed Mongolian document recognition system

NASA Astrophysics Data System (ADS)

Peng, Liangrui; Liu, Changsong; Ding, Xiaoqing; Wang, Hua; Jin, Jianming

2009-01-01

Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of projection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.

Research into automatic recognition of joints in human symmetrical movements

NASA Astrophysics Data System (ADS)

Fan, Yifang; Li, Zhiyu

2008-03-01

High speed photography is a major means of collecting data from human body movement. It enables the automatic identification of joints, which brings great significance to the research, treatment and recovery of injuries, the analysis to the diagnosis of sport techniques and the ergonomics. According to the features that when the adjacent joints of human body are in planetary motion, their distance remains the same, and according to the human body joint movement laws (such as the territory of the articular anatomy and the kinematic features), a new approach is introduced to process the image thresholding of joints filmed by the high speed camera, to automatically identify the joints and to automatically trace the joint points (by labeling markers at the joints). Based upon the closure of marking points, automatic identification can be achieved through thresholding treatment. Due to the screening frequency and the laws of human segment movement, when the marking points have been initialized, their automatic tracking can be achieved with the progressive sequential images.Then the testing results, the data from three-dimensional force platform and the characteristics that human body segment will only rotate around the closer ending segment when the segment has no boding force and only valid to the conservative force all tell that after being analyzed kinematically, the approach is approved to be valid.

Survey of Technologies for the Airport Border of the Future

DTIC Science & Technology

2014-04-01

geometry Handwriting recognition ID cards Image classification Image enhancement Image fusion Image matching Image processing Image segmentation Iris...00 Tongue print Footstep recognition Odour recognition Retinal recognition Emotion recognition Periocular recognition Handwriting recognition Ear...recognition Palmprint recognition Hand geometry DNA matching Vein matching Ear recognition Handwriting recognition Periocular recognition Emotion

Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition.

PubMed

Wong, Sebastien C; Stamatescu, Victor; Gatt, Adam; Kearney, David; Lee, Ivan; McDonnell, Mark D

2017-10-01

This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fast-learning image classifier, that is based on a shallow convolutional neural network architecture and demonstrate that object recognition improves when this is combined with object state information from the tracking algorithm. We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage, we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types. We describe our biologically inspired implementation, which adaptively learns the shape and motion of tracked objects, and apply it to the Neovision2 Tower benchmark data set, which contains multiple object types. An experimental evaluation demonstrates that our approach is competitive with the state-of-the-art video object recognition systems that do make use of object-specific prior knowledge in detection and tracking, while providing additional practical advantages by virtue of its generality.

Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils.

PubMed

Brandes, Susanne; Mokhtari, Zeinab; Essig, Fabian; Hünniger, Kerstin; Kurzai, Oliver; Figge, Marc Thilo

2015-02-01

Time-lapse microscopy is an important technique to study the dynamics of various biological processes. The labor-intensive manual analysis of microscopy videos is increasingly replaced by automated segmentation and tracking methods. These methods are often limited to certain cell morphologies and/or cell stainings. In this paper, we present an automated segmentation and tracking framework that does not have these restrictions. In particular, our framework handles highly variable cell shapes and does not rely on any cell stainings. Our segmentation approach is based on a combination of spatial and temporal image variations to detect moving cells in microscopy videos. This method yields a sensitivity of 99% and a precision of 95% in object detection. The tracking of cells consists of different steps, starting from single-cell tracking based on a nearest-neighbor-approach, detection of cell-cell interactions and splitting of cell clusters, and finally combining tracklets using methods from graph theory. The segmentation and tracking framework was applied to synthetic as well as experimental datasets with varying cell densities implying different numbers of cell-cell interactions. We established a validation framework to measure the performance of our tracking technique. The cell tracking accuracy was found to be >99% for all datasets indicating a high accuracy for connecting the detected cells between different time points. Copyright © 2014 Elsevier B.V. All rights reserved.

A Complete OCR System for Tamil Magazine Documents

NASA Astrophysics Data System (ADS)

Kokku, Aparna; Chakravarthy, Srinivasa

We present a complete optical character recognition (OCR) system for Tamil magazines/documents. All the standard elements of OCR process like de-skewing, preprocessing, segmentation, character recognition, and reconstruction are implemented. Experience with OCR problems teaches that for most subtasks of OCR, there is no single technique that gives perfect results for every type of document image. We exploit the ability of neural networks to learn from experience in solving the problems of segmentation and character recognition. Text segmentation of Tamil newsprint poses a new challenge owing to its italic-like font type; problems that arise in recognition of touching and close characters are discussed. Character recognition efficiency varied from 94 to 97% for this type of font. The grouping of blocks into logical units and the determination of reading order within each logical unit helped us in reconstructing automatically the document image in an editable format.

Biased figure-ground assignment affects conscious object recognition in spatial neglect.

PubMed

Eramudugolla, Ranmalee; Driver, Jon; Mattingley, Jason B

2010-09-01

Unilateral spatial neglect is a disorder of attention and spatial representation, in which early visual processes such as figure-ground segmentation have been assumed to be largely intact. There is evidence, however, that the spatial attention bias underlying neglect can bias the segmentation of a figural region from its background. Relatively few studies have explicitly examined the effect of spatial neglect on processing the figures that result from such scene segmentation. Here, we show that a neglect patient's bias in figure-ground segmentation directly influences his conscious recognition of these figures. By varying the relative salience of figural and background regions in static, two-dimensional displays, we show that competition between elements in such displays can modulate a neglect patient's ability to recognise parsed figures in a scene. The findings provide insight into the interaction between scene segmentation, explicit object recognition, and attention.

Is visual image segmentation a bottom-up or an interactive process?

PubMed

Vecera, S P; Farah, M J

1997-11-01

Visual image segmentation is the process by which the visual system groups features that are part of a single shape. Is image segmentation a bottom-up or an interactive process? In Experiments 1 and 2, we presented subjects with two overlapping shapes and asked them to determine whether two probed locations were on the same shape or on different shapes. The availability of top-down support was manipulated by presenting either upright or rotated letters. Subjects were fastest to respond when the shapes corresponded to familiar shapes--the upright letters. In Experiment 3, we used a variant of this segmentation task to rule out the possibility that subjects performed same/different judgments after segmentation and recognition of both letters. Finally, in Experiment 4, we ruled out the possibility that the advantage for upright letters was merely due to faster recognition of upright letters relative to rotated letters. The results suggested that the previous effects were not due to faster recognition of upright letters; stimulus familiarity influenced segmentation per se. The results are discussed in terms of an interactive model of visual image segmentation.

Exploiting range imagery: techniques and applications

NASA Astrophysics Data System (ADS)

Armbruster, Walter

2009-07-01

Practically no applications exist for which automatic processing of 2D intensity imagery can equal human visual perception. This is not the case for range imagery. The paper gives examples of 3D laser radar applications, for which automatic data processing can exceed human visual cognition capabilities and describes basic processing techniques for attaining these results. The examples are drawn from the fields of helicopter obstacle avoidance, object detection in surveillance applications, object recognition at high range, multi-object-tracking, and object re-identification in range image sequences. Processing times and recognition performances are summarized. The techniques used exploit the bijective continuity of the imaging process as well as its independence of object reflectivity, emissivity and illumination. This allows precise formulations of the probability distributions involved in figure-ground segmentation, feature-based object classification and model based object recognition. The probabilistic approach guarantees optimal solutions for single images and enables Bayesian learning in range image sequences. Finally, due to recent results in 3D-surface completion, no prior model libraries are required for recognizing and re-identifying objects of quite general object categories, opening the way to unsupervised learning and fully autonomous cognitive systems.

Vehicle track segmentation using higher order random fields

DOE PAGES

Quach, Tu -Thach

2017-01-09

Here, we present an approach to segment vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times. The approach uses multiscale higher order random field models to capture track statistics, such as curvatures and their parallel nature, that are not currently utilized in existing methods. These statistics are encoded as 3-by-3 patterns at different scales. The model can complete disconnected tracks often caused by sensor noise and various environmental effects. Coupling the model with a simple classifier, our approach is effective at segmenting salient tracks. We improve the F-measure onmore » a standard vehicle track data set to 0.963, up from 0.897 obtained by the current state-of-the-art method.« less

Vehicle track segmentation using higher order random fields

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

Quach, Tu -Thach

Here, we present an approach to segment vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times. The approach uses multiscale higher order random field models to capture track statistics, such as curvatures and their parallel nature, that are not currently utilized in existing methods. These statistics are encoded as 3-by-3 patterns at different scales. The model can complete disconnected tracks often caused by sensor noise and various environmental effects. Coupling the model with a simple classifier, our approach is effective at segmenting salient tracks. We improve the F-measure onmore » a standard vehicle track data set to 0.963, up from 0.897 obtained by the current state-of-the-art method.« less

Sensor-Aware Recognition and Tracking for Wide-Area Augmented Reality on Mobile Phones

PubMed Central

Chen, Jing; Cao, Ruochen; Wang, Yongtian

2015-01-01

Wide-area registration in outdoor environments on mobile phones is a challenging task in mobile augmented reality fields. We present a sensor-aware large-scale outdoor augmented reality system for recognition and tracking on mobile phones. GPS and gravity information is used to improve the VLAD performance for recognition. A kind of sensor-aware VLAD algorithm, which is self-adaptive to different scale scenes, is utilized to recognize complex scenes. Considering vision-based registration algorithms are too fragile and tend to drift, data coming from inertial sensors and vision are fused together by an extended Kalman filter (EKF) to achieve considerable improvements in tracking stability and robustness. Experimental results show that our method greatly enhances the recognition rate and eliminates the tracking jitters. PMID:26690439

Sensor-Aware Recognition and Tracking for Wide-Area Augmented Reality on Mobile Phones.

PubMed

Chen, Jing; Cao, Ruochen; Wang, Yongtian

2015-12-10

Wide-area registration in outdoor environments on mobile phones is a challenging task in mobile augmented reality fields. We present a sensor-aware large-scale outdoor augmented reality system for recognition and tracking on mobile phones. GPS and gravity information is used to improve the VLAD performance for recognition. A kind of sensor-aware VLAD algorithm, which is self-adaptive to different scale scenes, is utilized to recognize complex scenes. Considering vision-based registration algorithms are too fragile and tend to drift, data coming from inertial sensors and vision are fused together by an extended Kalman filter (EKF) to achieve considerable improvements in tracking stability and robustness. Experimental results show that our method greatly enhances the recognition rate and eliminates the tracking jitters.

Segmentation and Recognition of Continuous Human Activity

DTIC Science & Technology

2001-01-01

This paper presents a methodology for automatic segmentation and recognition of continuous human activity . We segment a continuous human activity into...commencement or termination. We use single action sequences for the training data set. The test sequences, on the other hand, are continuous sequences of human ... activity that consist of three or more actions in succession. The system has been tested on continuous activity sequences containing actions such as

Accumulative Difference Image Protocol for Particle Tracking in Fluorescence Microscopy Tested in Mouse Lymphonodes

PubMed Central

Villa, Carlo E.; Caccia, Michele; Sironi, Laura; D'Alfonso, Laura; Collini, Maddalena; Rivolta, Ilaria; Miserocchi, Giuseppe; Gorletta, Tatiana; Zanoni, Ivan; Granucci, Francesca; Chirico, Giuseppe

2010-01-01

The basic research in cell biology and in medical sciences makes large use of imaging tools mainly based on confocal fluorescence and, more recently, on non-linear excitation microscopy. Substantially the aim is the recognition of selected targets in the image and their tracking in time. We have developed a particle tracking algorithm optimized for low signal/noise images with a minimum set of requirements on the target size and with no a priori knowledge of the type of motion. The image segmentation, based on a combination of size sensitive filters, does not rely on edge detection and is tailored for targets acquired at low resolution as in most of the in-vivo studies. The particle tracking is performed by building, from a stack of Accumulative Difference Images, a single 2D image in which the motion of the whole set of the particles is coded in time by a color level. This algorithm, tested here on solid-lipid nanoparticles diffusing within cells and on lymphocytes diffusing in lymphonodes, appears to be particularly useful for the cellular and the in-vivo microscopy image processing in which few a priori assumption on the type, the extent and the variability of particle motions, can be done. PMID:20808918

Accumulative difference image protocol for particle tracking in fluorescence microscopy tested in mouse lymphonodes.

PubMed

Villa, Carlo E; Caccia, Michele; Sironi, Laura; D'Alfonso, Laura; Collini, Maddalena; Rivolta, Ilaria; Miserocchi, Giuseppe; Gorletta, Tatiana; Zanoni, Ivan; Granucci, Francesca; Chirico, Giuseppe

2010-08-17

The basic research in cell biology and in medical sciences makes large use of imaging tools mainly based on confocal fluorescence and, more recently, on non-linear excitation microscopy. Substantially the aim is the recognition of selected targets in the image and their tracking in time. We have developed a particle tracking algorithm optimized for low signal/noise images with a minimum set of requirements on the target size and with no a priori knowledge of the type of motion. The image segmentation, based on a combination of size sensitive filters, does not rely on edge detection and is tailored for targets acquired at low resolution as in most of the in-vivo studies. The particle tracking is performed by building, from a stack of Accumulative Difference Images, a single 2D image in which the motion of the whole set of the particles is coded in time by a color level. This algorithm, tested here on solid-lipid nanoparticles diffusing within cells and on lymphocytes diffusing in lymphonodes, appears to be particularly useful for the cellular and the in-vivo microscopy image processing in which few a priori assumption on the type, the extent and the variability of particle motions, can be done.

Ureter tracking and segmentation in CT urography (CTU) using COMPASS

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

Hadjiiski, Lubomir, E-mail: lhadjisk@umich.edu; Zick, David; Chan, Heang-Ping

2014-12-15

Purpose: The authors are developing a computerized system for automated segmentation of ureters in CTU, referred to as combined model-guided path-finding analysis and segmentation system (COMPASS). Ureter segmentation is a critical component for computer-aided diagnosis of ureter cancer. Methods: COMPASS consists of three stages: (1) rule-based adaptive thresholding and region growing, (2) path-finding and propagation, and (3) edge profile extraction and feature analysis. With institutional review board approval, 79 CTU scans performed with intravenous (IV) contrast material enhancement were collected retrospectively from 79 patient files. One hundred twenty-four ureters were selected from the 79 CTU volumes. On average, the uretersmore » spanned 283 computed tomography slices (range: 116–399, median: 301). More than half of the ureters contained malignant or benign lesions and some had ureter wall thickening due to malignancy. A starting point for each of the 124 ureters was identified manually to initialize the tracking by COMPASS. In addition, the centerline of each ureter was manually marked and used as reference standard for evaluation of tracking performance. The performance of COMPASS was quantitatively assessed by estimating the percentage of the length that was successfully tracked and segmented for each ureter and by estimating the average distance and the average maximum distance between the computer and the manually tracked centerlines. Results: Of the 124 ureters, 120 (97%) were segmented completely (100%), 121 (98%) were segmented through at least 70%, and 123 (99%) were segmented through at least 50% of its length. In comparison, using our previous method, 85 (69%) ureters were segmented completely (100%), 100 (81%) were segmented through at least 70%, and 107 (86%) were segmented at least 50% of its length. With COMPASS, the average distance between the computer and the manually generated centerlines is 0.54 mm, and the average maximum distance is 2.02 mm. With our previous method, the average distance between the centerlines was 0.80 mm, and the average maximum distance was 3.38 mm. The improvements in the ureteral tracking length and both distance measures were statistically significant (p < 0.0001). Conclusions: COMPASS improved significantly the ureter tracking, including regions across ureter lesions, wall thickening, and the narrowing of the lumen.« less

Activity-based exploitation of Full Motion Video (FMV)

NASA Astrophysics Data System (ADS)

Kant, Shashi

2012-06-01

Video has been a game-changer in how US forces are able to find, track and defeat its adversaries. With millions of minutes of video being generated from an increasing number of sensor platforms, the DOD has stated that the rapid increase in video is overwhelming their analysts. The manpower required to view and garner useable information from the flood of video is unaffordable, especially in light of current fiscal restraints. "Search" within full-motion video has traditionally relied on human tagging of content, and video metadata, to provision filtering and locate segments of interest, in the context of analyst query. Our approach utilizes a novel machine-vision based approach to index FMV, using object recognition & tracking, events and activities detection. This approach enables FMV exploitation in real-time, as well as a forensic look-back within archives. This approach can help get the most information out of video sensor collection, help focus the attention of overburdened analysts form connections in activity over time and conserve national fiscal resources in exploiting FMV.

The software peculiarities of pattern recognition in track detectors

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

Starkov, N.

The different kinds of nuclear track recognition algorithms are represented. Several complicated samples of use them in physical experiments are considered. The some processing methods of complicated images are described.

Multilevel Space-Time Aggregation for Bright Field Cell Microscopy Segmentation and Tracking

PubMed Central

Inglis, Tiffany; De Sterck, Hans; Sanders, Geoffrey; Djambazian, Haig; Sladek, Robert; Sundararajan, Saravanan; Hudson, Thomas J.

2010-01-01

A multilevel aggregation method is applied to the problem of segmenting live cell bright field microscope images. The method employed is a variant of the so-called “Segmentation by Weighted Aggregation” technique, which itself is based on Algebraic Multigrid methods. The variant of the method used is described in detail, and it is explained how it is tailored to the application at hand. In particular, a new scale-invariant “saliency measure” is proposed for deciding when aggregates of pixels constitute salient segments that should not be grouped further. It is shown how segmentation based on multilevel intensity similarity alone does not lead to satisfactory results for bright field cells. However, the addition of multilevel intensity variance (as a measure of texture) to the feature vector of each aggregate leads to correct cell segmentation. Preliminary results are presented for applying the multilevel aggregation algorithm in space time to temporal sequences of microscope images, with the goal of obtaining space-time segments (“object tunnels”) that track individual cells. The advantages and drawbacks of the space-time aggregation approach for segmentation and tracking of live cells in sequences of bright field microscope images are presented, along with a discussion on how this approach may be used in the future work as a building block in a complete and robust segmentation and tracking system. PMID:20467468

Flow detection via sparse frame analysis for suspicious event recognition in infrared imagery

NASA Astrophysics Data System (ADS)

Fernandes, Henrique C.; Batista, Marcos A.; Barcelos, Celia A. Z.; Maldague, Xavier P. V.

2013-05-01

It is becoming increasingly evident that intelligent systems are very bene¯cial for society and that the further development of such systems is necessary to continue to improve society's quality of life. One area that has drawn the attention of recent research is the development of automatic surveillance systems. In our work we outline a system capable of monitoring an uncontrolled area (an outside parking lot) using infrared imagery and recognizing suspicious events in this area. The ¯rst step is to identify moving objects and segment them from the scene's background. Our approach is based on a dynamic background-subtraction technique which robustly adapts detection to illumination changes. It is analyzed only regions where movement is occurring, ignoring in°uence of pixels from regions where there is no movement, to segment moving objects. Regions where movement is occurring are identi¯ed using °ow detection via sparse frame analysis. During the tracking process the objects are classi¯ed into two categories: Persons and Vehicles, based on features such as size and velocity. The last step is to recognize suspicious events that may occur in the scene. Since the objects are correctly segmented and classi¯ed it is possible to identify those events using features such as velocity and time spent motionless in one spot. In this paper we recognize the suspicious event suspicion of object(s) theft from inside a parked vehicle at spot X by a person" and results show that the use of °ow detection increases the recognition of this suspicious event from 78:57% to 92:85%.

Joint Multi-Leaf Segmentation, Alignment, and Tracking for Fluorescence Plant Videos.

PubMed

Yin, Xi; Liu, Xiaoming; Chen, Jin; Kramer, David M

2018-06-01

This paper proposes a novel framework for fluorescence plant video processing. The plant research community is interested in the leaf-level photosynthetic analysis within a plant. A prerequisite for such analysis is to segment all leaves, estimate their structures, and track them over time. We identify this as a joint multi-leaf segmentation, alignment, and tracking problem. First, leaf segmentation and alignment are applied on the last frame of a plant video to find a number of well-aligned leaf candidates. Second, leaf tracking is applied on the remaining frames with leaf candidate transformation from the previous frame. We form two optimization problems with shared terms in their objective functions for leaf alignment and tracking respectively. A quantitative evaluation framework is formulated to evaluate the performance of our algorithm with four metrics. Two models are learned to predict the alignment accuracy and detect tracking failure respectively in order to provide guidance for subsequent plant biology analysis. The limitation of our algorithm is also studied. Experimental results show the effectiveness, efficiency, and robustness of the proposed method.

Integrated segmentation and recognition of connected Ottoman script

NASA Astrophysics Data System (ADS)

Yalniz, Ismet Zeki; Altingovde, Ismail Sengor; Güdükbay, Uğur; Ulusoy, Özgür

2009-11-01

We propose a novel context-sensitive segmentation and recognition method for connected letters in Ottoman script. This method first extracts a set of segments from a connected script and determines the candidate letters to which extracted segments are most similar. Next, a function is defined for scoring each different syntactically correct sequence of these candidate letters. To find the candidate letter sequence that maximizes the score function, a directed acyclic graph is constructed. The letters are finally recognized by computing the longest path in this graph. Experiments using a collection of printed Ottoman documents reveal that the proposed method provides >90% precision and recall figures in terms of character recognition. In a further set of experiments, we also demonstrate that the framework can be used as a building block for an information retrieval system for digital Ottoman archives.

Exploring 3D Human Action Recognition: from Offline to Online.

PubMed

Liu, Zhenyu; Li, Rui; Tan, Jianrong

2018-02-20

With the introduction of cost-effective depth sensors, a tremendous amount of research has been devoted to studying human action recognition using 3D motion data. However, most existing methods work in an offline fashion, i.e., they operate on a segmented sequence. There are a few methods specifically designed for online action recognition, which continually predicts action labels as a stream sequence proceeds. In view of this fact, we propose a question: can we draw inspirations and borrow techniques or descriptors from existing offline methods, and then apply these to online action recognition? Note that extending offline techniques or descriptors to online applications is not straightforward, since at least two problems-including real-time performance and sequence segmentation-are usually not considered in offline action recognition. In this paper, we give a positive answer to the question. To develop applicable online action recognition methods, we carefully explore feature extraction, sequence segmentation, computational costs, and classifier selection. The effectiveness of the developed methods is validated on the MSR 3D Online Action dataset and the MSR Daily Activity 3D dataset.

Word-level recognition of multifont Arabic text using a feature vector matching approach

NASA Astrophysics Data System (ADS)

Erlandson, Erik J.; Trenkle, John M.; Vogt, Robert C., III

1996-03-01

Many text recognition systems recognize text imagery at the character level and assemble words from the recognized characters. An alternative approach is to recognize text imagery at the word level, without analyzing individual characters. This approach avoids the problem of individual character segmentation, and can overcome local errors in character recognition. A word-level recognition system for machine-printed Arabic text has been implemented. Arabic is a script language, and is therefore difficult to segment at the character level. Character segmentation has been avoided by recognizing text imagery of complete words. The Arabic recognition system computes a vector of image-morphological features on a query word image. This vector is matched against a precomputed database of vectors from a lexicon of Arabic words. Vectors from the database with the highest match score are returned as hypotheses for the unknown image. Several feature vectors may be stored for each word in the database. Database feature vectors generated using multiple fonts and noise models allow the system to be tuned to its input stream. Used in conjunction with database pruning techniques, this Arabic recognition system has obtained promising word recognition rates on low-quality multifont text imagery.

Context Effects and Spoken Word Recognition of Chinese: An Eye-Tracking Study

ERIC Educational Resources Information Center

Yip, Michael C. W.; Zhai, Mingjun

2018-01-01

This study examined the time-course of context effects on spoken word recognition during Chinese sentence processing. We recruited 60 native Mandarin listeners to participate in an eye-tracking experiment. In this eye-tracking experiment, listeners were told to listen to a sentence carefully, which ended with a Chinese homophone, and look at…

100. DAM ROLLER GATES GEAR SEGMENTS & TRACK ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

100. DAM - ROLLER GATES - GEAR SEGMENTS & TRACK EXTENSIONS (ML-8-47/16-FS) June 1935 - Upper Mississippi River 9-Foot Channel, Lock & Dam No. 8, On Mississippi River near Houston County, MN, Genoa, Vernon County, WI

Off-lexicon online Arabic handwriting recognition using neural network

NASA Astrophysics Data System (ADS)

Yahia, Hamdi; Chaabouni, Aymen; Boubaker, Houcine; Alimi, Adel M.

2017-03-01

This paper highlights a new method for online Arabic handwriting recognition based on graphemes segmentation. The main contribution of our work is to explore the utility of Beta-elliptic model in segmentation and features extraction for online handwriting recognition. Indeed, our method consists in decomposing the input signal into continuous part called graphemes based on Beta-Elliptical model, and classify them according to their position in the pseudo-word. The segmented graphemes are then described by the combination of geometric features and trajectory shape modeling. The efficiency of the considered features has been evaluated using feed forward neural network classifier. Experimental results using the benchmarking ADAB Database show the performance of the proposed method.

A fast and efficient segmentation scheme for cell microscopic image.

PubMed

Lebrun, G; Charrier, C; Lezoray, O; Meurie, C; Cardot, H

2007-04-27

Microscopic cellular image segmentation schemes must be efficient for reliable analysis and fast to process huge quantity of images. Recent studies have focused on improving segmentation quality. Several segmentation schemes have good quality but processing time is too expensive to deal with a great number of images per day. For segmentation schemes based on pixel classification, the classifier design is crucial since it is the one which requires most of the processing time necessary to segment an image. The main contribution of this work is focused on how to reduce the complexity of decision functions produced by support vector machines (SVM) while preserving recognition rate. Vector quantization is used in order to reduce the inherent redundancy present in huge pixel databases (i.e. images with expert pixel segmentation). Hybrid color space design is also used in order to improve data set size reduction rate and recognition rate. A new decision function quality criterion is defined to select good trade-off between recognition rate and processing time of pixel decision function. The first results of this study show that fast and efficient pixel classification with SVM is possible. Moreover posterior class pixel probability estimation is easy to compute with Platt method. Then a new segmentation scheme using probabilistic pixel classification has been developed. This one has several free parameters and an automatic selection must dealt with, but criteria for evaluate segmentation quality are not well adapted for cell segmentation, especially when comparison with expert pixel segmentation must be achieved. Another important contribution in this paper is the definition of a new quality criterion for evaluation of cell segmentation. The results presented here show that the selection of free parameters of the segmentation scheme by optimisation of the new quality cell segmentation criterion produces efficient cell segmentation.

Iterative cross section sequence graph for handwritten character segmentation.

PubMed

Dawoud, Amer

2007-08-01

The iterative cross section sequence graph (ICSSG) is an algorithm for handwritten character segmentation. It expands the cross section sequence graph concept by applying it iteratively at equally spaced thresholds. The iterative thresholding reduces the effect of information loss associated with image binarization. ICSSG preserves the characters' skeletal structure by preventing the interference of pixels that causes flooding of adjacent characters' segments. Improving the structural quality of the characters' skeleton facilitates better feature extraction and classification, which improves the overall performance of optical character recognition (OCR). Experimental results showed significant improvements in OCR recognition rates compared to other well-established segmentation algorithms.

Grayscale image segmentation for real-time traffic sign recognition: the hardware point of view

NASA Astrophysics Data System (ADS)

Cao, Tam P.; Deng, Guang; Elton, Darrell

2009-02-01

In this paper, we study several grayscale-based image segmentation methods for real-time road sign recognition applications on an FPGA hardware platform. The performance of different image segmentation algorithms in different lighting conditions are initially compared using PC simulation. Based on these results and analysis, suitable algorithms are implemented and tested on a real-time FPGA speed sign detection system. Experimental results show that the system using segmented images uses significantly less hardware resources on an FPGA while maintaining comparable system's performance. The system is capable of processing 60 live video frames per second.

Reading handprinted addresses on IRS tax forms

NASA Astrophysics Data System (ADS)

Ramanaprasad, Vemulapati; Shin, Yong-Chul; Srihari, Sargur N.

1996-03-01

The hand-printed address recognition system described in this paper is a part of the Name and Address Block Reader (NABR) system developed by the Center of Excellence for Document Analysis and Recognition (CEDAR). NABR is currently being used by the IRS to read address blocks (hand-print as well as machine-print) on fifteen different tax forms. Although machine- print address reading was relatively straightforward, hand-print address recognition has posed some special challenges due to demands on processing speed (with an expected throughput of 8450 forms/hour) and recognition accuracy. We discuss various subsystems involved in hand- printed address recognition, including word segmentation, word recognition, digit segmentation, and digit recognition. We also describe control strategies used to make effective use of these subsystems to maximize recognition accuracy. We present system performance on 931 address blocks in recognizing various fields, such as city, state, ZIP Code, street number and name, and personal names.

Voice tracking and spoken word recognition in the presence of other voices

NASA Astrophysics Data System (ADS)

Litong-Palima, Marisciel; Violanda, Renante; Saloma, Caesar

2004-12-01

We study the human hearing process by modeling the hair cell as a thresholded Hopf bifurcator and compare our calculations with experimental results involving human subjects in two different multi-source listening tasks of voice tracking and spoken-word recognition. In the model, we observed noise suppression by destructive interference between noise sources which weakens the effective noise strength acting on the hair cell. Different success rate characteristics were observed for the two tasks. Hair cell performance at low threshold levels agree well with results from voice-tracking experiments while those of word-recognition experiments are consistent with a linear model of the hearing process. The ability of humans to track a target voice is robust against cross-talk interference unlike word-recognition performance which deteriorates quickly with the number of uncorrelated noise sources in the environment which is a response behavior that is associated with linear systems.

Robotics virtual rail system and method

DOEpatents

Bruemmer, David J [Idaho Falls, ID; Few, Douglas A [Idaho Falls, ID; Walton, Miles C [Idaho Falls, ID

2011-07-05

A virtual track or rail system and method is described for execution by a robot. A user, through a user interface, generates a desired path comprised of at least one segment representative of the virtual track for the robot. Start and end points are assigned to the desired path and velocities are also associated with each of the at least one segment of the desired path. A waypoint file is generated including positions along the virtual track representing the desired path with the positions beginning from the start point to the end point including the velocities of each of the at least one segment. The waypoint file is sent to the robot for traversing along the virtual track.

Tracking cells in Life Cell Imaging videos using topological alignments.

PubMed

Mosig, Axel; Jäger, Stefan; Wang, Chaofeng; Nath, Sumit; Ersoy, Ilker; Palaniappan, Kannap-pan; Chen, Su-Shing

2009-07-16

With the increasing availability of live cell imaging technology, tracking cells and other moving objects in live cell videos has become a major challenge for bioimage informatics. An inherent problem for most cell tracking algorithms is over- or under-segmentation of cells - many algorithms tend to recognize one cell as several cells or vice versa. We propose to approach this problem through so-called topological alignments, which we apply to address the problem of linking segmentations of two consecutive frames in the video sequence. Starting from the output of a conventional segmentation procedure, we align pairs of consecutive frames through assigning sets of segments in one frame to sets of segments in the next frame. We achieve this through finding maximum weighted solutions to a generalized "bipartite matching" between two hierarchies of segments, where we derive weights from relative overlap scores of convex hulls of sets of segments. For solving the matching task, we rely on an integer linear program. Practical experiments demonstrate that the matching task can be solved efficiently in practice, and that our method is both effective and useful for tracking cells in data sets derived from a so-called Large Scale Digital Cell Analysis System (LSDCAS). The source code of the implementation is available for download from http://www.picb.ac.cn/patterns/Software/topaln.

Method and apparatus for optimizing a train trip using signal information

DOEpatents

Kumar, Ajith Kuttannair; Daum, Wolfgang; Otsubo, Tom; Hershey, John Erik; Hess, Gerald James

2014-06-10

A system is provided for operating a railway network including a first railway vehicle during a trip along track segments. The system includes a first element for determining travel parameters of the first railway vehicle, a second element for determining travel parameters of a second railway vehicle relative to the track segments to be traversed by the first vehicle during the trip, a processor for receiving information from the first and the second elements and for determining a relationship between occupation of a track segment by the second vehicle and later occupation of the same track segment by the first vehicle and an algorithm embodied within the processor having access to the information to create a trip plan that determines a speed trajectory for the first vehicle. The speed trajectory is responsive to the relationship and further in accordance with one or more operational criteria for the first vehicle.

Interactive segmentation of tongue contours in ultrasound video sequences using quality maps

NASA Astrophysics Data System (ADS)

Ghrenassia, Sarah; Ménard, Lucie; Laporte, Catherine

2014-03-01

Ultrasound (US) imaging is an effective and non invasive way of studying the tongue motions involved in normal and pathological speech, and the results of US studies are of interest for the development of new strategies in speech therapy. State-of-the-art tongue shape analysis techniques based on US images depend on semi-automated tongue segmentation and tracking techniques. Recent work has mostly focused on improving the accuracy of the tracking techniques themselves. However, occasional errors remain inevitable, regardless of the technique used, and the tongue tracking process must thus be supervised by a speech scientist who will correct these errors manually or semi-automatically. This paper proposes an interactive framework to facilitate this process. In this framework, the user is guided towards potentially problematic portions of the US image sequence by a segmentation quality map that is based on the normalized energy of an active contour model and automatically produced during tracking. When a problematic segmentation is identified, corrections to the segmented contour can be made on one image and propagated both forward and backward in the problematic subsequence, thereby improving the user experience. The interactive tools were tested in combination with two different tracking algorithms. Preliminary results illustrate the potential of the proposed framework, suggesting that the proposed framework generally improves user interaction time, with little change in segmentation repeatability.

Structure preserving clustering-object tracking via subgroup motion pattern segmentation

NASA Astrophysics Data System (ADS)

Fan, Zheyi; Zhu, Yixuan; Jiang, Jiao; Weng, Shuqin; Liu, Zhiwen

2018-01-01

Tracking clustering objects with similar appearances simultaneously in collective scenes is a challenging task in the field of collective motion analysis. Recent work on clustering-object tracking often suffers from poor tracking accuracy and terrible real-time performance due to the neglect or the misjudgment of the motion differences among objects. To address this problem, we propose a subgroup motion pattern segmentation framework based on a multilayer clustering structure and establish spatial constraints only among objects in the same subgroup, which entails having consistent motion direction and close spatial position. In addition, the subgroup segmentation results are updated dynamically because crowd motion patterns are changeable and affected by objects' destinations and scene structures. The spatial structure information combined with the appearance similarity information is used in the structure preserving object tracking framework to track objects. Extensive experiments conducted on several datasets containing multiple real-world crowd scenes validate the accuracy and the robustness of the presented algorithm for tracking objects in collective scenes.

Speech-Enabled Interfaces for Travel Information Systems with Large Grammars

NASA Astrophysics Data System (ADS)

Zhao, Baoli; Allen, Tony; Bargiela, Andrzej

This paper introduces three grammar-segmentation methods capable of handling the large grammar issues associated with producing a real-time speech-enabled VXML bus travel application for London. Large grammars tend to produce relatively slow recognition interfaces and this work shows how this limitation can be successfully addressed. Comparative experimental results show that the novel last-word recognition based grammar segmentation method described here achieves an optimal balance between recognition rate, speed of processing and naturalness of interaction.

Rigid shape matching by segmentation averaging.

PubMed

Wang, Hongzhi; Oliensis, John

2010-04-01

We use segmentations to match images by shape. The new matching technique does not require point-to-point edge correspondence and is robust to small shape variations and spatial shifts. To address the unreliability of segmentations computed bottom-up, we give a closed form approximation to an average over all segmentations. Our method has many extensions, yielding new algorithms for tracking, object detection, segmentation, and edge-preserving smoothing. For segmentation, instead of a maximum a posteriori approach, we compute the "central" segmentation minimizing the average distance to all segmentations of an image. For smoothing, instead of smoothing images based on local structures, we smooth based on the global optimal image structures. Our methods for segmentation, smoothing, and object detection perform competitively, and we also show promising results in shape-based tracking.

Unsupervised learning in persistent sensing for target recognition by wireless ad hoc networks of ground-based sensors

NASA Astrophysics Data System (ADS)

Hortos, William S.

2008-04-01

In previous work by the author, effective persistent and pervasive sensing for recognition and tracking of battlefield targets were seen to be achieved, using intelligent algorithms implemented by distributed mobile agents over a composite system of unmanned aerial vehicles (UAVs) for persistence and a wireless network of unattended ground sensors for pervasive coverage of the mission environment. While simulated performance results for the supervised algorithms of the composite system are shown to provide satisfactory target recognition over relatively brief periods of system operation, this performance can degrade by as much as 50% as target dynamics in the environment evolve beyond the period of system operation in which the training data are representative. To overcome this limitation, this paper applies the distributed approach using mobile agents to the network of ground-based wireless sensors alone, without the UAV subsystem, to provide persistent as well as pervasive sensing for target recognition and tracking. The supervised algorithms used in the earlier work are supplanted by unsupervised routines, including competitive-learning neural networks (CLNNs) and new versions of support vector machines (SVMs) for characterization of an unknown target environment. To capture the same physical phenomena from battlefield targets as the composite system, the suite of ground-based sensors can be expanded to include imaging and video capabilities. The spatial density of deployed sensor nodes is increased to allow more precise ground-based location and tracking of detected targets by active nodes. The "swarm" mobile agents enabling WSN intelligence are organized in a three processing stages: detection, recognition and sustained tracking of ground targets. Features formed from the compressed sensor data are down-selected according to an information-theoretic algorithm that reduces redundancy within the feature set, reducing the dimension of samples used in the target recognition and tracking routines. Target tracking is based on simplified versions of Kalman filtration. Accuracy of recognition and tracking of implemented versions of the proposed suite of unsupervised algorithms is somewhat degraded from the ideal. Target recognition and tracking by supervised routines and by unsupervised SVM and CLNN routines in the ground-based WSN is evaluated in simulations using published system values and sensor data from vehicular targets in ground-surveillance scenarios. Results are compared with previously published performance for the system of the ground-based sensor network (GSN) and UAV swarm.

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.

Global Linking of Cell Tracks Using the Viterbi Algorithm

PubMed Central

Jaldén, Joakim; Gilbert, Penney M.; Blau, Helen M.

2016-01-01

Automated tracking of living cells in microscopy image sequences is an important and challenging problem. With this application in mind, we propose a global track linking algorithm, which links cell outlines generated by a segmentation algorithm into tracks. The algorithm adds tracks to the image sequence one at a time, in a way which uses information from the complete image sequence in every linking decision. This is achieved by finding the tracks which give the largest possible increases to a probabilistically motivated scoring function, using the Viterbi algorithm. We also present a novel way to alter previously created tracks when new tracks are created, thus mitigating the effects of error propagation. The algorithm can handle mitosis, apoptosis, and migration in and out of the imaged area, and can also deal with false positives, missed detections, and clusters of jointly segmented cells. The algorithm performance is demonstrated on two challenging datasets acquired using bright-field microscopy, but in principle, the algorithm can be used with any cell type and any imaging technique, presuming there is a suitable segmentation algorithm. PMID:25415983

Summary of tracking and identification methods

NASA Astrophysics Data System (ADS)

Blasch, Erik; Yang, Chun; Kadar, Ivan

2014-06-01

Over the last two decades, many solutions have arisen to combine target tracking estimation with classification methods. Target tracking includes developments from linear to non-linear and Gaussian to non-Gaussian processing. Pattern recognition includes detection, classification, recognition, and identification methods. Integrating tracking and pattern recognition has resulted in numerous approaches and this paper seeks to organize the various approaches. We discuss the terminology so as to have a common framework for various standards such as the NATO STANAG 4162 - Identification Data Combining Process. In a use case, we provide a comparative example highlighting that location information (as an example) with additional mission objectives from geographical, human, social, cultural, and behavioral modeling is needed to determine identification as classification alone does not allow determining identification or intent.

TRACX: A Recognition-Based Connectionist Framework for Sequence Segmentation and Chunk Extraction

ERIC Educational Resources Information Center

French, Robert M.; Addyman, Caspar; Mareschal, Denis

2011-01-01

Individuals of all ages extract structure from the sequences of patterns they encounter in their environment, an ability that is at the very heart of cognition. Exactly what underlies this ability has been the subject of much debate over the years. A novel mechanism, implicit chunk recognition (ICR), is proposed for sequence segmentation and chunk…

New robust algorithm for tracking cells in videos of Drosophila morphogenesis based on finding an ideal path in segmented spatio-temporal cellular structures.

PubMed

Bellaïche, Yohanns; Bosveld, Floris; Graner, François; Mikula, Karol; Remesíková, Mariana; Smísek, Michal

2011-01-01

In this paper, we present a novel algorithm for tracking cells in time lapse confocal microscopy movie of a Drosophila epithelial tissue during pupal morphogenesis. We consider a 2D + time video as a 3D static image, where frames are stacked atop each other, and using a spatio-temporal segmentation algorithm we obtain information about spatio-temporal 3D tubes representing evolutions of cells. The main idea for tracking is the usage of two distance functions--first one from the cells in the initial frame and second one from segmented boundaries. We track the cells backwards in time. The first distance function attracts the subsequently constructed cell trajectories to the cells in the initial frame and the second one forces them to be close to centerlines of the segmented tubular structures. This makes our tracking algorithm robust against noise and missing spatio-temporal boundaries. This approach can be generalized to a 3D + time video analysis, where spatio-temporal tubes are 4D objects.

Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

NASA Astrophysics Data System (ADS)

Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

2014-03-01

This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

Model-based vision system for automatic recognition of structures in dental radiographs

NASA Astrophysics Data System (ADS)

Acharya, Raj S.; Samarabandu, Jagath K.; Hausmann, E.; Allen, K. A.

1991-07-01

X-ray diagnosis of destructive periodontal disease requires assessing serial radiographs by an expert to determine the change in the distance between cemento-enamel junction (CEJ) and the bone crest. To achieve this without the subjectivity of a human expert, a knowledge based system is proposed to automatically locate the two landmarks which are the CEJ and the level of alveolar crest at its junction with the periodontal ligament space. This work is a part of an ongoing project to automatically measure the distance between CEJ and the bone crest along a line parallel to the axis of the tooth. The approach presented in this paper is based on identifying a prominent feature such as the tooth boundary using local edge detection and edge thresholding to establish a reference and then using model knowledge to process sub-regions in locating the landmarks. Segmentation techniques invoked around these regions consists of a neural-network like hierarchical refinement scheme together with local gradient extraction, multilevel thresholding and ridge tracking. Recognition accuracy is further improved by first locating the easily identifiable parts of the bone surface and the interface between the enamel and the dentine and then extending these boundaries towards the periodontal ligament space and the tooth boundary respectively. The system is realized as a collection of tools (or knowledge sources) for pre-processing, segmentation, primary and secondary feature detection and a control structure based on the blackboard model to coordinate the activities of these tools.

A general framework for multicharacter segmentation and its application in recognizing multilingual Asian documents

NASA Astrophysics Data System (ADS)

Wen, Di; Ding, Xiaoqing

2003-12-01

In this paper we propose a general framework for character segmentation in complex multilingual documents, which is an endeavor to combine the traditionally separated segmentation and recognition processes into a cooperative system. The framework contains three basic steps: Dissection, Local Optimization and Global Optimization, which are designed to fuse various properties of the segmentation hypotheses hierarchically into a composite evaluation to decide the final recognition results. Experimental results show that this framework is general enough to be applied in variety of documents. A sample system based on this framework to recognize Chinese, Japanese and Korean documents and experimental performance is reported finally.

Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision.

PubMed

Li, Heng; Su, Xiaofan; Wang, Jing; Kan, Han; Han, Tingting; Zeng, Yajie; Chai, Xinyu

2018-01-01

Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual perception, prosthetic recipients can just complete some simple visual tasks, but more complex tasks like face identification/object recognition are extremely difficult. Therefore, it is necessary to investigate and apply image processing strategies for optimizing the visual perception of the recipients. This study focuses on recognition of the object of interest employing simulated prosthetic vision. We used a saliency segmentation method based on a biologically plausible graph-based visual saliency model and a grabCut-based self-adaptive-iterative optimization framework to automatically extract foreground objects. Based on this, two image processing strategies, Addition of Separate Pixelization and Background Pixel Shrink, were further utilized to enhance the extracted foreground objects. i) The results showed by verification of psychophysical experiments that under simulated prosthetic vision, both strategies had marked advantages over Direct Pixelization in terms of recognition accuracy and efficiency. ii) We also found that recognition performance under two strategies was tied to the segmentation results and was affected positively by the paired-interrelated objects in the scene. The use of the saliency segmentation method and image processing strategies can automatically extract and enhance foreground objects, and significantly improve object recognition performance towards recipients implanted a high-density implant. Copyright © 2017 Elsevier B.V. All rights reserved.

Molecular recognition in poly(epsilon-caprolactone)-based thermoplastic elastomers.

PubMed

Wisse, Eva; Spiering, A J H; van Leeuwen, Ellen N M; Renken, Raymond A E; Dankers, Patricia Y W; Brouwer, Linda A; van Luyn, Marja J A; Harmsen, Martin C; Sommerdijk, Nico A J M; Meijer, E W

2006-12-01

The molecular recognition properties of the hydrogen bonding segments in biodegradable thermoplastic elastomers were explored, aiming at the further functionalization of these potentially interesting biomaterials. A poly(epsilon-caprolactone)-based poly(urea) 2 was synthesized and characterized in terms of mechanical properties, processibility and histocompatibility. Comparison of the data with those obtained from the structurally related poly(urethane urea) 1 revealed that the difference in hard segment structure does not significantly affect the potency for application as a biomaterial. Nevertheless, the small differences in hard block composition had a strong effect on the molecular recognition properties of the hydrogen bonding segments. High selectivity was found for poly(urea) 2 in which bisureidobutylene-functionalized azobenzene dye 3 was selectively incorporated while bisureidopentylene-functionalized azobenzene dye 4 was completely released. In contrast, the incorporation of both dyes in poly(urethane urea) 1 led in both cases to their gradual release in time. Thermal analysis of the polymers in combination with variable temperature infrared experiments indicated that the hard blocks in 1 showed a sharp melting point, whereas those in 2 showed a very broad melting trajectory. This suggests a more precise organization of the hydrogen bonding segments in the hard blocks of poly(urea) 2 compared to poly(urethane urea) 1 and explains the results from the molecular recognition experiments. Preliminary results revealed that a bisureidobutylene-functionalized GRGDS peptide showed more supramolecular interaction with the PCL-based poly(urea), containing the bisureidobutylene recognition unit, as compared to HMW PCL, lacking this recognition unit.

User-assisted video segmentation system for visual communication

NASA Astrophysics Data System (ADS)

Wu, Zhengping; Chen, Chun

2002-01-01

Video segmentation plays an important role for efficient storage and transmission in visual communication. In this paper, we introduce a novel video segmentation system using point tracking and contour formation techniques. Inspired by the results from the study of the human visual system, we intend to solve the video segmentation problem into three separate phases: user-assisted feature points selection, feature points' automatic tracking, and contour formation. This splitting relieves the computer of ill-posed automatic segmentation problems, and allows a higher level of flexibility of the method. First, the precise feature points can be found using a combination of user assistance and an eigenvalue-based adjustment. Second, the feature points in the remaining frames are obtained using motion estimation and point refinement. At last, contour formation is used to extract the object, and plus a point insertion process to provide the feature points for next frame's tracking.

Automated target recognition and tracking using an optical pattern recognition neural network

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin

1991-01-01

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

Assessment of Left Ventricular Myocardial Viability by 3-Dimensional Speckle-Tracking Echocardiography in Patients With Myocardial Infarction.

PubMed

Ran, Hong; Zhang, Ping-Yang; Zhang, You-Xiang; Zhang, Jian-Xin; Wu, Wen-Fang; Dong, Jing; Ma, Xiao-Wu

2016-08-01

To determine whether 3-dimensional (3D) speckle-tracking echocardiography could provide a new way to assess myocardial viability in patients with myocardial infarction (MI). Forty-five patients with MI underwent routine echocardiography, 2-dimensional (2D) speckle-tracking echocardiography, and 3D speckle-tracking echocardiography. Radionuclide myocardial perfusion/metabolic imaging was used as a reference standard to define viable and nonviable myocardia. Among 720 myocardial segments in 45 patients, 368 showed abnormal motion on routine echocardiography; 204 of 368 were categorized as viable on single-photon emission computed tomography/positron emission tomography (SPECT/PET), whereas 164 were defined as nonviable; 300 normal segments on SPECT/PET among 352 segments without abnormal motion on routine echocardiography were categorized as a control group. The radial, longitudinal, 3D, and area strain on 3D speckle-tracking echocardiography had significant differences between control and nonviable groups (P < .001), whereas none of the parameters had significant differences between control and viable groups. There were no significant differences in circumferential, radial, and longitudinal peak systolic strain from 2D speckle-tracking echocardiography between viable and nonviable groups. Although there was no significant difference in circumferential strain between the groups, radial and longitudinal strain from 3D speckle-tracking echocardiography decreased significantly in the nonviable group. Moreover, 3D and area strain values were lower in the nonviable segments than the viable segments. By receiver operating characteristic analysis, radial strain from 3D speckle-tracking echocardiography with a cutoff of 11.1% had sensitivity of 95.1% and specificity of 53.4% for viable segments; longitudinal strain with a cutoff of 14.3% had sensitivity of 65.2% and specificity of 65.7%; 3D strain with a cutoff of 17.4% had sensitivity of 70.6% and specificity of 77.2%; and area strain with a cutoff of 23.2% had sensitivity of 91.5% and specificity of 82.8%. Three-dimensional speckle-tracking echocardiography might have potential for detection of myocardial viability in patients with cardiac dysfunction due to MI.

Textual and shape-based feature extraction and neuro-fuzzy classifier for nuclear track recognition

NASA Astrophysics Data System (ADS)

Khayat, Omid; Afarideh, Hossein

2013-04-01

Track counting algorithms as one of the fundamental principles of nuclear science have been emphasized in the recent years. Accurate measurement of nuclear tracks on solid-state nuclear track detectors is the aim of track counting systems. Commonly track counting systems comprise a hardware system for the task of imaging and software for analysing the track images. In this paper, a track recognition algorithm based on 12 defined textual and shape-based features and a neuro-fuzzy classifier is proposed. Features are defined so as to discern the tracks from the background and small objects. Then, according to the defined features, tracks are detected using a trained neuro-fuzzy system. Features and the classifier are finally validated via 100 Alpha track images and 40 training samples. It is shown that principle textual and shape-based features concomitantly yield a high rate of track detection compared with the single-feature based methods.

Tracking Small Artists

NASA Astrophysics Data System (ADS)

Russell, James C.; Klette, Reinhard; Chen, Chia-Yen

Tracks of small animals are important in environmental surveillance, where pattern recognition algorithms allow species identification of the individuals creating tracks. These individuals can also be seen as artists, presented in their natural environments with a canvas upon which they can make prints. We present tracks of small mammals and reptiles which have been collected for identification purposes, and re-interpret them from an esthetic point of view. We re-classify these tracks not by their geometric qualities as pattern recognition algorithms would, but through interpreting the 'artist', their brush strokes and intensity. We describe the algorithms used to enhance and present the work of the 'artists'.

An objective comparison of cell-tracking algorithms.

PubMed

Ulman, Vladimír; Maška, Martin; Magnusson, Klas E G; Ronneberger, Olaf; Haubold, Carsten; Harder, Nathalie; Matula, Pavel; Matula, Petr; Svoboda, David; Radojevic, Miroslav; Smal, Ihor; Rohr, Karl; Jaldén, Joakim; Blau, Helen M; Dzyubachyk, Oleh; Lelieveldt, Boudewijn; Xiao, Pengdong; Li, Yuexiang; Cho, Siu-Yeung; Dufour, Alexandre C; Olivo-Marin, Jean-Christophe; Reyes-Aldasoro, Constantino C; Solis-Lemus, Jose A; Bensch, Robert; Brox, Thomas; Stegmaier, Johannes; Mikut, Ralf; Wolf, Steffen; Hamprecht, Fred A; Esteves, Tiago; Quelhas, Pedro; Demirel, Ömer; Malmström, Lars; Jug, Florian; Tomancak, Pavel; Meijering, Erik; Muñoz-Barrutia, Arrate; Kozubek, Michal; Ortiz-de-Solorzano, Carlos

2017-12-01

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

Shape and texture fused recognition of flying targets

NASA Astrophysics Data System (ADS)

Kovács, Levente; Utasi, Ákos; Kovács, Andrea; Szirányi, Tamás

2011-06-01

This paper presents visual detection and recognition of flying targets (e.g. planes, missiles) based on automatically extracted shape and object texture information, for application areas like alerting, recognition and tracking. Targets are extracted based on robust background modeling and a novel contour extraction approach, and object recognition is done by comparisons to shape and texture based query results on a previously gathered real life object dataset. Application areas involve passive defense scenarios, including automatic object detection and tracking with cheap commodity hardware components (CPU, camera and GPS).

Measurement of pattern recognition efficiency of tracks generated by ionizing radiation in a Medipix2 device

NASA Astrophysics Data System (ADS)

Bouchami, J.; Gutiérrez, A.; Holy, T.; Houdayer, A.; Jakůbek, J.; Lebel, C.; Leroy, C.; Macana, J.; Martin, J.-P.; Pospíšil, S.; Prak, S.; Sabella, P.; Teyssier, C.; CERN Medipix Collaboration

2011-05-01

Several experiments were performed to establish the Medipix2 device capabilities for track recognition and its efficiency at measuring fluxes. A Medipix2 device was exposed to 241Am, 106Ru and 137Cs radioactive sources, separately and simultaneously. It was also exposed to heavy particle beams (protons and alpha-particles), recoiled on a gold foil to reduce the incoming flux and allow the study of the detector response struck by incoming particles at different incidence angles. For three proton beams (400 keV, 4 and 10 MeV), the device was exposed to the radioactive sources on top of beam, giving a mixed radiation field. To test the reliability of track recognition with this device, the activities of the radioactive sources were extracted from the experimental data and compared to the expected activities. Rotation of the Medipix2 device allowed the test of the heavy tracks recognition at different incidence angles.

48 CFR 9904.412-60.1 - Illustrations-CAS Pension Harmonization Rule.

Code of Federal Regulations, 2013 CFR

2013-10-01

... by segment or aggregation of segments. The actuarial gain and loss recognition of changes between... recognition of the asset gain or loss, which is the difference between the expected income, based on the...,100,000 $14,225,000 1 Normal Cost 910,700 89,100 821,600 1 Expense Load on Normal Cost 1, 2 Note 1...

48 CFR 9904.412-60.1 - Illustrations-CAS Pension Harmonization Rule.

Code of Federal Regulations, 2012 CFR

2012-10-01

... by segment or aggregation of segments. The actuarial gain and loss recognition of changes between... recognition of the asset gain or loss, which is the difference between the expected income, based on the...,100,000 $14,225,000 1 Normal Cost 910,700 89,100 821,600 1 Expense Load on Normal Cost 1, 2 Note 1...

48 CFR 9904.412-60.1 - Illustrations-CAS Pension Harmonization Rule.

Code of Federal Regulations, 2014 CFR

2014-10-01

... by segment or aggregation of segments. The actuarial gain and loss recognition of changes between... recognition of the asset gain or loss, which is the difference between the expected income, based on the...,100,000 $14,225,000 1 Normal Cost 910,700 89,100 821,600 1 Expense Load on Normal Cost 1, 2 Note 1...

An automatic method for segmentation of fission tracks in epidote crystal photomicrographs

NASA Astrophysics Data System (ADS)

de Siqueira, Alexandre Fioravante; Nakasuga, Wagner Massayuki; Pagamisse, Aylton; Tello Saenz, Carlos Alberto; Job, Aldo Eloizo

2014-08-01

Manual identification of fission tracks has practical problems, such as variation due to observe-observation efficiency. An automatic processing method that could identify fission tracks in a photomicrograph could solve this problem and improve the speed of track counting. However, separation of nontrivial images is one of the most difficult tasks in image processing. Several commercial and free softwares are available, but these softwares are meant to be used in specific images. In this paper, an automatic method based on starlet wavelets is presented in order to separate fission tracks in mineral photomicrographs. Automatization is obtained by the Matthews correlation coefficient, and results are evaluated by precision, recall and accuracy. This technique is an improvement of a method aimed at segmentation of scanning electron microscopy images. This method is applied in photomicrographs of epidote phenocrystals, in which accuracy higher than 89% was obtained in fission track segmentation, even for difficult images. Algorithms corresponding to the proposed method are available for download. Using the method presented here, a user could easily determine fission tracks in photomicrographs of mineral samples.

No damage to rail cars or SRB segments in derailment

NASA Technical Reports Server (NTRS)

2000-01-01

One of two solid rocket booster rail cars is off the track after being involved in a minor derailment incident during a routine movement on the tracks. The rail cars were being moved as part of a standard operation to '''order''' the cars, placing them into a proper sequence for upcoming segment processing activities. The rear wheels of one car and the front wheels of the car behind it slid off the tracks while passing through a railway switch onto a siding. They were traveling approximately 3 miles per hour at the time, about normal walking speed. No damage occurred to the SRB segments, or to the devices that secure the segments to the rail cars. The incident occurred on KSC property, just north of the NASA Causeway in the KSC Industrial Area.

Study of tracking and data acquisition system for the 1990's. Volume 4: TDAS space segment architecture

NASA Technical Reports Server (NTRS)

Orr, R. S.

1984-01-01

Tracking and data acquisition system (TDAS) requirements, TDAS architectural goals, enhanced TDAS subsystems, constellation and networking options, TDAS spacecraft options, crosslink implementation, baseline TDAS space segment architecture, and treat model development/security analysis are addressed.

Tracking fuzzy borders using geodesic curves with application to liver segmentation on planning CT

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

Yuan, Yading, E-mail: yading.yuan@mssm.edu; Chao, Ming; Sheu, Ren-Dih

Purpose: This work aims to develop a robust and efficient method to track the fuzzy borders between liver and the abutted organs where automatic liver segmentation usually suffers, and to investigate its applications in automatic liver segmentation on noncontrast-enhanced planning computed tomography (CT) images. Methods: In order to track the fuzzy liver–chestwall and liver–heart borders where oversegmentation is often found, a starting point and an ending point were first identified on the coronal view images; the fuzzy border was then determined as a geodesic curve constructed by minimizing the gradient-weighted path length between these two points near the fuzzy border.more » The minimization of path length was numerically solved by fast-marching method. The resultant fuzzy borders were incorporated into the authors’ automatic segmentation scheme, in which the liver was initially estimated by a patient-specific adaptive thresholding and then refined by a geodesic active contour model. By using planning CT images of 15 liver patients treated with stereotactic body radiation therapy, the liver contours extracted by the proposed computerized scheme were compared with those manually delineated by a radiation oncologist. Results: The proposed automatic liver segmentation method yielded an average Dice similarity coefficient of 0.930 ± 0.015, whereas it was 0.912 ± 0.020 if the fuzzy border tracking was not used. The application of fuzzy border tracking was found to significantly improve the segmentation performance. The mean liver volume obtained by the proposed method was 1727 cm{sup 3}, whereas it was 1719 cm{sup 3} for manual-outlined volumes. The computer-generated liver volumes achieved excellent agreement with manual-outlined volumes with correlation coefficient of 0.98. Conclusions: The proposed method was shown to provide accurate segmentation for liver in the planning CT images where contrast agent is not applied. The authors’ results also clearly demonstrated that the application of tracking the fuzzy borders could significantly reduce contour leakage during active contour evolution.« less

Detection limit of a VCO based detection chain dedicated to particles recognition and tracking

NASA Astrophysics Data System (ADS)

Coulié, K.; Rahajandraibe, W.; Aziza, H.; Micolau, G.; Vauché, R.

2018-01-01

A particle detection chain based on CMOS-SOI VCO circuit is presented. The solution is used for the recognition and the tracking of a given particle at circuit level. TCAD simulation of the detector has been performed on a 3×3 matrix of diodes based detector for particles recognition and tracking. The current response of the detector has been used for a case study in order to determine the ability of the chain to recognize an alpha particle crossing a 3×3 detection cell. The detection limit of the proposed solution is investigated and discussed in this paper.

Printed Arabic optical character segmentation

NASA Astrophysics Data System (ADS)

Mohammad, Khader; Ayyesh, Muna; Qaroush, Aziz; Tumar, Iyad

2015-03-01

A considerable progress in recognition techniques for many non-Arabic characters has been achieved. In contrary, few efforts have been put on the research of Arabic characters. In any Optical Character Recognition (OCR) system the segmentation step is usually the essential stage in which an extensive portion of processing is devoted and a considerable share of recognition errors is attributed. In this research, a novel segmentation approach for machine Arabic printed text with diacritics is proposed. The proposed method reduces computation, errors, gives a clear description for the sub-word and has advantages over using the skeleton approach in which the data and information of the character can be lost. Both of initial evaluation and testing of the proposed method have been developed using MATLAB and shows 98.7% promising results.

Predictive factor analysis for successful performance of iris recognition-assisted dynamic rotational eye tracking during laser in situ keratomileusis.

PubMed

Prakash, Gaurav; Ashok Kumar, Dhivya; Agarwal, Amar; Jacob, Soosan; Sarvanan, Yoga; Agarwal, Athiya

2010-02-01

To analyze the predictive factors associated with success of iris recognition and dynamic rotational eye tracking on a laser in situ keratomileusis (LASIK) platform with active assessment and correction of intraoperative cyclotorsion. Interventional case series. Two hundred seventy-five eyes of 142 consecutive candidates underwent LASIK with attempted iris recognition and dynamic rotational tracking on the Technolas 217z100 platform (Techolas Perfect Vision, St Louis, Missouri, USA) at a tertiary care ophthalmic hospital. The main outcome measures were age, gender, flap creation method (femtosecond, microkeratome, epi-LASIK), success of static rotational tracking, ablation algorithm, pulses, and depth; preablation and intraablation rotational activity were analyzed and evaluated using regression models. Preablation static iris recognition was successful in 247 eyes, without difference in flap creation methods (P = .6). Age (partial correlation, -0.16; P = .014), amount of pulses (partial correlation, 0.39; P = 1.6 x 10(-8)), and gender (P = .02) were significant predictive factors for the amount of intraoperative cyclodeviation. Tracking difficulties leading to linking the ablation with a new intraoperatively acquired iris image were more with femtosecond-assisted flaps (P = 2.8 x 10(-7)) and the amount of intraoperative cyclotorsion (P = .02). However, the number of cases having nonresolvable failure of intraoperative rotational tracking was similar in the 3 flap creation methods (P = .22). Intraoperative cyclotorsional activity depends on the age, gender, and duration of ablation (pulses delivered). Femtosecond flaps do not seem to have a disadvantage over microkeratome flaps as far as iris recognition and success of intraoperative dynamic rotational tracking is concerned. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

PubMed Central

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease. PMID:27977767

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

PubMed

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

Machine-printed Arabic OCR

NASA Astrophysics Data System (ADS)

Hassibi, Khosrow M.

1994-02-01

This paper presents a brief overview of our research in the development of an OCR system for recognition of machine-printed texts in languages that use the Arabic alphabet. The cursive nature of machine-printed Arabic makes the segmentation of words into letters a challenging problem. In our approach, through a novel preliminary segmentation technique, a word is broken into pieces where each piece may not represent a valid letter in general. Neural networks trained on a training sample set of about 500 Arabic text images are used for recognition of these pieces. The rules governing the alphabet and character-level contextual information are used for recombining these pieces into valid letters. Higher-level contextual analysis schemes including the use of an Arabic lexicon and n-grams is also under development and are expected to improve the word recognition accuracy. The segmentation, recognition, and contextual analysis processes are closely integrated using a feedback scheme. The details of preparation of the training set and some recent results on training of the networks will be presented.

A robust pointer segmentation in biomedical images toward building a visual ontology for biomedical article retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Pointers (arrows and symbols) are frequently used in biomedical images to highlight specific image regions of interest (ROIs) that are mentioned in figure captions and/or text discussion. Detection of pointers is the first step toward extracting relevant visual features from ROIs and combining them with textual descriptions for a multimodal (text and image) biomedical article retrieval system. Recently we developed a pointer recognition algorithm based on an edge-based pointer segmentation method, and subsequently reported improvements made on our initial approach involving the use of Active Shape Models (ASM) for pointer recognition and region growing-based method for pointer segmentation. These methods contributed to improving the recall of pointer recognition but not much to the precision. The method discussed in this article is our recent effort to improve the precision rate. Evaluation performed on two datasets and compared with other pointer segmentation methods show significantly improved precision and the highest F1 score.

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Ming; Xie, Fei; Zhao, Jing; Sun, Rui; Zhang, Lei; Zhang, Yue

2018-04-01

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

Research on gesture recognition of augmented reality maintenance guiding system based on improved SVM

NASA Astrophysics Data System (ADS)

Zhao, Shouwei; Zhang, Yong; Zhou, Bin; Ma, Dongxi

2014-09-01

Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system's image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.

Automatic detection and recognition of traffic signs in stereo images based on features and probabilistic neural networks

NASA Astrophysics Data System (ADS)

Sheng, Yehua; Zhang, Ka; Ye, Chun; Liang, Cheng; Li, Jian

2008-04-01

Considering the problem of automatic traffic sign detection and recognition in stereo images captured under motion conditions, a new algorithm for traffic sign detection and recognition based on features and probabilistic neural networks (PNN) is proposed in this paper. Firstly, global statistical color features of left image are computed based on statistics theory. Then for red, yellow and blue traffic signs, left image is segmented to three binary images by self-adaptive color segmentation method. Secondly, gray-value projection and shape analysis are used to confirm traffic sign regions in left image. Then stereo image matching is used to locate the homonymy traffic signs in right image. Thirdly, self-adaptive image segmentation is used to extract binary inner core shapes of detected traffic signs. One-dimensional feature vectors of inner core shapes are computed by central projection transformation. Fourthly, these vectors are input to the trained probabilistic neural networks for traffic sign recognition. Lastly, recognition results in left image are compared with recognition results in right image. If results in stereo images are identical, these results are confirmed as final recognition results. The new algorithm is applied to 220 real images of natural scenes taken by the vehicle-borne mobile photogrammetry system in Nanjing at different time. Experimental results show a detection and recognition rate of over 92%. So the algorithm is not only simple, but also reliable and high-speed on real traffic sign detection and recognition. Furthermore, it can obtain geometrical information of traffic signs at the same time of recognizing their types.

Adaptive block online learning target tracking based on super pixel segmentation

NASA Astrophysics Data System (ADS)

Cheng, Yue; Li, Jianzeng

2018-04-01

Video target tracking technology under the unremitting exploration of predecessors has made big progress, but there are still lots of problems not solved. This paper proposed a new algorithm of target tracking based on image segmentation technology. Firstly we divide the selected region using simple linear iterative clustering (SLIC) algorithm, after that, we block the area with the improved density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm. Each sub-block independently trained classifier and tracked, then the algorithm ignore the failed tracking sub-block while reintegrate the rest of the sub-blocks into tracking box to complete the target tracking. The experimental results show that our algorithm can work effectively under occlusion interference, rotation change, scale change and many other problems in target tracking compared with the current mainstream algorithms.

KENNEDY SPACE CENTER, FLA. - Workers ride the rails along with a container enclosing a segment of a solid rocket booster being moved to the main track. Several segments are being returned to Utah for testing. The segments were part of the STS-114 stack. It is the first time actual flight segments that had been stacked for flight in the VAB are being returned for testing. They will undergo firing, which will enable inspectors to check the viability of the solid and verify the life expectancy for stacked segments.

NASA Image and Video Library

2004-01-30

KENNEDY SPACE CENTER, FLA. - Workers ride the rails along with a container enclosing a segment of a solid rocket booster being moved to the main track. Several segments are being returned to Utah for testing. The segments were part of the STS-114 stack. It is the first time actual flight segments that had been stacked for flight in the VAB are being returned for testing. They will undergo firing, which will enable inspectors to check the viability of the solid and verify the life expectancy for stacked segments.

Heuristic algorithm for optical character recognition of Arabic script

NASA Astrophysics Data System (ADS)

Yarman-Vural, Fatos T.; Atici, A.

1996-02-01

In this paper, a heuristic method is developed for segmentation, feature extraction and recognition of the Arabic script. The study is part of a large project for the transcription of the documents in Ottoman Archives. A geometrical and topological feature analysis method is developed for segmentation and feature extraction stages. Chain code transformation is applied to main strokes of the characters which are then classified by the hidden Markov model (HMM) in the recognition stage. Experimental results indicate that the performance of the proposed method is impressive, provided that the thinning process does not yield spurious branches.

Autonomous space target recognition and tracking approach using star sensors based on a Kalman filter.

PubMed

Ye, Tao; Zhou, Fuqiang

2015-04-10

When imaged by detectors, space targets (including satellites and debris) and background stars have similar point-spread functions, and both objects appear to change as detectors track targets. Therefore, traditional tracking methods cannot separate targets from stars and cannot directly recognize targets in 2D images. Consequently, we propose an autonomous space target recognition and tracking approach using a star sensor technique and a Kalman filter (KF). A two-step method for subpixel-scale detection of star objects (including stars and targets) is developed, and the combination of the star sensor technique and a KF is used to track targets. The experimental results show that the proposed method is adequate for autonomously recognizing and tracking space targets.

The Maximum Likelihood Estimation of Signature Transformation /MLEST/ algorithm. [for affine transformation of crop inventory data

NASA Technical Reports Server (NTRS)

Thadani, S. G.

1977-01-01

The Maximum Likelihood Estimation of Signature Transformation (MLEST) algorithm is used to obtain maximum likelihood estimates (MLE) of affine transformation. The algorithm has been evaluated for three sets of data: simulated (training and recognition segment pairs), consecutive-day (data gathered from Landsat images), and geographical-extension (large-area crop inventory experiment) data sets. For each set, MLEST signature extension runs were made to determine MLE values and the affine-transformed training segment signatures were used to classify the recognition segments. The classification results were used to estimate wheat proportions at 0 and 1% threshold values.

Segmentation of Nerve Bundles and Ganglia in Spine MRI Using Particle Filters

PubMed Central

Dalca, Adrian; Danagoulian, Giovanna; Kikinis, Ron; Schmidt, Ehud; Golland, Polina

2011-01-01

Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation. PMID:22003741

Segmentation of nerve bundles and ganglia in spine MRI using particle filters.

PubMed

Dalca, Adrian; Danagoulian, Giovanna; Kikinis, Ron; Schmidt, Ehud; Golland, Polina

2011-01-01

Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.

New track shift safety limits for high-speed rail applications

DOT National Transportation Integrated Search

2001-11-01

Track shift is defined as the permanent lateral distortion of a track segment due to high vehicle induced lateral loads. Excessive track shift can lead to unsafe conditions leading to ride quality deterioration or vehicle derailment. Current track sh...

Three-dimensional model-based object recognition and segmentation in cluttered scenes.

PubMed

Mian, Ajmal S; Bennamoun, Mohammed; Owens, Robyn

2006-10-01

Viewpoint independent recognition of free-form objects and their segmentation in the presence of clutter and occlusions is a challenging task. We present a novel 3D model-based algorithm which performs this task automatically and efficiently. A 3D model of an object is automatically constructed offline from its multiple unordered range images (views). These views are converted into multidimensional table representations (which we refer to as tensors). Correspondences are automatically established between these views by simultaneously matching the tensors of a view with those of the remaining views using a hash table-based voting scheme. This results in a graph of relative transformations used to register the views before they are integrated into a seamless 3D model. These models and their tensor representations constitute the model library. During online recognition, a tensor from the scene is simultaneously matched with those in the library by casting votes. Similarity measures are calculated for the model tensors which receive the most votes. The model with the highest similarity is transformed to the scene and, if it aligns accurately with an object in the scene, that object is declared as recognized and is segmented. This process is repeated until the scene is completely segmented. Experiments were performed on real and synthetic data comprised of 55 models and 610 scenes and an overall recognition rate of 95 percent was achieved. Comparison with the spin images revealed that our algorithm is superior in terms of recognition rate and efficiency.

MATHEMATICS OF SENSING, EXPLOITATION, AND EXECUTION (MSEE) Sensing, Exploitation, and Execution (SEE) on a Foundation for Representation, Inference, and Learning

DTIC Science & Technology

2016-07-01

reconstruction, video synchronization, multi - view tracking, action recognition, reasoning with uncertainty 16. SECURITY CLASSIFICATION OF: 17...3.4.2. Human action recognition across multi - views ......................................................................................... 44 3.4.3...68 4.2.1. Multi - view Multi -object Tracking with 3D cues

49 CFR 213.4 - Excepted track.

Code of Federal Regulations, 2010 CFR

2010-10-01

... business hours; (b) The identified segment is not located within 30 feet of an adjacent track which can be subjected to simultaneous use at speeds in excess of 10 miles per hour; (c) The identified segment is... feet on either side, or located on a public street or highway, if railroad cars containing commodities...

Space Operations

DTIC Science & Technology

2009-01-06

enabling precise blue force tracking (BFT), enhancing joint force situational awareness, maneuverability, and command and control (C2... spacecraft , transmits the status of those systems to the control segment on the ground, and receives and processes instructions from the control segment...missions include the tracking , telemetry, and control operations of: (1) Ultrahigh frequency (UHF) follow-on satellite system and fleet

Method and apparatus for optimizing a train trip using signal information

DOEpatents

Kumar, Ajith Kuttannair; Daum, Wolfgang; Otsubo, Tom; Hershey, John Erik; Hess, Gerald James

2013-02-05

One embodiment of the invention includes a system for operating a railway network comprising a first railway vehicle (400) during a trip along track segments (401/412/420). The system comprises a first element (65) for determining travel parameters of the first railway vehicle (400), a second element (65) for determining travel parameters of a second railway vehicle (418) relative to the track segments to be traversed by the first vehicle during the trip, a processor (62) for receiving information from the first (65) and the second (65) elements and for determining a relationship between occupation of a track segment (401/412/420) by the second vehicle (418) and later occupation of the same track segment by the first vehicle (400) and an algorithm embodied within the processor (62) having access to the information to create a trip plan that determines a speed trajectory for the first vehicle (400), wherein the speed trajectory is responsive to the relationship and further in accordance with one or more operational criteria for the first vehicle (400).

Interrupted Monosyllabic Words: The Effects of Ten Interruption Locations on Recognition Performance by Older Listeners with Sensorineural Hearing Loss.

PubMed

Wilson, Richard H; Sharrett, Kadie C

2017-01-01

Two previous experiments from our laboratory with 70 interrupted monosyllabic words demonstrated that recognition performance was influenced by the temporal location of the interruption pattern. The interruption pattern (10 interruptions/sec, 50% duty cycle) was always the same and referenced word onset; the only difference between the patterns was the temporal location of the on- and off-segments of the interruption cycle. In the first study, both young and older listeners obtained better recognition performances when the initial on-segment coincided with word onset than when the initial on-segment was delayed by 50 msec. The second experiment with 24 young listeners detailed recognition performance as the interruption pattern was incremented in 10-msec steps through the 0- to 90-msec onset range. Across the onset conditions, 95% of the functions were either flat or U-shaped. To define the effects that interruption pattern locations had on word recognition by older listeners with sensorineural hearing loss as the interruption pattern incremented, re: word onset, from 0 to 90 msec in 10-msec steps. A repeated-measures design with ten interruption patterns (onset conditions) and one uninterruption condition. Twenty-four older males (mean = 69.6 yr) with sensorineural hearing loss participated in two 1-hour sessions. The three-frequency pure-tone average was 24.0 dB HL and word recognition was ≥80% correct. Seventy consonant-vowel nucleus-consonant words formed the corpus of materials with 25 additional words used for practice. For each participant, the 700 interrupted stimuli (70 words by 10 onset conditions), the 70 words uninterrupted, and two practice lists each were randomized and recorded on compact disc in 33 tracks of 25 words each. The data were analyzed at the participant and word levels and compared to the results obtained earlier on 24 young listeners with normal hearing. The mean recognition performance on the 70 words uninterrupted was 91.0% with an overall mean performance on the ten interruption conditions of 63.2% (range: 57.9-69.3%), compared to 80.4% (range: 73.0-87.7%) obtained earlier on the young adults. The best performances were at the extremes of the onset conditions. Standard deviations ranged from 22.1% to 28.1% (24 participants) and from 9.2% to 12.8% (70 words). An arithmetic algorithm categorized the shapes of the psychometric functions across the ten onset conditions. With the older participants in the current study, 40% of the functions were flat, 41.4% were U-shaped, and 18.6% were inverted U-shaped, which compared favorably to the function shapes by the young listeners in the earlier study of 50.0%, 41.4%, and 8.6%, respectively. There were two words on which the older listeners had 40% better performances. Collectively, the data are orderly, but at the individual word or participant level, the data are somewhat volatile, which may reflect auditory processing differences between the participant groups. The diversity of recognition performances by the older listeners on the ten interruption conditions with each of the 70 words supports the notion that the term hearing loss is inclusive of processes well beyond the filtering produced by end-organ sensitivity deficits. American Academy of Audiology

Acoustic signature recognition technique for Human-Object Interactions (HOI) in persistent surveillance systems

NASA Astrophysics Data System (ADS)

Alkilani, Amjad; Shirkhodaie, Amir

2013-05-01

Handling, manipulation, and placement of objects, hereon called Human-Object Interaction (HOI), in the environment generate sounds. Such sounds are readily identifiable by the human hearing. However, in the presence of background environment noises, recognition of minute HOI sounds is challenging, though vital for improvement of multi-modality sensor data fusion in Persistent Surveillance Systems (PSS). Identification of HOI sound signatures can be used as precursors to detection of pertinent threats that otherwise other sensor modalities may miss to detect. In this paper, we present a robust method for detection and classification of HOI events via clustering of extracted features from training of HOI acoustic sound waves. In this approach, salient sound events are preliminary identified and segmented from background via a sound energy tracking method. Upon this segmentation, frequency spectral pattern of each sound event is modeled and its features are extracted to form a feature vector for training. To reduce dimensionality of training feature space, a Principal Component Analysis (PCA) technique is employed to expedite fast classification of test feature vectors, a kd-tree and Random Forest classifiers are trained for rapid classification of training sound waves. Each classifiers employs different similarity distance matching technique for classification. Performance evaluations of classifiers are compared for classification of a batch of training HOI acoustic signatures. Furthermore, to facilitate semantic annotation of acoustic sound events, a scheme based on Transducer Mockup Language (TML) is proposed. The results demonstrate the proposed approach is both reliable and effective, and can be extended to future PSS applications.

Automated tracking, segmentation and trajectory classification of pelvic organs on dynamic MRI.

PubMed

Nekooeimehr, Iman; Lai-Yuen, Susana; Bao, Paul; Weitzenfeld, Alfredo; Hart, Stuart

2016-08-01

Pelvic organ prolapse is a major health problem in women where pelvic floor organs (bladder, uterus, small bowel, and rectum) fall from their normal position and bulge into the vagina. Dynamic Magnetic Resonance Imaging (DMRI) is presently used to analyze the organs' movements from rest to maximum strain providing complementary support for diagnosis. However, there is currently no automated or quantitative approach to measure the movement of the pelvic organs and their correlation with the severity of prolapse. In this paper, a two-stage method is presented to automatically track and segment pelvic organs on DMRI followed by a multiple-object trajectory classification method to improve the diagnosis of pelvic organ prolapse. Organs are first tracked using particle filters and K-means clustering with prior information. Then, they are segmented using the convex hull of the cluster of particles. Finally, the trajectories of the pelvic organs are modeled using a new Coupled Switched Hidden Markov Model (CSHMM) to classify the severity of pelvic organ prolapse. The tracking and segmentation results are validated using Dice Similarity Index (DSI) whereas the classification results are compared with two manual clinical measurements. Results demonstrate that the presented method is able to automatically track and segment pelvic organs with a DSI above 82% for 26 out of 46 cases and DSI above 75% for all 46 tested cases. The accuracy of the trajectory classification model is also better than current manual measurements.

VIPRAM_L1CMS: a 2-Tier 3D Architecture for Pattern Recognition for Track Finding

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

Hoff, J. R.; Joshi, Joshi,S.; Liu, Liu,

In HEP tracking trigger applications, flagging an individual detector hit is not important. Rather, the path of a charged particle through many detector layers is what must be found. Moreover, given the increased luminosity projected for future LHC experiments, this type of track finding will be required within the Level 1 Trigger system. This means that future LHC experiments require not just a chip capable of high-speed track finding but also one with a high-speed readout architecture. VIPRAM_L1CMS is 2-Tier Vertically Integrated chip designed to fulfill these requirements. It is a complete pipelined Pattern Recognition Associative Memory (PRAM) architecture includingmore » pattern recognition, result sparsification, and readout for Level 1 trigger applications in CMS with 15-bit wide detector addresses and eight detector layers included in the track finding. Pattern recognition is based on classic Content Addressable Memories with a Current Race Scheme to reduce timing complexity and a 4-bit Selective Precharge to minimize power consumption. VIPRAM_L1CMS uses a pipelined set of priority-encoded binary readout structures to sparsify and readout active road flags at frequencies of at least 100MHz. VIPRAM_L1CMS is designed to work directly with the Pulsar2b Architecture.« less

Development of robust behaviour recognition for an at-home biomonitoring robot with assistance of subject localization and enhanced visual tracking.

PubMed

Imamoglu, Nevrez; Dorronzoro, Enrique; Wei, Zhixuan; Shi, Huangjun; Sekine, Masashi; González, José; Gu, Dongyun; Chen, Weidong; Yu, Wenwei

2014-01-01

Our research is focused on the development of an at-home health care biomonitoring mobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination values and tracking distance intervals. Then, regarding subject safety and continuous robot based subject tracking, various control parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was tested on different walking patterns with different subjects, and the results showed high recognition accuracy.

Development of Robust Behaviour Recognition for an at-Home Biomonitoring Robot with Assistance of Subject Localization and Enhanced Visual Tracking

PubMed Central

Imamoglu, Nevrez; Dorronzoro, Enrique; Wei, Zhixuan; Shi, Huangjun; González, José; Gu, Dongyun; Yu, Wenwei

2014-01-01

Our research is focused on the development of an at-home health care biomonitoring mobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination values and tracking distance intervals. Then, regarding subject safety and continuous robot based subject tracking, various control parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was tested on different walking patterns with different subjects, and the results showed high recognition accuracy. PMID:25587560

Tumor propagation model using generalized hidden Markov model

NASA Astrophysics Data System (ADS)

Park, Sun Young; Sargent, Dustin

2017-02-01

Tumor tracking and progression analysis using medical images is a crucial task for physicians to provide accurate and efficient treatment plans, and monitor treatment response. Tumor progression is tracked by manual measurement of tumor growth performed by radiologists. Several methods have been proposed to automate these measurements with segmentation, but many current algorithms are confounded by attached organs and vessels. To address this problem, we present a new generalized tumor propagation model considering time-series prior images and local anatomical features using a Hierarchical Hidden Markov model (HMM) for tumor tracking. First, we apply the multi-atlas segmentation technique to identify organs/sub-organs using pre-labeled atlases. Second, we apply a semi-automatic direct 3D segmentation method to label the initial boundary between the lesion and neighboring structures. Third, we detect vessels in the ROI surrounding the lesion. Finally, we apply the propagation model with the labeled organs and vessels to accurately segment and measure the target lesion. The algorithm has been designed in a general way to be applicable to various body parts and modalities. In this paper, we evaluate the proposed algorithm on lung and lung nodule segmentation and tracking. We report the algorithm's performance by comparing the longest diameter and nodule volumes using the FDA lung Phantom data and a clinical dataset.

Semi-automatic tracking, smoothing and segmentation of hyoid bone motion from videofluoroscopic swallowing study.

PubMed

Kim, Won-Seok; Zeng, Pengcheng; Shi, Jian Qing; Lee, Youngjo; Paik, Nam-Jong

2017-01-01

Motion analysis of the hyoid bone via videofluoroscopic study has been used in clinical research, but the classical manual tracking method is generally labor intensive and time consuming. Although some automatic tracking methods have been developed, masked points could not be tracked and smoothing and segmentation, which are necessary for functional motion analysis prior to registration, were not provided by the previous software. We developed software to track the hyoid bone motion semi-automatically. It works even in the situation where the hyoid bone is masked by the mandible and has been validated in dysphagia patients with stroke. In addition, we added the function of semi-automatic smoothing and segmentation. A total of 30 patients' data were used to develop the software, and data collected from 17 patients were used for validation, of which the trajectories of 8 patients were partly masked. Pearson correlation coefficients between the manual and automatic tracking are high and statistically significant (0.942 to 0.991, P-value<0.0001). Relative errors between automatic tracking and manual tracking in terms of the x-axis, y-axis and 2D range of hyoid bone excursion range from 3.3% to 9.2%. We also developed an automatic method to segment each hyoid bone trajectory into four phases (elevation phase, anterior movement phase, descending phase and returning phase). The semi-automatic hyoid bone tracking from VFSS data by our software is valid compared to the conventional manual tracking method. In addition, the ability of automatic indication to switch the automatic mode to manual mode in extreme cases and calibration without attaching the radiopaque object is convenient and useful for users. Semi-automatic smoothing and segmentation provide further information for functional motion analysis which is beneficial to further statistical analysis such as functional classification and prognostication for dysphagia. Therefore, this software could provide the researchers in the field of dysphagia with a convenient, useful, and all-in-one platform for analyzing the hyoid bone motion. Further development of our method to track the other swallowing related structures or objects such as epiglottis and bolus and to carry out the 2D curve registration may be needed for a more comprehensive functional data analysis for dysphagia with big data.

Robust visual object tracking with interleaved segmentation

NASA Astrophysics Data System (ADS)

Abel, Peter; Kieritz, Hilke; Becker, Stefan; Arens, Michael

2017-10-01

In this paper we present a new approach for tracking non-rigid, deformable objects by means of merging an on-line boosting-based tracker and a fast foreground background segmentation. We extend an on-line boosting- based tracker, which uses axes-aligned bounding boxes with fixed aspect-ratio as tracking states. By constructing a confidence map from the on-line boosting-based tracker and unifying this map with a confidence map, which is obtained from a foreground background segmentation algorithm, we build a superior confidence map. For constructing a rough confidence map of a new frame based on on-line boosting, we employ the responses of the strong classifier as well as the single weak classifier responses that were built before during the updating step. This confidence map provides a rough estimation of the object's position and dimension. In order to refine this confidence map, we build a fine, pixel-wisely segmented confidence map and merge both maps together. Our segmentation method is color-histogram-based and provides a fine and fast image segmentation. By means of back-projection and the Bayes' rule, we obtain a confidence value for every pixel. The rough and the fine confidence maps are merged together by building an adaptively weighted sum of both maps. The weights are obtained by utilizing the variances of both confidence maps. Further, we apply morphological operators in the merged confidence map in order to reduce the noise. In the resulting map we estimate the object localization and dimension via continuous adaptive mean shift. Our approach provides a rotated rectangle as tracking states, which enables a more precise description of non-rigid, deformable objects than axes-aligned bounding boxes. We evaluate our tracker on the visual object tracking (VOT) benchmark dataset 2016.

Texture segmentation of non-cooperative spacecrafts images based on wavelet and fractal dimension

NASA Astrophysics Data System (ADS)

Wu, Kanzhi; Yue, Xiaokui

2011-06-01

With the increase of on-orbit manipulations and space conflictions, missions such as tracking and capturing the target spacecrafts are aroused. Unlike cooperative spacecrafts, fixing beacons or any other marks on the targets is impossible. Due to the unknown shape and geometry features of non-cooperative spacecraft, in order to localize the target and obtain the latitude, we need to segment the target image and recognize the target from the background. The data and errors during the following procedures such as feature extraction and matching can also be reduced. Multi-resolution analysis of wavelet theory reflects human beings' recognition towards images from low resolution to high resolution. In addition, spacecraft is the only man-made object in the image compared to the natural background and the differences will be certainly observed between the fractal dimensions of target and background. Combined wavelet transform and fractal dimension, in this paper, we proposed a new segmentation algorithm for the images which contains complicated background such as the universe and planet surfaces. At first, Daubechies wavelet basis is applied to decompose the image in both x axis and y axis, thus obtain four sub-images. Then, calculate the fractal dimensions in four sub-images using different methods; after analyzed the results of fractal dimensions in sub-images, we choose Differential Box Counting in low resolution image as the principle to segment the texture which has the greatest divergences between different sub-images. This paper also presents the results of experiments by using the algorithm above. It is demonstrated that an accurate texture segmentation result can be obtained using the proposed technique.

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Generation, recognition, and consistent fusion of partial boundary representations from range images

NASA Astrophysics Data System (ADS)

Kohlhepp, Peter; Hanczak, Andrzej M.; Li, Gang

1994-10-01

This paper presents SOMBRERO, a new system for recognizing and locating 3D, rigid, non- moving objects from range data. The objects may be polyhedral or curved, partially occluding, touching or lying flush with each other. For data collection, we employ 2D time- of-flight laser scanners mounted to a moving gantry robot. By combining sensor and robot coordinates, we obtain 3D cartesian coordinates. Boundary representations (Brep's) provide view independent geometry models that are both efficiently recognizable and derivable automatically from sensor data. SOMBRERO's methods for generating, matching and fusing Brep's are highly synergetic. A split-and-merge segmentation algorithm with dynamic triangular builds a partial (21/2D) Brep from scattered data. The recognition module matches this scene description with a model database and outputs recognized objects, their positions and orientations, and possibly surfaces corresponding to unknown objects. We present preliminary results in scene segmentation and recognition. Partial Brep's corresponding to different range sensors or viewpoints can be merged into a consistent, complete and irredundant 3D object or scene model. This fusion algorithm itself uses the recognition and segmentation methods.

Diminutives facilitate word segmentation in natural speech: cross-linguistic evidence.

PubMed

Kempe, Vera; Brooks, Patricia J; Gillis, Steven; Samson, Graham

2007-06-01

Final-syllable invariance is characteristic of diminutives (e.g., doggie), which are a pervasive feature of the child-directed speech registers of many languages. Invariance in word endings has been shown to facilitate word segmentation (Kempe, Brooks, & Gillis, 2005) in an incidental-learning paradigm in which synthesized Dutch pseudonouns were used. To broaden the cross-linguistic evidence for this invariance effect and to increase its ecological validity, adult English speakers (n=276) were exposed to naturally spoken Dutch or Russian pseudonouns presented in sentence contexts. A forced choice test was given to assess target recognition, with foils comprising unfamiliar syllable combinations in Experiments 1 and 2 and syllable combinations straddling word boundaries in Experiment 3. A control group (n=210) received the recognition test with no prior exposure to targets. Recognition performance improved with increasing final-syllable rhyme invariance, with larger increases for the experimental group. This confirms that word ending invariance is a valid segmentation cue in artificial, as well as naturalistic, speech and that diminutives may aid segmentation in a number of languages.

Iris recognition: on the segmentation of degraded images acquired in the visible wavelength.

PubMed

Proença, Hugo

2010-08-01

Iris recognition imaging constraints are receiving increasing attention. There are several proposals to develop systems that operate in the visible wavelength and in less constrained environments. These imaging conditions engender acquired noisy artifacts that lead to severely degraded images, making iris segmentation a major issue. Having observed that existing iris segmentation methods tend to fail in these challenging conditions, we present a segmentation method that can handle degraded images acquired in less constrained conditions. We offer the following contributions: 1) to consider the sclera the most easily distinguishable part of the eye in degraded images, 2) to propose a new type of feature that measures the proportion of sclera in each direction and is fundamental in segmenting the iris, and 3) to run the entire procedure in deterministically linear time in respect to the size of the image, making the procedure suitable for real-time applications.

A two-stage approach for fully automatic segmentation of venous vascular structures in liver CT images

NASA Astrophysics Data System (ADS)

Kaftan, Jens N.; Tek, Hüseyin; Aach, Til

2009-02-01

The segmentation of the hepatic vascular tree in computed tomography (CT) images is important for many applications such as surgical planning of oncological resections and living liver donations. In surgical planning, vessel segmentation is often used as basis to support the surgeon in the decision about the location of the cut to be performed and the extent of the liver to be removed, respectively. We present a novel approach to hepatic vessel segmentation that can be divided into two stages. First, we detect and delineate the core vessel components efficiently with a high specificity. Second, smaller vessel branches are segmented by a robust vessel tracking technique based on a medialness filter response, which starts from the terminal points of the previously segmented vessels. Specifically, in the first phase major vessels are segmented using the globally optimal graphcuts algorithm in combination with foreground and background seed detection, while the computationally more demanding tracking approach needs to be applied only locally in areas of smaller vessels within the second stage. The method has been evaluated on contrast-enhanced liver CT scans from clinical routine showing promising results. In addition to the fully-automatic instance of this method, the vessel tracking technique can also be used to easily add missing branches/sub-trees to an already existing segmentation result by adding single seed-points.

A top-down manner-based DCNN architecture for semantic image segmentation.

PubMed

Qiao, Kai; Chen, Jian; Wang, Linyuan; Zeng, Lei; Yan, Bin

2017-01-01

Given their powerful feature representation for recognition, deep convolutional neural networks (DCNNs) have been driving rapid advances in high-level computer vision tasks. However, their performance in semantic image segmentation is still not satisfactory. Based on the analysis of visual mechanism, we conclude that DCNNs in a bottom-up manner are not enough, because semantic image segmentation task requires not only recognition but also visual attention capability. In the study, superpixels containing visual attention information are introduced in a top-down manner, and an extensible architecture is proposed to improve the segmentation results of current DCNN-based methods. We employ the current state-of-the-art fully convolutional network (FCN) and FCN with conditional random field (DeepLab-CRF) as baselines to validate our architecture. Experimental results of the PASCAL VOC segmentation task qualitatively show that coarse edges and error segmentation results are well improved. We also quantitatively obtain about 2%-3% intersection over union (IOU) accuracy improvement on the PASCAL VOC 2011 and 2012 test sets.

Microstructure analysis of the secondary pulmonary lobules by 3D synchrotron radiation CT

NASA Astrophysics Data System (ADS)

Fukuoka, Y.; Kawata, Y.; Niki, N.; Umetani, K.; Nakano, Y.; Ohmatsu, H.; Moriyama, N.; Itoh, H.

2014-03-01

Recognition of abnormalities related to the lobular anatomy has become increasingly important in the diagnosis and differential diagnosis of lung abnormalities at clinical routines of CT examinations. This paper aims a 3-D microstructural analysis of the pulmonary acinus with isotropic spatial resolution in the range of several micrometers by using micro CT. Previously, we demonstrated the ability of synchrotron radiation micro CT (SRμCT) using offset scan mode in microstructural analysis of the whole part of the secondary pulmonary lobule. In this paper, we present a semiautomatic method to segment the acinar and subacinar airspaces from the secondary pulmonary lobule and to track small vessels running inside alveolar walls in human acinus imaged by the SRμCT. The method beains with and segmentation of the tissues such as pleural surface, interlobular septa, alveola wall, or vessel using a threshold technique and 3-D connected component analysis. 3-D air space are then conustructed separated by tissues and represented branching patterns of airways and airspaces distal to the terminal bronchiole. A graph-partitioning approach isolated acini whose stems are interactively defined as the terminal bronchiole in the secondary pulmonary lobule. Finally, we performed vessel tracking using a non-linear sate space which captures both smoothness of the trajectories and intensity coherence along vessel orientations. Results demonstrate that the proposed method can extract several acinar airspaces from the 3-D SRμCT image of secondary pulmonary lobule and that the extracted acinar airspace enable an accurate quantitative description of the anatomy of the human acinus for interpretation of the basic unit of pulmonary structure and function.

KENNEDY SPACE CENTER, FLA. - An engine pulls the container enclosing a segment of a solid rocket booster from the Rotation Processing and Surge Facility. The container will join others on the main track for a trip to Utah where the segments will undergo firing. The segments were part of the STS-114 stack. It is the first time actual flight segments that had been stacked for flight in the VAB are being returned for testing. They will undergo firing, which will enable inspectors to check the viability of the solid and verify the life expectancy for stacked segments.

NASA Image and Video Library

2004-01-30

KENNEDY SPACE CENTER, FLA. - An engine pulls the container enclosing a segment of a solid rocket booster from the Rotation Processing and Surge Facility. The container will join others on the main track for a trip to Utah where the segments will undergo firing. The segments were part of the STS-114 stack. It is the first time actual flight segments that had been stacked for flight in the VAB are being returned for testing. They will undergo firing, which will enable inspectors to check the viability of the solid and verify the life expectancy for stacked segments.

Trends in Correlation-Based Pattern Recognition and Tracking in Forward-Looking Infrared Imagery

PubMed Central

Alam, Mohammad S.; Bhuiyan, Sharif M. A.

2014-01-01

In this paper, we review the recent trends and advancements on correlation-based pattern recognition and tracking in forward-looking infrared (FLIR) imagery. In particular, we discuss matched filter-based correlation techniques for target detection and tracking which are widely used for various real time applications. We analyze and present test results involving recently reported matched filters such as the maximum average correlation height (MACH) filter and its variants, and distance classifier correlation filter (DCCF) and its variants. Test results are presented for both single/multiple target detection and tracking using various real-life FLIR image sequences. PMID:25061840

KENNEDY SPACE CENTER, FLA. - The red NASA engine backs up with its cargo of containers in order to change tracks. The containers enclose segments of a solid rocket booster being returned to Utah for testing. The segments were part of the STS-114 stack. It is the first time actual flight segments that had been stacked for flight in the VAB are being returned for testing. They will undergo firing, which will enable inspectors to check the viability of the solid and verify the life expectancy for stacked segments.

NASA Image and Video Library

2004-01-30

KENNEDY SPACE CENTER, FLA. - The red NASA engine backs up with its cargo of containers in order to change tracks. The containers enclose segments of a solid rocket booster being returned to Utah for testing. The segments were part of the STS-114 stack. It is the first time actual flight segments that had been stacked for flight in the VAB are being returned for testing. They will undergo firing, which will enable inspectors to check the viability of the solid and verify the life expectancy for stacked segments.

Fine-grained recognition of plants from images.

PubMed

Šulc, Milan; Matas, Jiří

2017-01-01

Fine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differences. We review the state-of-the-art and discuss plant recognition tasks, from identification of plants from specific plant organs to general plant recognition "in the wild". We propose texture analysis and deep learning methods for different plant recognition tasks. The methods are evaluated and compared them to the state-of-the-art. Texture analysis is only applied to images with unambiguous segmentation (bark and leaf recognition), whereas CNNs are only applied when sufficiently large datasets are available. The results provide an insight in the complexity of different plant recognition tasks. The proposed methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition "in the wild". The results suggest that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available. The generality and higher capacity of state-of-the-art CNNs makes them suitable for plant recognition "in the wild" where the views on plant organs or plants vary significantly and the difficulty is increased by occlusions and background clutter.

Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research.

PubMed

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-Etriby, Sherif

2016-03-11

Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers-that we proposed earlier-improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction.

Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research

PubMed Central

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-etriby, Sherif

2016-01-01

Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers—that we proposed earlier—improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction. PMID:26978368

Image-based tracking of the suturing needle during laparoscopic interventions

NASA Astrophysics Data System (ADS)

Speidel, S.; Kroehnert, A.; Bodenstedt, S.; Kenngott, H.; Müller-Stich, B.; Dillmann, R.

2015-03-01

One of the most complex and difficult tasks for surgeons during minimally invasive interventions is suturing. A prerequisite to assist the suturing process is the tracking of the needle. The endoscopic images provide a rich source of information which can be used for needle tracking. In this paper, we present an image-based method for markerless needle tracking. The method uses a color-based and geometry-based segmentation to detect the needle. Once an initial needle detection is obtained, a region of interest enclosing the extracted needle contour is passed on to a reduced segmentation. It is evaluated with in vivo images from da Vinci interventions.

Real-time motion compensation for EM bronchoscope tracking with smooth output - ex-vivo validation

NASA Astrophysics Data System (ADS)

Reichl, Tobias; Gergel, Ingmar; Menzel, Manuela; Hautmann, Hubert; Wegner, Ingmar; Meinzer, Hans-Peter; Navab, Nassir

2012-02-01

Navigated bronchoscopy provides benefits for endoscopists and patients, but accurate tracking information is needed. We present a novel real-time approach for bronchoscope tracking combining electromagnetic (EM) tracking, airway segmentation, and a continuous model of output. We augment a previously published approach by including segmentation information in the tracking optimization instead of image similarity. Thus, the new approach is feasible in real-time. Since the true bronchoscope trajectory is continuous, the output is modeled using splines and the control points are optimized with respect to displacement from EM tracking measurements and spatial relation to segmented airways. Accuracy of the proposed method and its components is evaluated on a ventilated porcine ex-vivo lung with respect to ground truth data acquired from a human expert. We demonstrate the robustness of the output of the proposed method against added artificial noise in the input data. Smoothness in terms of inter-frame distance is shown to remain below 2 mm, even when up to 5 mm of Gaussian noise are added to the input. The approach is shown to be easily extensible to include other measures like image similarity.

Memory for temporally dynamic scenes.

PubMed

Ferguson, Ryan; Homa, Donald; Ellis, Derek

2017-07-01

Recognition memory was investigated for individual frames extracted from temporally continuous, visually rich film segments of 5-15 min. Participants viewed a short clip from a film in either a coherent or a jumbled order, followed by a recognition test of studied frames. Foils came either from an earlier or a later part of the film (Experiment 1) or from deleted segments selected from random cuts of varying duration (0.5 to 30 s) within the film itself (Experiment 2). When the foils came from an earlier or later part of the film (Experiment 1), recognition was excellent, with the hit rate far exceeding the false-alarm rate (.78 vs. 18). In Experiment 2, recognition was far worse, with the hit rate (.76) exceeding the false-alarm rate only for foils drawn from the longest cuts (15 and 30 s) and matching the false-alarm rate for the 5 s segments. When the foils were drawn from the briefest cuts (0.5 and 1.0 s), the false-alarm rate exceeded the hit rate. Unexpectedly, jumbling had no effect on recognition in either experiment. These results are consistent with the view that memory for complex visually temporal events is excellent, with the integrity unperturbed by disruption of the global structure of the visual stream. Disruption of memory was observed only when foils were drawn from embedded segments of duration less than 5 s, an outcome consistent with the view that memory at these shortest durations are consolidated with expectations drawn from the previous stream.

Image Processing Strategies Based on a Visual Saliency Model for Object Recognition Under Simulated Prosthetic Vision.

PubMed

Wang, Jing; Li, Heng; Fu, Weizhen; Chen, Yao; Li, Liming; Lyu, Qing; Han, Tingting; Chai, Xinyu

2016-01-01

Retinal prostheses have the potential to restore partial vision. Object recognition in scenes of daily life is one of the essential tasks for implant wearers. Still limited by the low-resolution visual percepts provided by retinal prostheses, it is important to investigate and apply image processing methods to convey more useful visual information to the wearers. We proposed two image processing strategies based on Itti's visual saliency map, region of interest (ROI) extraction, and image segmentation. Itti's saliency model generated a saliency map from the original image, in which salient regions were grouped into ROI by the fuzzy c-means clustering. Then Grabcut generated a proto-object from the ROI labeled image which was recombined with background and enhanced in two ways--8-4 separated pixelization (8-4 SP) and background edge extraction (BEE). Results showed that both 8-4 SP and BEE had significantly higher recognition accuracy in comparison with direct pixelization (DP). Each saliency-based image processing strategy was subject to the performance of image segmentation. Under good and perfect segmentation conditions, BEE and 8-4 SP obtained noticeably higher recognition accuracy than DP, and under bad segmentation condition, only BEE boosted the performance. The application of saliency-based image processing strategies was verified to be beneficial to object recognition in daily scenes under simulated prosthetic vision. They are hoped to help the development of the image processing module for future retinal prostheses, and thus provide more benefit for the patients. Copyright © 2015 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Vision-based object detection and recognition system for intelligent vehicles

NASA Astrophysics Data System (ADS)

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

1999-01-01

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

The study of infrared target recognition at sea background based on visual attention computational model

NASA Astrophysics Data System (ADS)

Wang, Deng-wei; Zhang, Tian-xu; Shi, Wen-jun; Wei, Long-sheng; Wang, Xiao-ping; Ao, Guo-qing

2009-07-01

Infrared images at sea background are notorious for the low signal-to-noise ratio, therefore, the target recognition of infrared image through traditional methods is very difficult. In this paper, we present a novel target recognition method based on the integration of visual attention computational model and conventional approach (selective filtering and segmentation). The two distinct techniques for image processing are combined in a manner to utilize the strengths of both. The visual attention algorithm searches the salient regions automatically, and represented them by a set of winner points, at the same time, demonstrated the salient regions in terms of circles centered at these winner points. This provides a priori knowledge for the filtering and segmentation process. Based on the winner point, we construct a rectangular region to facilitate the filtering and segmentation, then the labeling operation will be added selectively by requirement. Making use of the labeled information, from the final segmentation result we obtain the positional information of the interested region, label the centroid on the corresponding original image, and finish the localization for the target. The cost time does not depend on the size of the image but the salient regions, therefore the consumed time is greatly reduced. The method is used in the recognition of several kinds of real infrared images, and the experimental results reveal the effectiveness of the algorithm presented in this paper.

Thai Automatic Speech Recognition

DTIC Science & Technology

2005-01-01

used in an external DARPA evaluation involving medical scenarios between an American Doctor and a naïve monolingual Thai patient. 2. Thai Language... dictionary generation more challenging, and (3) the lack of word segmentation, which calls for automatic segmentation approaches to make n-gram language...requires a dictionary and provides various segmentation algorithms to automatically select suitable segmentations. Here we used a maximal matching

Coupled chemical reactions in dynamic nanometric confinement: VII. Biosensors based on swift heavy ion tracks with membranes

NASA Astrophysics Data System (ADS)

Fink, D.; Muñoz H., G.; Garcia-Arrelano, H.; Alfonta, L.; Vacik, J.; Kiv, A.; Hnatowicz, V.

2017-02-01

In previous papers it was shown that the coupling of the two chemical reactions: {NaOH etchant - PET polymer} and {NaOH etchant - AgNO3 solution} within the dynamic confinement of etched swift heavy ion tracks eventually leads to the formation of tiny Ag2O membranes within these nanopores, thus separating the latter ones into two adjacent segments. It is shown here that the deposition of enzymes in these two segments transforms these structures into biosensors. In our earlier developed sensors with transparent etched ion tracks, we frequently used glucose oxidase as enzyme and glucose as analyte. In these cases, the enzymatic reaction within the tracks leads to a change in the pH value of the confined solution and hence also in the track conductivity, so these structures can be used for biosensing. When applying, for easy comparison, the same enzyme/analyte combination to the segmented sensor arrangement presented here, we find a striking improvement in detection sensitivity which points at a different biosensing mechanism due to intrinsic polarisation effects across the newly inserted membranes.

Recognition-by-Components: A Theory of Human Image Understanding.

ERIC Educational Resources Information Center

Biederman, Irving

1987-01-01

The theory proposed (recognition-by-components) hypothesizes the perceptual recognition of objects to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components. Experiments on the perception of briefly presented pictures support the theory. (Author/LMO)

On the Importance of Polar Interactions for Complexes Containing Intrinsically Disordered Proteins

PubMed Central

Wong, Eric T. C.; Na, Dokyun; Gsponer, Jörg

2013-01-01

There is a growing recognition for the importance of proteins with large intrinsically disordered (ID) segments in cell signaling and regulation. ID segments in these proteins often harbor regions that mediate molecular recognition. Coupled folding and binding of the recognition regions has been proposed to confer high specificity to interactions involving ID segments. However, researchers recently questioned the origin of the interaction specificity of ID proteins because of the overrepresentation of hydrophobic residues in their interaction interfaces. Here, we focused on the role of polar and charged residues in interactions mediated by ID segments. Making use of the extended nature of most ID segments when in complex with globular proteins, we first identified large numbers of complexes between globular proteins and ID segments by using radius-of-gyration-based selection criteria. Consistent with previous studies, we found the interfaces of these complexes to be enriched in hydrophobic residues, and that these residues contribute significantly to the stability of the interaction interface. However, our analyses also show that polar interactions play a larger role in these complexes than in structured protein complexes. Computational alanine scanning and salt-bridge analysis indicate that interfaces in ID complexes are highly complementary with respect to electrostatics, more so than interfaces of globular proteins. Follow-up calculations of the electrostatic contributions to the free energy of binding uncovered significantly stronger Coulombic interactions in complexes harbouring ID segments than in structured protein complexes. However, they are counter-balanced by even higher polar-desolvation penalties. We propose that polar interactions are a key contributing factor to the observed high specificity of ID segment-mediated interactions. PMID:23990768

No damage to rail cars or SRB segments in derailment

NASA Technical Reports Server (NTRS)

2000-01-01

After being involved in a minor derailment incident during a routine movement on the tracks, rail cars carrying solid rocket booster segments sit idle. The rail cars were being moved as part of a standard operation to '''order''' the cars, placing them into a proper sequence for upcoming segment processing activities. The rear wheels of one car and the front wheels of the car behind it slid off the tracks while passing through a railway switch onto a siding. They were traveling approximately 3 miles per hour at the time, about normal walking speed. No damage occurred to the SRB segments, or to the devices that secure the segments to the rail cars. The incident occurred on KSC property, just north of the NASA Causeway in the KSC Industrial Area.

KSC-00pp0934

NASA Image and Video Library

2000-07-20

One of two solid rocket booster rail cars is off the track after being involved in a minor derailment incident during a routine movement on the tracks. The rail cars were being moved as part of a standard operation to “order” the cars, placing them into a proper sequence for upcoming segment processing activities. The rear wheels of one car and the front wheels of the car behind it slid off the tracks while passing through a railway switch onto a siding. They were traveling approximately 3 miles per hour at the time, about normal walking speed. No damage occurred to the SRB segments, or to the devices that secure the segments to the rail cars. The incident occurred on KSC property, just north of the NASA Causeway in the KSC Industrial Area

KSC00pp0934

NASA Image and Video Library

2000-07-20

One of two solid rocket booster rail cars is off the track after being involved in a minor derailment incident during a routine movement on the tracks. The rail cars were being moved as part of a standard operation to “order” the cars, placing them into a proper sequence for upcoming segment processing activities. The rear wheels of one car and the front wheels of the car behind it slid off the tracks while passing through a railway switch onto a siding. They were traveling approximately 3 miles per hour at the time, about normal walking speed. No damage occurred to the SRB segments, or to the devices that secure the segments to the rail cars. The incident occurred on KSC property, just north of the NASA Causeway in the KSC Industrial Area

Impact of touring, performance schedule, and definitions on 1-year injury rates in a modern dance company.

PubMed

Bronner, Shaw; Wood, Lily

2017-11-01

There is ongoing debate about how to define injury in dance: the most encompassing one or a time-loss definition. We examined the relationship between touring, performance schedule and injury definition on injury rates in a professional modern dance company over one-year. In-house healthcare management tracked 35 dancers for work-related musculoskeletal injuries (WMSI), time-loss injuries (TLinj), complaints, and exposure. The year was divided into 6 segments to allow comparison of effects of performance, rehearsal, and touring. Injuries/segment were converted into injuries/1000-h dance exposure. We conducted negative binomial regression analysis to determine differences between segments, P ≤ 0.05. Twenty WMSI, 0.44 injuries/1000-h, were sustained over one-year. WMSI were 6 times more likely to occur in Segment-6, compared with other segments (incident rate ratio = 6.055, P = 0.031). The highest rate of TLinj and traumatic injuries also occurred in Segment-6, reflecting concentrated rehearsal, New York season and performances abroad. More overuse injuries occurred in Segment-2, an international tour, attributed to raked stages. Lack of methods to quantify performance other than injury may mask effects of touring on dancer's well-being. Tracking complaints permits understanding of stressors to specific body regions and healthcare utilisation; however, TLinj remain the most important injuries to track because they impact other dancers and organisational costs.

Ground target recognition using rectangle estimation.

PubMed

Grönwall, Christina; Gustafsson, Fredrik; Millnert, Mille

2006-11-01

We propose a ground target recognition method based on 3-D laser radar data. The method handles general 3-D scattered data. It is based on the fact that man-made objects of complex shape can be decomposed to a set of rectangles. The ground target recognition method consists of four steps; 3-D size and orientation estimation, target segmentation into parts of approximately rectangular shape, identification of segments that represent the target's functional/main parts, and target matching with CAD models. The core in this approach is rectangle estimation. The performance of the rectangle estimation method is evaluated statistically using Monte Carlo simulations. A case study on tank recognition is shown, where 3-D data from four fundamentally different types of laser radar systems are used. Although the approach is tested on rather few examples, we believe that the approach is promising.

The activation of segmental and tonal information in visual word recognition.

PubMed

Li, Chuchu; Lin, Candise Y; Wang, Min; Jiang, Nan

2013-08-01

Mandarin Chinese has a logographic script in which graphemes map onto syllables and morphemes. It is not clear whether Chinese readers activate phonological information during lexical access, although phonological information is not explicitly represented in Chinese orthography. In the present study, we examined the activation of phonological information, including segmental and tonal information in Chinese visual word recognition, using the Stroop paradigm. Native Mandarin speakers named the presentation color of Chinese characters in Mandarin. The visual stimuli were divided into five types: color characters (e.g., , hong2, "red"), homophones of the color characters (S+T+; e.g., , hong2, "flood"), different-tone homophones (S+T-; e.g., , hong1, "boom"), characters that shared the same tone but differed in segments with the color characters (S-T+; e.g., , ping2, "bottle"), and neutral characters (S-T-; e.g., , qian1, "leading through"). Classic Stroop facilitation was shown in all color-congruent trials, and interference was shown in the incongruent trials. Furthermore, the Stroop effect was stronger for S+T- than for S-T+ trials, and was similar between S+T+ and S+T- trials. These findings suggested that both tonal and segmental forms of information play roles in lexical constraints; however, segmental information has more weight than tonal information. We proposed a revised visual word recognition model in which the functions of both segmental and suprasegmental types of information and their relative weights are taken into account.

Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process

NASA Astrophysics Data System (ADS)

Nakanishi, W.; Fuse, T.; Ishikawa, T.

2015-05-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.

A probability tracking approach to segmentation of ultrasound prostate images using weak shape priors

NASA Astrophysics Data System (ADS)

Xu, Robert S.; Michailovich, Oleg V.; Solovey, Igor; Salama, Magdy M. A.

2010-03-01

Prostate specific antigen density is an established parameter for indicating the likelihood of prostate cancer. To this end, the size and volume of the gland have become pivotal quantities used by clinicians during the standard cancer screening process. As an alternative to manual palpation, an increasing number of volume estimation methods are based on the imagery data of the prostate. The necessity to process large volumes of such data requires automatic segmentation algorithms, which can accurately and reliably identify the true prostate region. In particular, transrectal ultrasound (TRUS) imaging has become a standard means of assessing the prostate due to its safe nature and high benefit-to-cost ratio. Unfortunately, modern TRUS images are still plagued by many ultrasound imaging artifacts such as speckle noise and shadowing, which results in relatively low contrast and reduced SNR of the acquired images. Consequently, many modern segmentation methods incorporate prior knowledge about the prostate geometry to enhance traditional segmentation techniques. In this paper, a novel approach to the problem of TRUS segmentation, particularly the definition of the prostate shape prior, is presented. The proposed approach is based on the concept of distribution tracking, which provides a unified framework for tracking both photometric and morphological features of the prostate. In particular, the tracking of morphological features defines a novel type of "weak" shape priors. The latter acts as a regularization force, which minimally bias the segmentation procedure, while rendering the final estimate stable and robust. The value of the proposed methodology is demonstrated in a series of experiments.

Real-time edge tracking using a tactile sensor

NASA Technical Reports Server (NTRS)

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

1989-01-01

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

Motion Imagery Processing and Exploitation (MIPE)

DTIC Science & Technology

2013-01-01

facial recognition —i.e., the identification of a specific person.37 Object detection is often (but not always) considered a prerequisite for instance...The goal of segmentation is to distinguish objects and identify boundaries in images. Some of the earliest approaches to facial recognition involved...methods of instance recognition are at varying levels of maturity. Facial recognition methods are arguably the most mature; the technology is well

Automated Target Acquisition, Recognition and Tracking (ATTRACT). Phase 1

NASA Technical Reports Server (NTRS)

Abdallah, Mahmoud A.

1995-01-01

The primary objective of phase 1 of this research project is to conduct multidisciplinary research that will contribute to fundamental scientific knowledge in several of the USAF critical technology areas. Specifically, neural networks, signal processing techniques, and electro-optic capabilities are utilized to solve problems associated with automated target acquisition, recognition, and tracking. To accomplish the stated objective, several tasks have been identified and were executed.

Tracking the Time Course of Word-Frequency Effects in Auditory Word Recognition with Event-Related Potentials

ERIC Educational Resources Information Center

Dufour, Sophie; Brunelliere, Angele; Frauenfelder, Ulrich H.

2013-01-01

Although the word-frequency effect is one of the most established findings in spoken-word recognition, the precise processing locus of this effect is still a topic of debate. In this study, we used event-related potentials (ERPs) to track the time course of the word-frequency effect. In addition, the neighborhood density effect, which is known to…

3D multi-scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images.

PubMed

Li, Xiaomeng; Dou, Qi; Chen, Hao; Fu, Chi-Wing; Qi, Xiaojuan; Belavý, Daniel L; Armbrecht, Gabriele; Felsenberg, Dieter; Zheng, Guoyan; Heng, Pheng-Ann

2018-04-01

Intervertebral discs (IVDs) are small joints that lie between adjacent vertebrae. The localization and segmentation of IVDs are important for spine disease diagnosis and measurement quantification. However, manual annotation is time-consuming and error-prone with limited reproducibility, particularly for volumetric data. In this work, our goal is to develop an automatic and accurate method based on fully convolutional networks (FCN) for the localization and segmentation of IVDs from multi-modality 3D MR data. Compared with single modality data, multi-modality MR images provide complementary contextual information, which contributes to better recognition performance. However, how to effectively integrate such multi-modality information to generate accurate segmentation results remains to be further explored. In this paper, we present a novel multi-scale and modality dropout learning framework to locate and segment IVDs from four-modality MR images. First, we design a 3D multi-scale context fully convolutional network, which processes the input data in multiple scales of context and then merges the high-level features to enhance the representation capability of the network for handling the scale variation of anatomical structures. Second, to harness the complementary information from different modalities, we present a random modality voxel dropout strategy which alleviates the co-adaption issue and increases the discriminative capability of the network. Our method achieved the 1st place in the MICCAI challenge on automatic localization and segmentation of IVDs from multi-modality MR images, with a mean segmentation Dice coefficient of 91.2% and a mean localization error of 0.62 mm. We further conduct extensive experiments on the extended dataset to validate our method. We demonstrate that the proposed modality dropout strategy with multi-modality images as contextual information improved the segmentation accuracy significantly. Furthermore, experiments conducted on extended data collected from two different time points demonstrate the efficacy of our method on tracking the morphological changes in a longitudinal study. Copyright © 2018 Elsevier B.V. All rights reserved.

Tone classification of syllable-segmented Thai speech based on multilayer perception

NASA Astrophysics Data System (ADS)

Satravaha, Nuttavudh; Klinkhachorn, Powsiri; Lass, Norman

2002-05-01

Thai is a monosyllabic tonal language that uses tone to convey lexical information about the meaning of a syllable. Thus to completely recognize a spoken Thai syllable, a speech recognition system not only has to recognize a base syllable but also must correctly identify a tone. Hence, tone classification of Thai speech is an essential part of a Thai speech recognition system. Thai has five distinctive tones (``mid,'' ``low,'' ``falling,'' ``high,'' and ``rising'') and each tone is represented by a single fundamental frequency (F0) pattern. However, several factors, including tonal coarticulation, stress, intonation, and speaker variability, affect the F0 pattern of a syllable in continuous Thai speech. In this study, an efficient method for tone classification of syllable-segmented Thai speech, which incorporates the effects of tonal coarticulation, stress, and intonation, as well as a method to perform automatic syllable segmentation, were developed. Acoustic parameters were used as the main discriminating parameters. The F0 contour of a segmented syllable was normalized by using a z-score transformation before being presented to a tone classifier. The proposed system was evaluated on 920 test utterances spoken by 8 speakers. A recognition rate of 91.36% was achieved by the proposed system.

Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing.

PubMed

Vatsa, Mayank; Singh, Richa; Noore, Afzel

2008-08-01

This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.

Track-based event recognition in a realistic crowded environment

NASA Astrophysics Data System (ADS)

van Huis, Jasper R.; Bouma, Henri; Baan, Jan; Burghouts, Gertjan J.; Eendebak, Pieter T.; den Hollander, Richard J. M.; Dijk, Judith; van Rest, Jeroen H.

2014-10-01

Automatic detection of abnormal behavior in CCTV cameras is important to improve the security in crowded environments, such as shopping malls, airports and railway stations. This behavior can be characterized at different time scales, e.g., by small-scale subtle and obvious actions or by large-scale walking patterns and interactions between people. For example, pickpocketing can be recognized by the actual snatch (small scale), when he follows the victim, or when he interacts with an accomplice before and after the incident (longer time scale). This paper focusses on event recognition by detecting large-scale track-based patterns. Our event recognition method consists of several steps: pedestrian detection, object tracking, track-based feature computation and rule-based event classification. In the experiment, we focused on single track actions (walk, run, loiter, stop, turn) and track interactions (pass, meet, merge, split). The experiment includes a controlled setup, where 10 actors perform these actions. The method is also applied to all tracks that are generated in a crowded shopping mall in a selected time frame. The results show that most of the actions can be detected reliably (on average 90%) at a low false positive rate (1.1%), and that the interactions obtain lower detection rates (70% at 0.3% FP). This method may become one of the components that assists operators to find threatening behavior and enrich the selection of videos that are to be observed.

Exploring 3D Human Action Recognition: from Offline to Online

PubMed Central

Li, Rui; Liu, Zhenyu; Tan, Jianrong

2018-01-01

With the introduction of cost-effective depth sensors, a tremendous amount of research has been devoted to studying human action recognition using 3D motion data. However, most existing methods work in an offline fashion, i.e., they operate on a segmented sequence. There are a few methods specifically designed for online action recognition, which continually predicts action labels as a stream sequence proceeds. In view of this fact, we propose a question: can we draw inspirations and borrow techniques or descriptors from existing offline methods, and then apply these to online action recognition? Note that extending offline techniques or descriptors to online applications is not straightforward, since at least two problems—including real-time performance and sequence segmentation—are usually not considered in offline action recognition. In this paper, we give a positive answer to the question. To develop applicable online action recognition methods, we carefully explore feature extraction, sequence segmentation, computational costs, and classifier selection. The effectiveness of the developed methods is validated on the MSR 3D Online Action dataset and the MSR Daily Activity 3D dataset. PMID:29461502

A new user-assisted segmentation and tracking technique for an object-based video editing system

NASA Astrophysics Data System (ADS)

Yu, Hong Y.; Hong, Sung-Hoon; Lee, Mike M.; Choi, Jae-Gark

2004-03-01

This paper presents a semi-automatic segmentation method which can be used to generate video object plane (VOP) for object based coding scheme and multimedia authoring environment. Semi-automatic segmentation can be considered as a user-assisted segmentation technique. A user can initially mark objects of interest around the object boundaries and then the user-guided and selected objects are continuously separated from the unselected areas through time evolution in the image sequences. The proposed segmentation method consists of two processing steps: partially manual intra-frame segmentation and fully automatic inter-frame segmentation. The intra-frame segmentation incorporates user-assistance to define the meaningful complete visual object of interest to be segmentation and decides precise object boundary. The inter-frame segmentation involves boundary and region tracking to obtain temporal coherence of moving object based on the object boundary information of previous frame. The proposed method shows stable efficient results that could be suitable for many digital video applications such as multimedia contents authoring, content based coding and indexing. Based on these results, we have developed objects based video editing system with several convenient editing functions.

Acquisition of Malay word recognition skills: lessons from low-progress early readers.

PubMed

Lee, Lay Wah; Wheldall, Kevin

2011-02-01

Malay is a consistent alphabetic orthography with complex syllable structures. The focus of this research was to investigate word recognition performance in order to inform reading interventions for low-progress early readers. Forty-six Grade 1 students were sampled and 11 were identified as low-progress readers. The results indicated that both syllable awareness and phoneme blending were significant predictors of word recognition, suggesting that both syllable and phonemic grain-sizes are important in Malay word recognition. Item analysis revealed a hierarchical pattern of difficulty based on the syllable and the phonic structure of the words. Error analysis identified the sources of errors to be errors due to inefficient syllable segmentation, oversimplification of syllables, insufficient grapheme-phoneme knowledge and inefficient phonemic code assembly. Evidence also suggests that direct instruction in syllable segmentation, phonemic awareness and grapheme-phoneme correspondence is necessary for low-progress readers to acquire word recognition skills. Finally, a logical sequence to teach grapheme-phoneme decoding in Malay is suggested. Copyright © 2010 John Wiley & Sons, Ltd.

Robust keyword retrieval method for OCRed text

NASA Astrophysics Data System (ADS)

Fujii, Yusaku; Takebe, Hiroaki; Tanaka, Hiroshi; Hotta, Yoshinobu

2011-01-01

Document management systems have become important because of the growing popularity of electronic filing of documents and scanning of books, magazines, manuals, etc., through a scanner or a digital camera, for storage or reading on a PC or an electronic book. Text information acquired by optical character recognition (OCR) is usually added to the electronic documents for document retrieval. Since texts generated by OCR generally include character recognition errors, robust retrieval methods have been introduced to overcome this problem. In this paper, we propose a retrieval method that is robust against both character segmentation and recognition errors. In the proposed method, the insertion of noise characters and dropping of characters in the keyword retrieval enables robustness against character segmentation errors, and character substitution in the keyword of the recognition candidate for each character in OCR or any other character enables robustness against character recognition errors. The recall rate of the proposed method was 15% higher than that of the conventional method. However, the precision rate was 64% lower.

A knowledge-based object recognition system for applications in the space station

NASA Technical Reports Server (NTRS)

Dhawan, Atam P.

1988-01-01

A knowledge-based three-dimensional (3D) object recognition system is being developed. The system uses primitive-based hierarchical relational and structural matching for the recognition of 3D objects in the two-dimensional (2D) image for interpretation of the 3D scene. At present, the pre-processing, low-level preliminary segmentation, rule-based segmentation, and the feature extraction are completed. The data structure of the primitive viewing knowledge-base (PVKB) is also completed. Algorithms and programs based on attribute-trees matching for decomposing the segmented data into valid primitives were developed. The frame-based structural and relational descriptions of some objects were created and stored in a knowledge-base. This knowledge-base of the frame-based descriptions were developed on the MICROVAX-AI microcomputer in LISP environment. The simulated 3D scene of simple non-overlapping objects as well as real camera data of images of 3D objects of low-complexity have been successfully interpreted.

A Real-Time High Performance Computation Architecture for Multiple Moving Target Tracking Based on Wide-Area Motion Imagery via Cloud and Graphic Processing Units

PubMed Central

Liu, Kui; Wei, Sixiao; Chen, Zhijiang; Jia, Bin; Chen, Genshe; Ling, Haibin; Sheaff, Carolyn; Blasch, Erik

2017-01-01

This paper presents the first attempt at combining Cloud with Graphic Processing Units (GPUs) in a complementary manner within the framework of a real-time high performance computation architecture for the application of detecting and tracking multiple moving targets based on Wide Area Motion Imagery (WAMI). More specifically, the GPU and Cloud Moving Target Tracking (GC-MTT) system applied a front-end web based server to perform the interaction with Hadoop and highly parallelized computation functions based on the Compute Unified Device Architecture (CUDA©). The introduced multiple moving target detection and tracking method can be extended to other applications such as pedestrian tracking, group tracking, and Patterns of Life (PoL) analysis. The cloud and GPUs based computing provides an efficient real-time target recognition and tracking approach as compared to methods when the work flow is applied using only central processing units (CPUs). The simultaneous tracking and recognition results demonstrate that a GC-MTT based approach provides drastically improved tracking with low frame rates over realistic conditions. PMID:28208684

A Real-Time High Performance Computation Architecture for Multiple Moving Target Tracking Based on Wide-Area Motion Imagery via Cloud and Graphic Processing Units.

PubMed

Liu, Kui; Wei, Sixiao; Chen, Zhijiang; Jia, Bin; Chen, Genshe; Ling, Haibin; Sheaff, Carolyn; Blasch, Erik

2017-02-12

This paper presents the first attempt at combining Cloud with Graphic Processing Units (GPUs) in a complementary manner within the framework of a real-time high performance computation architecture for the application of detecting and tracking multiple moving targets based on Wide Area Motion Imagery (WAMI). More specifically, the GPU and Cloud Moving Target Tracking (GC-MTT) system applied a front-end web based server to perform the interaction with Hadoop and highly parallelized computation functions based on the Compute Unified Device Architecture (CUDA©). The introduced multiple moving target detection and tracking method can be extended to other applications such as pedestrian tracking, group tracking, and Patterns of Life (PoL) analysis. The cloud and GPUs based computing provides an efficient real-time target recognition and tracking approach as compared to methods when the work flow is applied using only central processing units (CPUs). The simultaneous tracking and recognition results demonstrate that a GC-MTT based approach provides drastically improved tracking with low frame rates over realistic conditions.

KSC00pp0939

NASA Image and Video Library

2000-07-20

After being involved in a minor derailment incident during a routine movement on the tracks, rail cars carrying solid rocket booster segments sit idle. The rail cars were being moved as part of a standard operation to “order” the cars, placing them into a proper sequence for upcoming segment processing activities. The rear wheels of one car and the front wheels of the car behind it slid off the tracks while passing through a railway switch onto a siding. They were traveling approximately 3 miles per hour at the time, about normal walking speed. No damage occurred to the SRB segments, or to the devices that secure the segments to the rail cars. The incident occurred on KSC property, just north of the NASA Causeway in the KSC Industrial Area

KSC-00pp0939

NASA Image and Video Library

2000-07-20

After being involved in a minor derailment incident during a routine movement on the tracks, rail cars carrying solid rocket booster segments sit idle. The rail cars were being moved as part of a standard operation to “order” the cars, placing them into a proper sequence for upcoming segment processing activities. The rear wheels of one car and the front wheels of the car behind it slid off the tracks while passing through a railway switch onto a siding. They were traveling approximately 3 miles per hour at the time, about normal walking speed. No damage occurred to the SRB segments, or to the devices that secure the segments to the rail cars. The incident occurred on KSC property, just north of the NASA Causeway in the KSC Industrial Area

Fish tracking by combining motion based segmentation and particle filtering

NASA Astrophysics Data System (ADS)

Bichot, E.; Mascarilla, L.; Courtellemont, P.

2006-01-01

In this paper, we suggest a new importance sampling scheme to improve a particle filtering based tracking process. This scheme relies on exploitation of motion segmentation. More precisely, we propagate hypotheses from particle filtering to blobs of similar motion to target. Hence, search is driven toward regions of interest in the state space and prediction is more accurate. We also propose to exploit segmentation to update target model. Once the moving target has been identified, a representative model is learnt from its spatial support. We refer to this model in the correction step of the tracking process. The importance sampling scheme and the strategy to update target model improve the performance of particle filtering in complex situations of occlusions compared to a simple Bootstrap approach as shown by our experiments on real fish tank sequences.

A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

PubMed

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

2016-03-08

On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual con-tours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (< 1 ms) with a satisfying accuracy (Dice = 0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on-board MR-IGRT system.

The 2016 NIST Speaker Recognition Evaluation

DTIC Science & Technology

2017-08-20

The 2016 NIST Speaker Recognition Evaluation Seyed Omid Sadjadi1,∗, Timothée Kheyrkhah1,†, Audrey Tong1, Craig Greenberg1, Douglas Reynolds2, Elliot...recent in an ongoing series of speaker recognition evaluations (SRE) to foster research in ro- bust text-independent speaker recognition, as well as...online evaluation platform, a fixed training data condition, more variability in test segment duration (uni- formly distributed between 10s and 60s

Bayesian source tracking via focalization and marginalization in an uncertain Mediterranean Sea environment.

PubMed

Dosso, Stan E; Wilmut, Michael J; Nielsen, Peter L

2010-07-01

This paper applies Bayesian source tracking in an uncertain environment to Mediterranean Sea data, and investigates the resulting tracks and track uncertainties as a function of data information content (number of data time-segments, number of frequencies, and signal-to-noise ratio) and of prior information (environmental uncertainties and source-velocity constraints). To track low-level sources, acoustic data recorded for multiple time segments (corresponding to multiple source positions along the track) are inverted simultaneously. Environmental uncertainty is addressed by including unknown water-column and seabed properties as nuisance parameters in an augmented inversion. Two approaches are considered: Focalization-tracking maximizes the posterior probability density (PPD) over the unknown source and environmental parameters. Marginalization-tracking integrates the PPD over environmental parameters to obtain a sequence of joint marginal probability distributions over source coordinates, from which the most-probable track and track uncertainties can be extracted. Both approaches apply track constraints on the maximum allowable vertical and radial source velocity. The two approaches are applied for towed-source acoustic data recorded at a vertical line array at a shallow-water test site in the Mediterranean Sea where previous geoacoustic studies have been carried out.

Automatic multiple zebrafish larvae tracking in unconstrained microscopic video conditions.

PubMed

Wang, Xiaoying; Cheng, Eva; Burnett, Ian S; Huang, Yushi; Wlodkowic, Donald

2017-12-14

The accurate tracking of zebrafish larvae movement is fundamental to research in many biomedical, pharmaceutical, and behavioral science applications. However, the locomotive characteristics of zebrafish larvae are significantly different from adult zebrafish, where existing adult zebrafish tracking systems cannot reliably track zebrafish larvae. Further, the far smaller size differentiation between larvae and the container render the detection of water impurities inevitable, which further affects the tracking of zebrafish larvae or require very strict video imaging conditions that typically result in unreliable tracking results for realistic experimental conditions. This paper investigates the adaptation of advanced computer vision segmentation techniques and multiple object tracking algorithms to develop an accurate, efficient and reliable multiple zebrafish larvae tracking system. The proposed system has been tested on a set of single and multiple adult and larvae zebrafish videos in a wide variety of (complex) video conditions, including shadowing, labels, water bubbles and background artifacts. Compared with existing state-of-the-art and commercial multiple organism tracking systems, the proposed system improves the tracking accuracy by up to 31.57% in unconstrained video imaging conditions. To facilitate the evaluation on zebrafish segmentation and tracking research, a dataset with annotated ground truth is also presented. The software is also publicly accessible.

Breast Cancer Diagnostics Based on Spatial Genome Organization

DTIC Science & Technology

2012-07-01

using an already established imaging tool, called NMFA-FLO (Nuclei Manual and FISH automatic). In order to achieve accurate segmentation of nuclei...in tissue we used an artificial neuronal network (ANN)-based supervised pattern recognition approach to screen out well segmented nuclei, after image ... segmentation used to process images for automated nuclear segmentation . Part a) has been adapted from [15] and b) from [16]. Figure 4. Comparison of

Finger Vein Recognition Based on Local Directional Code

PubMed Central

Meng, Xianjing; Yang, Gongping; Yin, Yilong; Xiao, Rongyang

2012-01-01

Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Line Binary Pattern (LLBP). However, the rich directional information hidden in the finger vein pattern has not been fully exploited by the existing local patterns. Inspired by the Webber Local Descriptor (WLD), this paper represents a new direction based local descriptor called Local Directional Code (LDC) and applies it to finger vein recognition. In LDC, the local gradient orientation information is coded as an octonary decimal number. Experimental results show that the proposed method using LDC achieves better performance than methods using LLBP. PMID:23202194

Finger vein recognition based on local directional code.

PubMed

Meng, Xianjing; Yang, Gongping; Yin, Yilong; Xiao, Rongyang

2012-11-05

Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Line Binary Pattern (LLBP). However, the rich directional information hidden in the finger vein pattern has not been fully exploited by the existing local patterns. Inspired by the Webber Local Descriptor (WLD), this paper represents a new direction based local descriptor called Local Directional Code (LDC) and applies it to finger vein recognition. In LDC, the local gradient orientation information is coded as an octonary decimal number. Experimental results show that the proposed method using LDC achieves better performance than methods using LLBP.

Analysis of objects in binary images. M.S. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Leonard, Desiree M.

1991-01-01

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.

Calibration of 3D ultrasound to an electromagnetic tracking system

NASA Astrophysics Data System (ADS)

Lang, Andrew; Parthasarathy, Vijay; Jain, Ameet

2011-03-01

The use of electromagnetic (EM) tracking is an important guidance tool that can be used to aid procedures requiring accurate localization such as needle injections or catheter guidance. Using EM tracking, the information from different modalities can be easily combined using pre-procedural calibration information. These calibrations are performed individually, per modality, allowing different imaging systems to be mixed and matched according to the procedure at hand. In this work, a framework for the calibration of a 3D transesophageal echocardiography probe to EM tracking is developed. The complete calibration framework includes three required steps: data acquisition, needle segmentation, and calibration. Ultrasound (US) images of an EM tracked needle must be acquired with the position of the needles in each volume subsequently extracted by segmentation. The calibration transformation is determined through a registration between the segmented points and the recorded EM needle positions. Additionally, the speed of sound is compensated for since calibration is performed in water that has a different speed then is assumed by the US machine. A statistical validation framework has also been developed to provide further information related to the accuracy and consistency of the calibration. Further validation of the calibration showed an accuracy of 1.39 mm.

Subpixel based defocused points removal in photon-limited volumetric dataset

NASA Astrophysics Data System (ADS)

Muniraj, Inbarasan; Guo, Changliang; Malallah, Ra'ed; Maraka, Harsha Vardhan R.; Ryle, James P.; Sheridan, John T.

2017-03-01

The asymptotic property of the maximum likelihood estimator (MLE) has been utilized to reconstruct three-dimensional (3D) sectional images in the photon counting imaging (PCI) regime. At first, multiple 2D intensity images, known as Elemental images (EI), are captured. Then the geometric ray-tracing method is employed to reconstruct the 3D sectional images at various depth cues. We note that a 3D sectional image consists of both focused and defocused regions, depending on the reconstructed depth position. The defocused portion is redundant and should be removed in order to facilitate image analysis e.g., 3D object tracking, recognition, classification and navigation. In this paper, we present a subpixel level three-step based technique (i.e. involving adaptive thresholding, boundary detection and entropy based segmentation) to discard the defocused sparse-samples from the reconstructed photon-limited 3D sectional images. Simulation results are presented demonstrating the feasibility and efficiency of the proposed method.

Improved document image segmentation algorithm using multiresolution morphology

NASA Astrophysics Data System (ADS)

Bukhari, Syed Saqib; Shafait, Faisal; Breuel, Thomas M.

2011-01-01

Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,1 which is also available in his open-source Leptonica library.2The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets.

Traffic sign recognition by color segmentation and neural network

NASA Astrophysics Data System (ADS)

Surinwarangkoon, Thongchai; Nitsuwat, Supot; Moore, Elvin J.

2011-12-01

An algorithm is proposed for traffic sign detection and identification based on color filtering, color segmentation and neural networks. Traffic signs in Thailand are classified by color into four types: namely, prohibitory signs (red or blue), general warning signs (yellow) and construction area warning signs (amber). A color filtering method is first used to detect traffic signs and classify them by type. Then color segmentation methods adapted for each color type are used to extract inner features, e.g., arrows, bars etc. Finally, neural networks trained to recognize signs in each color type are used to identify any given traffic sign. Experiments show that the algorithm can improve the accuracy of traffic sign detection and recognition for the traffic signs used in Thailand.

Data-Driven Multiresolution Camera Using the Foveal Adaptive Pyramid

PubMed Central

González, Martin; Sánchez-Pedraza, Antonio; Marfil, Rebeca; Rodríguez, Juan A.; Bandera, Antonio

2016-01-01

There exist image processing applications, such as tracking or pattern recognition, that are not necessarily precise enough to maintain the same resolution across the whole image sensor. In fact, they must only keep it as high as possible in a relatively small region, but covering a wide field of view. This is the aim of foveal vision systems. Briefly, they propose to sense a large field of view at a spatially-variant resolution: one relatively small region, the fovea, is mapped at a high resolution, while the rest of the image is captured at a lower resolution. In these systems, this fovea must be moved, from one region of interest to another one, to scan a visual scene. It is interesting that the part of the scene that is covered by the fovea should not be merely spatial, but closely related to perceptual objects. Segmentation and attention are then intimately tied together: while the segmentation process is responsible for extracting perceptively-coherent entities from the scene (proto-objects), attention can guide segmentation. From this loop, the concept of foveal attention arises. This work proposes a hardware system for mapping a uniformly-sampled sensor to a space-variant one. Furthermore, this mapping is tied with a software-based, foveal attention mechanism that takes as input the stream of generated foveal images. The whole hardware/software architecture has been designed to be embedded within an all programmable system on chip (AP SoC). Our results show the flexibility of the data port for exchanging information between the mapping and attention parts of the architecture and the good performance rates of the mapping procedure. Experimental evaluation also demonstrates that the segmentation method and the attention model provide results comparable to other more computationally-expensive algorithms. PMID:27898029

Data-Driven Multiresolution Camera Using the Foveal Adaptive Pyramid.

PubMed

González, Martin; Sánchez-Pedraza, Antonio; Marfil, Rebeca; Rodríguez, Juan A; Bandera, Antonio

2016-11-26

There exist image processing applications, such as tracking or pattern recognition, that are not necessarily precise enough to maintain the same resolution across the whole image sensor. In fact, they must only keep it as high as possible in a relatively small region, but covering a wide field of view. This is the aim of foveal vision systems. Briefly, they propose to sense a large field of view at a spatially-variant resolution: one relatively small region, the fovea, is mapped at a high resolution, while the rest of the image is captured at a lower resolution. In these systems, this fovea must be moved, from one region of interest to another one, to scan a visual scene. It is interesting that the part of the scene that is covered by the fovea should not be merely spatial, but closely related to perceptual objects. Segmentation and attention are then intimately tied together: while the segmentation process is responsible for extracting perceptively-coherent entities from the scene (proto-objects), attention can guide segmentation. From this loop, the concept of foveal attention arises. This work proposes a hardware system for mapping a uniformly-sampled sensor to a space-variant one. Furthermore, this mapping is tied with a software-based, foveal attention mechanism that takes as input the stream of generated foveal images. The whole hardware/software architecture has been designed to be embedded within an all programmable system on chip (AP SoC). Our results show the flexibility of the data port for exchanging information between the mapping and attention parts of the architecture and the good performance rates of the mapping procedure. Experimental evaluation also demonstrates that the segmentation method and the attention model provide results comparable to other more computationally-expensive algorithms.

Layered motion segmentation and depth ordering by tracking edges.

PubMed

Smith, Paul; Drummond, Tom; Cipolla, Roberto

2004-04-01

This paper presents a new Bayesian framework for motion segmentation--dividing a frame from an image sequence into layers representing different moving objects--by tracking edges between frames. Edges are found using the Canny edge detector, and the Expectation-Maximization algorithm is then used to fit motion models to these edges and also to calculate the probabilities of the edges obeying each motion model. The edges are also used to segment the image into regions of similar color. The most likely labeling for these regions is then calculated by using the edge probabilities, in association with a Markov Random Field-style prior. The identification of the relative depth ordering of the different motion layers is also determined, as an integral part of the process. An efficient implementation of this framework is presented for segmenting two motions (foreground and background) using two frames. It is then demonstrated how, by tracking the edges into further frames, the probabilities may be accumulated to provide an even more accurate and robust estimate, and segment an entire sequence. Further extensions are then presented to address the segmentation of more than two motions. Here, a hierarchical method of initializing the Expectation-Maximization algorithm is described, and it is demonstrated that the Minimum Description Length principle may be used to automatically select the best number of motion layers. The results from over 30 sequences (demonstrating both two and three motions) are presented and discussed.

TRIAC II. A MatLab code for track measurements from SSNT detectors

NASA Astrophysics Data System (ADS)

Patiris, D. L.; Blekas, K.; Ioannides, K. G.

2007-08-01

A computer program named TRIAC II written in MATLAB and running with a friendly GUI has been developed for recognition and parameters measurements of particles' tracks from images of Solid State Nuclear Track Detectors. The program, using image analysis tools, counts the number of tracks and depending on the current working mode classifies them according to their radii (Mode I—circular tracks) or their axis (Mode II—elliptical tracks), their mean intensity value (brightness) and their orientation. Images of the detectors' surfaces are input to the code, which generates text files as output, including the number of counted tracks with the associated track parameters. Hough transform techniques are used for the estimation of the number of tracks and their parameters, providing results even in cases of overlapping tracks. Finally, it is possible for the user to obtain informative histograms as well as output files for each image and/or group of images. Program summaryTitle of program:TRIAC II Catalogue identifier:ADZC_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADZC_v1_0 Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer: Pentium III, 600 MHz Installations: MATLAB 7.0 Operating system under which the program has been tested: Windows XP Programming language used:MATLAB Memory required to execute with typical data:256 MB No. of bits in a word:32 No. of processors used:one Has the code been vectorized or parallelized?:no No. of lines in distributed program, including test data, etc.:25 964 No. of bytes in distributed program including test data, etc.: 4 354 510 Distribution format:tar.gz Additional comments: This program requires the MatLab Statistical toolbox and the Image Processing Toolbox to be installed. Nature of physical problem: Following the passage of a charged particle (protons and heavier) through a Solid State Nuclear Track Detector (SSNTD), a damage region is created, usually named latent track. After the chemical etching of the detectors in aqueous NaOH or KOH solutions, latent tracks can be sufficiently enlarged (with diameters of 1 μm or more) to become visible under an optical microscope. Using the appropriate apparatus, one can record images of the SSNTD's surface. The shapes of the particle's tracks are strongly dependent on their charge, energy and the angle of incidence. Generally, they have elliptical shapes and in the special case of vertical incidence, they are circular. The manual counting of tracks is a tedious and time-consuming task. An automatic system is needed to speed up the process and to increase the accuracy of the results. Method of solution: TRIAC II is based on a segmentation method that groups image pixels according to their intensity value (brightness) in a number of grey level groups. After the segmentation of pixels, the program recognizes and separates the track from the background, subsequently performing image morphology, where oversized objects or objects smaller than a threshold value are removed. Finally, using the appropriate Hough transform technique, the program counts the tracks, even those which overlap and classifies them according to their shape parameters and brightness. Typical running time: The analysis of an image with a PC (Intel Pentium III processor running at 600 MHz) requires 2 to 10 minutes, depending on the number of observed tracks and the digital resolution of the image. Unusual features of the program: This program has been tested with images of CR-39 detectors exposed to alpha particles. Also, in low contrast images with few or small tracks, background pixels can be recognized as track pixels. To avoid this problem the brightness of the background pixels should be sufficiently higher than that of the track pixels.

A System for Mailpiece ZIP Code Assignment through Contextual Analysis. Phase 2

DTIC Science & Technology

1991-03-01

Segmentation Address Block Interpretation Automatic Feature Generation Word Recognition Feature Detection Word Verification Optical Character Recognition Directory...in the Phase III effort. 1.1 Motivation The United States Postal Service (USPS) deploys large numbers of optical character recognition (OCR) machines...4):208-218, November 1986. [2] Gronmeyer, L. K., Ruffin, B. W., Lybanon, M. A., Neely, P. L., and Pierce, S. E. An Overview of Optical Character Recognition (OCR

Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data

NASA Astrophysics Data System (ADS)

Gloger, Oliver; Tönnies, Klaus; Mensel, Birger; Völzke, Henry

2015-11-01

In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches.

Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data.

PubMed

Gloger, Oliver; Tönnies, Klaus; Mensel, Birger; Völzke, Henry

2015-11-21

In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches.

Tracking with occlusions via graph cuts.

PubMed

Papadakis, Nicolas; Bugeau, Aurélie

2011-01-01

This work presents a new method for tracking and segmenting along time-interacting objects within an image sequence. One major contribution of the paper is the formalization of the notion of visible and occluded parts. For each object, we aim at tracking these two parts. Assuming that the velocity of each object is driven by a dynamical law, predictions can be used to guide the successive estimations. Separating these predicted areas into good and bad parts with respect to the final segmentation and representing the objects with their visible and occluded parts permit handling partial and complete occlusions. To achieve this tracking, a label is assigned to each object and an energy function representing the multilabel problem is minimized via a graph cuts optimization. This energy contains terms based on image intensities which enable segmenting and regularizing the visible parts of the objects. It also includes terms dedicated to the management of the occluded and disappearing areas, which are defined on the areas of prediction of the objects. The results on several challenging sequences prove the strength of the proposed approach.

Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer.

PubMed

Fida, Benish; Bernabucci, Ivan; Bibbo, Daniele; Conforto, Silvia; Schmid, Maurizio

2015-07-01

Accuracy of systems able to recognize in real time daily living activities heavily depends on the processing step for signal segmentation. So far, windowing approaches are used to segment data and the window size is usually chosen based on previous studies. However, literature is vague on the investigation of its effect on the obtained activity recognition accuracy, if both short and long duration activities are considered. In this work, we present the impact of window size on the recognition of daily living activities, where transitions between different activities are also taken into account. The study was conducted on nine participants who wore a tri-axial accelerometer on their waist and performed some short (sitting, standing, and transitions between activities) and long (walking, stair descending and stair ascending) duration activities. Five different classifiers were tested, and among the different window sizes, it was found that 1.5 s window size represents the best trade-off in recognition among activities, with an obtained accuracy well above 90%. Differences in recognition accuracy for each activity highlight the utility of developing adaptive segmentation criteria, based on the duration of the activities. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

How does horizontal and vertical navigation influence spatial memory of multifloored environments?

PubMed

Thibault, Guillaume; Pasqualotto, Achille; Vidal, Manuel; Droulez, Jacques; Berthoz, Alain

2013-01-01

Although a number of studies have been devoted to 2-D navigation, relatively little is known about how the brain encodes and recalls navigation in complex multifloored environments. Previous studies have proposed that humans preferentially memorize buildings by a set of horizontal 2-D representations. Yet this might stem from the fact that environments were also explored by floors. Here, we have investigated the effect of spatial learning on memory of a virtual multifloored building. Two groups of 28 participants watched a computer movie that showed either a route along floors one at a time or travel between floors by simulated lifts, consisting in both cases of a 2-D trajectory in the vertical plane. To test recognition, the participants viewed a camera movement that either replicated a segment of the learning route (familiar segment) or did not (novel segment-i.e., shortcuts). Overall, floor recognition was not reliably superior to column recognition, but learning along a floor route produced a better spatial memory performance than did learning along a column route. Moreover, the participants processed familiar segments more accurately than novel ones, not only after floor learning, but crucially, also after column learning, suggesting a key role of the observation mode on the exploitation of spatial memory.

Strategies to Improve Activity Recognition Based on Skeletal Tracking: Applying Restrictions Regarding Body Parts and Similarity Boundaries †

PubMed Central

Gutiérrez-López-Franca, Carlos; Hervás, Ramón; Johnson, Esperanza

2018-01-01

This paper aims to improve activity recognition systems based on skeletal tracking through the study of two different strategies (and its combination): (a) specialized body parts analysis and (b) stricter restrictions for the most easily detectable activities. The study was performed using the Extended Body-Angles Algorithm, which is able to analyze activities using only a single key sample. This system allows to select, for each considered activity, which are its relevant joints, which makes it possible to monitor the body of the user selecting only a subset of the same. But this feature of the system has both advantages and disadvantages. As a consequence, in the past we had some difficulties with the recognition of activities that only have a small subset of the joints of the body as relevant. The goal of this work, therefore, is to analyze the effect produced by the application of several strategies on the results of an activity recognition system based on skeletal tracking joint oriented devices. Strategies that we applied with the purpose of improve the recognition rates of the activities with a small subset of relevant joints. Through the results of this work, we aim to give the scientific community some first indications about which considered strategy is better. PMID:29789478

Interaction between a railway track and uniformly moving tandem wheels

NASA Astrophysics Data System (ADS)

Belotserkovskiy, P. M.

2006-12-01

Interaction among loaded wheels via railway track is studied. The vertical parametric oscillations of an infinite row of identical equally spaced wheels, bearing constant load and uniformly moving over a railway track, are calculated by means of Fourier series technique. If the distance between two consecutive wheels is big enough, then one can disregard their interaction via the railway track and consider every wheel as a single one. In this case, however, the Fourier series technique represents an appropriate computation time-saving approximation to a Fourier integral transformation technique that describes the oscillations of a single moving wheel. Two schemes are considered. In the first scheme, every wheel bears the same load. In the second one, consecutive wheels bear contrarily directed loads of the same magnitude. The second scheme leads to simpler calculations and so is recommended to model the wheel-track interaction. The railway track periodicity due to sleeper spacing is taken into account. Each period is the track segment between two adjacent sleepers. A partial differential equation with constant coefficients governs the vertical oscillations of each segment. Boundary conditions bind the oscillations of two neighbour segments and provide periodicity to the track. The shear deformation in the rail cross-section strongly influences the parametric oscillations. It also causes discontinuity of the rail centre-line slope at any point, where a concentrated transverse force is applied. Therefore, Timoshenko beam properties with respect to the topic of this paper are discussed. Interaction between a railway track and a bogie moving at moderate speed is studied. The study points to influence of the bogie frame oscillations on variation in the wheel-rail contact force over the sleeper span. The simplified bogie model considered includes only the primary suspension. A static load applied to the bogie frame centre presents the vehicle body.

Filter Design and Performance Evaluation for Fingerprint Image Segmentation

PubMed Central

Thai, Duy Hoang; Huckemann, Stephan; Gottschlich, Carsten

2016-01-01

Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available. PMID:27171150

Orientation estimation of anatomical structures in medical images for object recognition

NASA Astrophysics Data System (ADS)

Bağci, Ulaş; Udupa, Jayaram K.; Chen, Xinjian

2011-03-01

Recognition of anatomical structures is an important step in model based medical image segmentation. It provides pose estimation of objects and information about "where" roughly the objects are in the image and distinguishing them from other object-like entities. In,1 we presented a general method of model-based multi-object recognition to assist in segmentation (delineation) tasks. It exploits the pose relationship that can be encoded, via the concept of ball scale (b-scale), between the binary training objects and their associated grey images. The goal was to place the model, in a single shot, close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. Unlike position and scale parameters, we observe that orientation parameters require more attention when estimating the pose of the model as even small differences in orientation parameters can lead to inappropriate recognition. Motivated from the non-Euclidean nature of the pose information, we propose in this paper the use of non-Euclidean metrics to estimate orientation of the anatomical structures for more accurate recognition and segmentation. We statistically analyze and evaluate the following metrics for orientation estimation: Euclidean, Log-Euclidean, Root-Euclidean, Procrustes Size-and-Shape, and mean Hermitian metrics. The results show that mean Hermitian and Cholesky decomposition metrics provide more accurate orientation estimates than other Euclidean and non-Euclidean metrics.

An Objective Comparison of Cell Tracking Algorithms

PubMed Central

Ulman, Vladimír; Maška, Martin; Magnusson, Klas E. G.; Ronneberger, Olaf; Haubold, Carsten; Harder, Nathalie; Matula, Pavel; Matula, Petr; Svoboda, David; Radojevic, Miroslav; Smal, Ihor; Rohr, Karl; Jaldén, Joakim; Blau, Helen M.; Dzyubachyk, Oleh; Lelieveldt, Boudewijn; Xiao, Pengdong; Li, Yuexiang; Cho, Siu-Yeung; Dufour, Alexandre C.; Olivo-Marin, Jean-Christophe; Reyes-Aldasoro, Constantino C.; Solis-Lemus, Jose A.; Bensch, Robert; Brox, Thomas; Stegmaier, Johannes; Mikut, Ralf; Wolf, Steffen; Hamprecht, Fred. A.; Esteves, Tiago; Quelhas, Pedro; Demirel, Ömer; Malmström, Lars; Jug, Florian; Tomancak, Pavel; Meijering, Erik; Muñoz-Barrutia, Arrate; Kozubek, Michal; Ortiz-de-Solorzano, Carlos

2017-01-01

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell tracking algorithms. With twenty-one participating algorithms and a data repository consisting of thirteen datasets of various microscopy modalities, the challenge displays today’s state of the art in the field. We analyze the results using performance measures for segmentation and tracking that rank all participating methods. We also analyze the performance of all algorithms in terms of biological measures and their practical usability. Even though some methods score high in all technical aspects, not a single one obtains fully correct solutions. We show that methods that either take prior information into account using learning strategies or analyze cells in a global spatio-temporal video context perform better than other methods under the segmentation and tracking scenarios included in the challenge. PMID:29083403

Polar versus Cartesian velocity models for maneuvering target tracking with IMM

NASA Astrophysics Data System (ADS)

Laneuville, Dann

This paper compares various model sets in different IMM filters for the maneuvering target tracking problem. The aim is to see whether we can improve the tracking performance of what is certainly the most widely used model set in the literature for the maneuvering target tracking problem: a Nearly Constant Velocity model and a Nearly Coordinated Turn model. Our new challenger set consists of a mixed Cartesian position and polar velocity state vector to describe the uniform motion segments and is augmented with the turn rate to obtain the second model for the maneuvering segments. This paper also gives a general procedure to discretize up to second order any non-linear continuous time model with linear diffusion. Comparative simulations on an air defence scenario with a 2D radar, show that this new approach improves significantly the tracking performance in this case.

Volumetric segmentation of range images for printed circuit board inspection

NASA Astrophysics Data System (ADS)

Van Dop, Erik R.; Regtien, Paul P. L.

1996-10-01

Conventional computer vision approaches towards object recognition and pose estimation employ 2D grey-value or color imaging. As a consequence these images contain information about projections of a 3D scene only. The subsequent image processing will then be difficult, because the object coordinates are represented with just image coordinates. Only complicated low-level vision modules like depth from stereo or depth from shading can recover some of the surface geometry of the scene. Recent advances in fast range imaging have however paved the way towards 3D computer vision, since range data of the scene can now be obtained with sufficient accuracy and speed for object recognition and pose estimation purposes. This article proposes the coded-light range-imaging method together with superquadric segmentation to approach this task. Superquadric segments are volumetric primitives that describe global object properties with 5 parameters, which provide the main features for object recognition. Besides, the principle axes of a superquadric segment determine the phase of an object in the scene. The volumetric segmentation of a range image can be used to detect missing, false or badly placed components on assembled printed circuit boards. Furthermore, this approach will be useful to recognize and extract valuable or toxic electronic components on printed circuit boards scrap that currently burden the environment during electronic waste processing. Results on synthetic range images with errors constructed according to a verified noise model illustrate the capabilities of this approach.

Convolutional networks for vehicle track segmentation

NASA Astrophysics Data System (ADS)

Quach, Tu-Thach

2017-10-01

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times of the same scene, rely on simple and fast models to label track pixels. These models, however, are unable to capture natural track features, such as continuity and parallelism. More powerful but computationally expensive models can be used in offline settings. We present an approach that uses dilated convolutional networks consisting of a series of 3×3 convolutions to segment vehicle tracks. The design of our networks considers the fact that remote sensing applications tend to operate in low power and have limited training data. As a result, we aim for small and efficient networks that can be trained end-to-end to learn natural track features entirely from limited training data. We demonstrate that our six-layer network, trained on just 90 images, is computationally efficient and improves the F-score on a standard dataset to 0.992, up from 0.959 obtained by the current state-of-the-art method.

Flexibility in Statistical Word Segmentation: Finding Words in Foreign Speech

ERIC Educational Resources Information Center

Graf Estes, Katharine; Gluck, Stephanie Chen-Wu; Bastos, Carolina

2015-01-01

The present experiments investigated the flexibility of statistical word segmentation. There is ample evidence that infants can use statistical cues (e.g., syllable transitional probabilities) to segment fluent speech. However, it is unclear how effectively infants track these patterns in unfamiliar phonological systems. We examined whether…

Continuous EEG signal analysis for asynchronous BCI application.

PubMed

Hsu, Wei-Yen

2011-08-01

In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.

A Neural-Dynamic Architecture for Concurrent Estimation of Object Pose and Identity

PubMed Central

Lomp, Oliver; Faubel, Christian; Schöner, Gregor

2017-01-01

Handling objects or interacting with a human user about objects on a shared tabletop requires that objects be identified after learning from a small number of views and that object pose be estimated. We present a neurally inspired architecture that learns object instances by storing features extracted from a single view of each object. Input features are color and edge histograms from a localized area that is updated during processing. The system finds the best-matching view for the object in a novel input image while concurrently estimating the object’s pose, aligning the learned view with current input. The system is based on neural dynamics, computationally operating in real time, and can handle dynamic scenes directly off live video input. In a scenario with 30 everyday objects, the system achieves recognition rates of 87.2% from a single training view for each object, while also estimating pose quite precisely. We further demonstrate that the system can track moving objects, and that it can segment the visual array, selecting and recognizing one object while suppressing input from another known object in the immediate vicinity. Evaluation on the COIL-100 dataset, in which objects are depicted from different viewing angles, revealed recognition rates of 91.1% on the first 30 objects, each learned from four training views. PMID:28503145

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar

PubMed Central

Shin, Young Hoon; Seo, Jiwon

2016-01-01

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker’s vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing. PMID:27801867

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar.

PubMed

Shin, Young Hoon; Seo, Jiwon

2016-10-29

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker's vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing.

Simulation and performance of an artificial retina for 40 MHz track reconstruction

DOE PAGES

Abba, A.; Bedeschi, F.; Citterio, M.; ...

2015-03-05

We present the results of a detailed simulation of the artificial retina pattern-recognition algorithm, designed to reconstruct events with hundreds of charged-particle tracks in pixel and silicon detectors at LHCb with LHC crossing frequency of 40 MHz. Performances of the artificial retina algorithm are assessed using the official Monte Carlo samples of the LHCb experiment. We found performances for the retina pattern-recognition algorithm comparable with the full LHCb reconstruction algorithm.

Four-dimensional guidance algorithms for aircraft in an air traffic control environment

NASA Technical Reports Server (NTRS)

Pecsvaradi, T.

1975-01-01

Theoretical development and computer implementation of three guidance algorithms are presented. From a small set of input parameters the algorithms generate the ground track, altitude profile, and speed profile required to implement an experimental 4-D guidance system. Given a sequence of waypoints that define a nominal flight path, the first algorithm generates a realistic, flyable ground track consisting of a sequence of straight line segments and circular arcs. Each circular turn is constrained by the minimum turning radius of the aircraft. The ground track and the specified waypoint altitudes are used as inputs to the second algorithm which generates the altitude profile. The altitude profile consists of piecewise constant flight path angle segments, each segment lying within specified upper and lower bounds. The third algorithm generates a feasible speed profile subject to constraints on the rate of change in speed, permissible speed ranges, and effects of wind. Flight path parameters are then combined into a chronological sequence to form the 4-D guidance vectors. These vectors can be used to drive the autopilot/autothrottle of the aircraft so that a 4-D flight path could be tracked completely automatically; or these vectors may be used to drive the flight director and other cockpit displays, thereby enabling the pilot to track a 4-D flight path manually.

A binary link tracker for the BaBar level 1 trigger system

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

Berenyi, A.; Chen, H.K.; Dao, K.

1999-08-01

The BaBar detector at PEP-II will operate in a high-luminosity e{sup +}e{sup {minus}} collider environment near the {Upsilon}(4S) resonance with the primary goal of studying CP violation in the B meson system. In this environment, typical physics events of interest involve multiple charged particles. These events are identified by counting these tracks in a fast first level (Level 1) trigger system, by reconstructing the tracks in real time. For this purpose, a Binary Link Tracker Module (BLTM) was designed and fabricated for the BaBar Level 1 Drift Chamber trigger system. The BLTM is responsible for linking track segments, constructed bymore » the Track Segment Finder Modules (TSFM), into complete tracks. A single BLTM module processes a 360 MBytes/s stream of segment hit data, corresponding to information from the entire Drift Chamber, and implements a fast and robust algorithm that tolerates high hit occupancies as well as local inefficiencies of the Drift Chamber. The algorithms and the necessary control logic of the BLTM were implemented in Field Programmable Gate Arrays (FPGAs), using the VHDL hardware description language. The finished 9U x 400 mm Euro-format board contains roughly 75,000 gates of programmable logic or about 10,000 lines of VHDL code synthesized into five FPGAs.« less

Research on infrared dim-point target detection and tracking under sea-sky-line complex background

NASA Astrophysics Data System (ADS)

Dong, Yu-xing; Li, Yan; Zhang, Hai-bo

2011-08-01

Target detection and tracking technology in infrared image is an important part of modern military defense system. Infrared dim-point targets detection and recognition under complex background is a difficulty and important strategic value and challenging research topic. The main objects that carrier-borne infrared vigilance system detected are sea-skimming aircrafts and missiles. Due to the characteristics of wide field of view of vigilance system, the target is usually under the sea clutter. Detection and recognition of the target will be taken great difficulties .There are some traditional point target detection algorithms, such as adaptive background prediction detecting method. When background has dispersion-decreasing structure, the traditional target detection algorithms would be more useful. But when the background has large gray gradient, such as sea-sky-line, sea waves etc .The bigger false-alarm rate will be taken in these local area .It could not obtain satisfactory results. Because dim-point target itself does not have obvious geometry or texture feature ,in our opinion , from the perspective of mathematics, the detection of dim-point targets in image is about singular function analysis .And from the perspective image processing analysis , the judgment of isolated singularity in the image is key problem. The foregoing points for dim-point targets detection, its essence is a separation of target and background of different singularity characteristics .The image from infrared sensor usually accompanied by different kinds of noise. These external noises could be caused by the complicated background or from the sensor itself. The noise might affect target detection and tracking. Therefore, the purpose of the image preprocessing is to reduce the effects from noise, also to raise the SNR of image, and to increase the contrast of target and background. According to the low sea-skimming infrared flying small target characteristics , the median filter is used to eliminate noise, improve signal-to-noise ratio, then the multi-point multi-storey vertical Sobel algorithm will be used to detect the sea-sky-line ,so that we can segment sea and sky in the image. Finally using centroid tracking method to capture and trace target. This method has been successfully used to trace target under the sea-sky complex background.

TH-AB-202-03: A Novel Tool for Computing Deliverable Doses in Dynamic MLC Tracking Treatments

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

Fast, M; Kamerling, C; Menten, M

2016-06-15

Purpose: In tracked dynamic multi-leaf collimator (MLC) treatments, segments are continuously adapted to the target centroid motion in beams-eye-view. On-the-fly segment adaptation, however, potentially induces dosimetric errors due to the finite MLC leaf width and non-rigid target motion. In this study, we outline a novel tool for computing the 4d dose of lung SBRT plans delivered with MLC tracking. Methods: The following automated workflow was developed: A) centroid tracking, where the initial segments are morphed to each 4dCT phase based on the beams-eye-view GTV shift (followed by a dose calculation on each phase); B) re-optimized tracking, in which all morphedmore » initial plans from (A) are further optimised (“warm-started”) in each 4dCT phase using the initial optimisation parameters but phase-specific volume definitions. Finally, both dose sets are accumulated to the reference phase using deformable image registration. Initial plans were generated according to the RTOG-1021 guideline (54Gy, 3-Fx, equidistant 9-beam IMRT) on the peak-exhale (reference) phase of a phase-binned 4dCT. Treatment planning and delivery simulations were performed in RayStation (research v4.6) using our in-house segment-morphing algorithm, which directly links to RayStation through a native C++ interface. Results: Computing the tracking plans and 4d dose distributions via the in-house interface takes 5 and 8 minutes respectively for centroid and re-optimized tracking. For a sample lung SBRT patient with 14mm peak-to-peak motion in sup-inf direction, mainly perpendicular leaf motion (0-collimator) resulted in small dose changes for PTV-D95 (−13cGy) and GTV-D98 (+18cGy) for the centroid tracking case compared to the initial plan. Modest reductions of OAR doses (e.g. spinal cord D2: −11cGy) were achieved in the idealized tracking case. Conclusion: This study presents an automated “1-click” workflow for computing deliverable MLC tracking doses in RayStation. Adding a non-deliverable re-optimized tracking scenario is expected to help quantify plan robustness for more challenging patients with anatomy deformations. We acknowledge support of the MLC tracking research from Elekta AB. MFF is supported by Cancer Research UK under Programme C33589/A19908. Research at ICR is also supported by Cancer Research UK under Programme C33589/A19727 and NHS funding to the NIHR Biomedical Research Centre at RMH and ICR.« less

Surface ablation with iris recognition and dynamic rotational eye tracking-based tissue saving treatment with the Technolas 217z excimer laser.

PubMed

Prakash, Gaurav; Agarwal, Amar; Kumar, Dhivya Ashok; Jacob, Soosan; Agarwal, Athiya; Maity, Amrita

2011-03-01

To evaluate the visual and refractive outcomes and expected benefits of Tissue Saving Treatment algorithm-guided surface ablation with iris recognition and dynamic rotational eye tracking. This prospective, interventional case series comprised 122 eyes (70 patients). Pre- and postoperative assessment included uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), refraction, and higher order aberrations. All patients underwent Tissue Saving Treatment algorithm-guided surface ablation with iris recognition and dynamic rotational eye tracking using the Technolas 217z 100-Hz excimer platform (Technolas Perfect Vision GmbH). Follow-up was performed up to 6 months postoperatively. Theoretical benefit analysis was performed to evaluate the algorithm's outcomes compared to others. Preoperative spherocylindrical power was sphere -3.62 ± 1.60 diopters (D) (range: 0 to -6.75 D), cylinder -1.15 ± 1.00 D (range: 0 to -3.50 D), and spherical equivalent -4.19 ± 1.60 D (range: -7.75 to -2.00 D). At 6 months, 91% (111/122) of eyes were within ± 0.50 D of attempted correction. Postoperative UDVA was comparable to preoperative CDVA at 1 month (P=.47) and progressively improved at 6 months (P=.004). Two eyes lost one line of CDVA at 6 months. Theoretical benefit analysis revealed that of 101 eyes with astigmatism, 29 would have had cyclotorsion-induced astigmatism of ≥ 10% if iris recognition and dynamic rotational eye tracking were not used. Furthermore, the mean percentage decrease in maximum depth of ablation by using the Tissue Saving Treatment was 11.8 ± 2.9% over Aspheric, 17.8 ± 6.2% over Personalized, and 18.2 ± 2.8% over Planoscan algorithms. Tissue saving surface ablation with iris recognition and dynamic rotational eye tracking was safe and effective in this series of eyes. Copyright 2011, SLACK Incorporated.

A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

PubMed

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

2016-03-01

On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on-board MR-IGRT system. PACS number(s): 87.57.nm, 87.57.N-, 87.61.Tg. © 2016 The Authors.

A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

PubMed Central

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J.; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa

2016-01-01

On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image‐guided radiotherapy (MR‐IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k‐means (FKM), k‐harmonic means (KHM), and reaction‐diffusion level set evolution (RD‐LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR‐TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR‐TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD‐LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP‐TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high‐contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on‐board MR‐IGRT system. PACS number(s): 87.57.nm, 87.57.N‐, 87.61.Tg

Video Image Tracking Engine

NASA Technical Reports Server (NTRS)

Howard, Richard T. (Inventor); Bryan, ThomasC. (Inventor); Book, Michael L. (Inventor)

2004-01-01

A method and system for processing an image including capturing an image and storing the image as image pixel data. Each image pixel datum is stored in a respective memory location having a corresponding address. Threshold pixel data is selected from the image pixel data and linear spot segments are identified from the threshold pixel data selected.. Ihe positions of only a first pixel and a last pixel for each linear segment are saved. Movement of one or more objects are tracked by comparing the positions of fust and last pixels of a linear segment present in the captured image with respective first and last pixel positions in subsequent captured images. Alternatively, additional data for each linear data segment is saved such as sum of pixels and the weighted sum of pixels i.e., each threshold pixel value is multiplied by that pixel's x-location).

Primary versus secondary achalasia: New signs on barium esophagogram

PubMed Central

Gupta, Pankaj; Debi, Uma; Sinha, Saroj Kant; Prasad, Kaushal Kishor

2015-01-01

Aim: To investigate new signs on barium swallow that can differentiate primary from secondary achalasia. Materials and Methods: Records of 30 patients with primary achalasia and 17 patients with secondary achalasia were reviewed. Clinical, endoscopic, and manometric data was recorded. Barium esophagograms were evaluated for peristalsis and morphology of distal esophageal segment (length, symmetry, nodularity, shouldering, filling defects, and “tram-track sign”). Results: Mean age at presentation was 39 years in primary achalasia and 49 years in secondary achalasia. The mean duration of symptoms was 3.5 years in primary achalasia and 3 months in secondary achalasia. False-negative endoscopic results were noted in the first instance in five patients. In the secondary achalasia group, five patients had distal esophageal segment morphology indistinguishable from that of primary achalasia. None of the patients with primary achalasia and 35% patients with secondary achalasia had a length of the distal segment approaching combined height of two vertebral bodies. None of the patients with secondary achalasia and 34% patients with primary achalasia had maximum caliber of esophagus approaching combined height of two vertebral bodies. Tertiary contractions were noted in 90% patients with primary achalasia and 24% patients with secondary achalasia. Tram-track sign was found in 55% patients with primary achalasia. Filling defects in the distal esophageal segment were noted in 94% patients with secondary achalasia. Conclusion: Length of distal esophageal segment, tertiary contractions, tram-track sign, and filling defects in distal esophageal segment are useful esophagographic features distinguishing primary from secondary achalasia. PMID:26288525

A 4D biomechanical lung phantom for joint segmentation/registration evaluation

NASA Astrophysics Data System (ADS)

Markel, Daniel; Levesque, Ives; Larkin, Joe; Léger, Pierre; El Naqa, Issam

2016-10-01

At present, there exists few openly available methods for evaluation of simultaneous segmentation and registration algorithms. These methods allow for a combination of both techniques to track the tumor in complex settings such as adaptive radiotherapy. We have produced a quality assurance platform for evaluating this specific subset of algorithms using a preserved porcine lung in such that it is multi-modality compatible: positron emission tomography (PET), computer tomography (CT) and magnetic resonance imaging (MRI). A computer controlled respirator was constructed to pneumatically manipulate the lungs in order to replicate human breathing traces. A registration ground truth was provided using an in-house bifurcation tracking pipeline. Segmentation ground truth was provided by synthetic multi-compartment lesions to simulate biologically active tumor, background tissue and a necrotic core. The bifurcation tracking pipeline results were compared to digital deformations and used to evaluate three registration algorithms, Diffeomorphic demons, fast-symmetric forces demons and MiMVista’s deformable registration tool. Three segmentation algorithms the Chan Vese level sets method, a Hybrid technique and the multi-valued level sets algorithm. The respirator was able to replicate three seperate breathing traces with a mean accuracy of 2-2.2%. Bifurcation tracking error was found to be sub-voxel when using human CT data for displacements up to 6.5 cm and approximately 1.5 voxel widths for displacements up to 3.5 cm for the porcine lungs. For the fast-symmetric, diffeomorphic and MiMvista registration algorithms, mean geometric errors were found to be 0.430+/- 0.001 , 0.416+/- 0.001 and 0.605+/- 0.002 voxels widths respectively using the vector field differences and 0.4+/- 0.2 , 0.4+/- 0.2 and 0.6+/- 0.2 voxel widths using the bifurcation tracking pipeline. The proposed phantom was found sufficient for accurate evaluation of registration and segmentation algorithms. The use of automatically generated anatomical landmarks proposed can eliminate the time and potential innacuracy of manual landmark selection using expert observers.

TU-F-17A-03: A 4D Lung Phantom for Coupled Registration/Segmentation Evaluation

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

Markel, D; El Naqa, I; Levesque, I

2014-06-15

Purpose: Coupling the processes of segmentation and registration (regmentation) is a recent development that allows improved efficiency and accuracy for both steps and may improve the clinical feasibility of online adaptive radiotherapy. Presented is a multimodality animal tissue model designed specifically to provide a ground truth to simultaneously evaluate segmentation and registration errors during respiratory motion. Methods: Tumor surrogates were constructed from vacuum sealed hydrated natural sea sponges with catheters used for the injection of PET radiotracer. These contained two compartments allowing for two concentrations of radiotracer mimicking both tumor and background signals. The lungs were inflated to different volumesmore » using an air pump and flow valve and scanned using PET/CT and MRI. Anatomical landmarks were used to evaluate the registration accuracy using an automated bifurcation tracking pipeline for reproducibility. The bifurcation tracking accuracy was assessed using virtual deformations of 2.6 cm, 5.2 cm and 7.8 cm of a CT scan of a corresponding human thorax. Bifurcations were detected in the deformed dataset and compared to known deformation coordinates for 76 points. Results: The bifurcation tracking accuracy was found to have a mean error of −0.94, 0.79 and −0.57 voxels in the left-right, anterior-posterior and inferior-superior axes using a 1×1×5 mm3 resolution after the CT volume was deformed 7.8 cm. The tumor surrogates provided a segmentation ground truth after being registered to the phantom image. Conclusion: A swine lung model in conjunction with vacuum sealed sponges and a bifurcation tracking algorithm is presented that is MRI, PET and CT compatible and anatomically and kinetically realistic. Corresponding software for tracking anatomical landmarks within the phantom shows sub-voxel accuracy. Vacuum sealed sponges provide realistic tumor surrogate with a known boundary. A ground truth with minimal uncertainty is thus realized that can be used for comparing the performance of registration and segmentation algorithms.« less

Robust Indoor Human Activity Recognition Using Wireless Signals.

PubMed

Wang, Yi; Jiang, Xinli; Cao, Rongyu; Wang, Xiyang

2015-07-15

Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

Evaluating structural pattern recognition for handwritten math via primitive label graphs

NASA Astrophysics Data System (ADS)

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

2013-01-01

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

Vigilante: Ultrafast Smart Sensor for Target Recognition and Precision Tracking in a Simulated CMD Scenario

NASA Technical Reports Server (NTRS)

Uldomkesmalee, Suraphol; Suddarth, Steven C.

1997-01-01

VIGILANTE is an ultrafast smart sensor testbed for generic Automatic Target Recognition (ATR) applications with a series of capability demonstration focussed on cruise missile defense (CMD). VIGILANTE's sensor/processor architecture is based on next-generation UV/visible/IR sensors and a tera-operations per second sugar-cube processor, as well as supporting airborne vehicle. Excellent results of efficient ATR methodologies that use an eigenvectors/neural network combination and feature-based precision tracking have been demonstrated in the laboratory environment.

See-What-I-Do: Increasing Mentor and Trainee Sense of Co-Presence in Trauma Surgeries with the STAR Platform

DTIC Science & Technology

2016-04-01

publications, images, and videos.  Technologies or techniques . The technique for one shot gesture recognition is a result from the research activity... shot learning concept for gesture recognition. Name: Aditya Ajay Shanghavi Project Role: Master Student Researcher Identifier (e.g. ORCID ID...use case . The transparency error depends more on the x than the z head tracking error. Head tracking is typically accurate to less than 10mm in x

49 CFR 213.347 - Automotive or railroad crossings at grade.

Code of Federal Regulations, 2010 CFR

2010-10-01

... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION TRACK SAFETY STANDARDS Train Operations at Track Classes 6 and.../barrier system to address the protection of highway traffic and high speed trains. Trains shall not...-approved warning/barrier system exists on that track segment; and (2) All elements of that warning/barrier...

The railroad tracks are being repaired at KSC

NASA Technical Reports Server (NTRS)

2000-01-01

The railroad tracks are under repair at Kennedy Space Center. This section of track is located on KSC property, just north of the NASA Causeway in the KSC Industrial Area. The repairs were required following the minor derailment of two solid rocket booster segment cars on July 18.

75 FR 25927 - Vehicle/Track Interaction Safety Standards; High-Speed and High Cant Deficiency Operations

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-10

... qualification process as an important tool for the assessment of vehicle performance. These simulations are... qualification process, simulations would be conducted using both a measured track geometry segment... on the results of simulation studies designed to identify track geometry irregularities associated...

Real-time auto-adaptive margin generation for MLC-tracked radiotherapy

NASA Astrophysics Data System (ADS)

Glitzner, M.; Fast, M. F.; de Senneville, B. Denis; Nill, S.; Oelfke, U.; Lagendijk, J. J. W.; Raaymakers, B. W.; Crijns, S. P. M.

2017-01-01

In radiotherapy, abdominal and thoracic sites are candidates for performing motion tracking. With real-time control it is possible to adjust the multileaf collimator (MLC) position to the target position. However, positions are not perfectly matched and position errors arise from system delays and complicated response of the electromechanic MLC system. Although, it is possible to compensate parts of these errors by using predictors, residual errors remain and need to be compensated to retain target coverage. This work presents a method to statistically describe tracking errors and to automatically derive a patient-specific, per-segment margin to compensate the arising underdosage on-line, i.e. during plan delivery. The statistics of the geometric error between intended and actual machine position are derived using kernel density estimators. Subsequently a margin is calculated on-line according to a selected coverage parameter, which determines the amount of accepted underdosage. The margin is then applied onto the actual segment to accommodate the positioning errors in the enlarged segment. The proof-of-concept was tested in an on-line tracking experiment and showed the ability to recover underdosages for two test cases, increasing {{V}90 %} in the underdosed area about 47 % and 41 % , respectively. The used dose model was able to predict the loss of dose due to tracking errors and could be used to infer the necessary margins. The implementation had a running time of 23 ms which is compatible with real-time requirements of MLC tracking systems. The auto-adaptivity to machine and patient characteristics makes the technique a generic yet intuitive candidate to avoid underdosages due to MLC tracking errors.

49 CFR 236.1003 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-10-01

... auxiliary or industry tracks, a segment or route of railroad tracks: (1) Of a Class I railroad, as.... Tourist, scenic, historic, or excursion operations as defined in part 238 of this chapter are not...

Model-based extended quaternion Kalman filter to inertial orientation tracking of arbitrary kinematic chains.

PubMed

Szczęsna, Agnieszka; Pruszowski, Przemysław

2016-01-01

Inertial orientation tracking is still an area of active research, especially in the context of out-door, real-time, human motion capture. Existing systems either propose loosely coupled tracking approaches where each segment is considered independently, taking the resulting drawbacks into account, or tightly coupled solutions that are limited to a fixed chain with few segments. Such solutions have no flexibility to change the skeleton structure, are dedicated to a specific set of joints, and have high computational complexity. This paper describes the proposal of a new model-based extended quaternion Kalman filter that allows for estimation of orientation based on outputs from the inertial measurements unit sensors. The filter considers interdependencies resulting from the construction of the kinematic chain so that the orientation estimation is more accurate. The proposed solution is a universal filter that does not predetermine the degree of freedom at the connections between segments of the model. To validation the motion of 3-segments single link pendulum captured by optical motion capture system is used. The next step in the research will be to use this method for inertial motion capture with a human skeleton model.

KSC-2009-5065

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., workers attach the upper segment of the transportation canister to the lower segments around the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

Sex- and Method-Specific Reference Values for Right Ventricular Strain by 2-Dimensional Speckle-Tracking Echocardiography.

PubMed

Muraru, Denisa; Onciul, Sebastian; Peluso, Diletta; Soriani, Nicola; Cucchini, Umberto; Aruta, Patrizia; Romeo, Gabriella; Cavalli, Giacomo; Iliceto, Sabino; Badano, Luigi P

2016-02-01

Despite the fact that assessment of right ventricular longitudinal strain (RVLS) carries important implications for patient diagnosis, prognosis, and treatment, its implementation in clinical settings has been hampered by the limited reference values and the lack of uniformity in software, method, and definition used for measuring RVLS. Accordingly, this study was designed to establish (1) the reference values for RVLS by 2-dimensional speckle-tracking echocardiography; and (2) their relationship with demographic, hemodynamic, and cardiac factors. In 276 healthy volunteers (55% women; age, 18-76 years), free wall and septum RVLS (6 segments) and free wall RVLS (3 segments) using both 6- and 3-segment regions of interest were obtained. Feasibility of 6-segment RVLS was 92%. Free wall RVLS from 3- versus 6-segment regions of interest had similar values, yet 6-segment region of interest was more feasible (86% versus 73%; P<0.001) and reproducible. Reference values (lower limits of normality) were as follows: 6-segment RVLS, -24.7±2.6% (-20.0%) for men and -26.7±3.1% (-20.3%) for women; 3-segment RVLS, -29.3±3.4% (-22.5%) for men and -31.6±4.0% (-23.3%) for women (P<0.001). Free wall RVLS was 5±2 strain units (%) larger in magnitude than 6-segment RVLS, 10±4% larger than septal RVLS, and 2±4% larger in women than in men (P<0.001). At multivariable analysis, age, sex, pulmonary systolic pressure, right atrial minimal volume, as well as right atrial and left ventricular longitudinal strain resulted as correlates of RVLS values. This is the largest study providing sex- and method-specific reference values for RVLS. Our data may foster the implementation of 2-dimensional speckle-tracking echocardiography-derived RV analysis in clinical practice. © 2016 American Heart Association, Inc.

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.

Human region segmentation and description methods for domiciliary healthcare monitoring using chromatic methodology

NASA Astrophysics Data System (ADS)

Al-Temeemy, Ali A.

2018-03-01

A descriptor is proposed for use in domiciliary healthcare monitoring systems. The descriptor is produced from chromatic methodology to extract robust features from the monitoring system's images. It has superior discrimination capabilities, is robust to events that normally disturb monitoring systems, and requires less computational time and storage space to achieve recognition. A method of human region segmentation is also used with this descriptor. The performance of the proposed descriptor was evaluated using experimental data sets, obtained through a series of experiments performed in the Centre for Intelligent Monitoring Systems, University of Liverpool. The evaluation results show high recognition performance for the proposed descriptor in comparison to traditional descriptors, such as moments invariant. The results also show the effectiveness of the proposed segmentation method regarding distortion effects associated with domiciliary healthcare systems.

Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata.

PubMed

Ghanizadeh, Afshin; Abarghouei, Amir Atapour; Sinaie, Saman; Saad, Puteh; Shamsuddin, Siti Mariyam

2011-07-01

Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.

A novel in vivo method for lung segment movement tracking

NASA Astrophysics Data System (ADS)

Leira, H. O.; Tangen, G. A.; Hofstad, E. F.; Langø, T.; Amundsen, T.

2012-02-01

Knowledge about lung movement in health and disease is sparse. Current evaluation methods, such as CT, MRI and external view have significant limitations. To study respiratory movement for image guided tumour diagnostics and respiratory physiology, we needed a method that overcomes these limitations. We fitted balloon catheters with electromagnetic sensors, and placed them in lung lobes of ventilated pigs. The sensors sensed their position at 40 Hz in an electromagnetic tracking field with a precision of ∼0.5 mm. The method was evaluated by recording sensor movement in different body positions and at different tidal volumes. No ‘gold standard’ exists for lung segment tracking, so our results were compared to ‘common knowledge’. The sensors were easily placed, showed no clinically relevant position drift and yielded sub-millimetre accuracy. Our measurements fit ‘common knowledge’, as increased ventilation volume increased respiratory movement, and the right lung moved significantly less in the right than the left lateral position. The novel method for tracking lung segment movements during respiration was easy to implement and yielded high spatial and temporal resolution, and the equipment parts are reusable. It is easy to implement as a research tool for lung physiology, navigated bronchoscopy and radiation therapy.

Non-rigid estimation of cell motion in calcium time-lapse images

NASA Astrophysics Data System (ADS)

Hachi, Siham; Lucumi Moreno, Edinson; Desmet, An-Sofie; Vanden Berghe, Pieter; Fleming, Ronan M. T.

2016-03-01

Calcium imaging is a widely used technique in neuroscience permitting the simultaneous monitoring of electro- physiological activity of hundreds of neurons at single cell resolution. Identification of neuronal activity requires rapid and reliable image analysis techniques, especially when neurons fire and move simultaneously over time. Traditionally, image segmentation is performed to extract individual neurons in the first frame of a calcium sequence. Thereafter, the mean intensity is calculated from the same region of interest in each frame to infer calcium signals. However, when cells move, deform and fire, this segmentation on its own generates artefacts and therefore biased neuronal activity. Therefore, there is a pressing need to develop a more efficient cell tracking technique. We hereby present a novel vision-based cell tracking scheme using a thin-plate spline deformable model. The thin-plate spline warping is based on control points detected using the Fast from Accelerated Segment Test descriptor and tracked using the Lucas-Kanade optical flow. Our method is able to track neurons in calcium time-series, even when there are large changes in intensity, such as during a firing event. The robustness and efficiency of the proposed approach is validated on real calcium time-lapse images of a neuronal population.

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

PubMed Central

Arsalan, Muhammad; Naqvi, Rizwan Ali; Kim, Dong Seop; Nguyen, Phong Ha; Owais, Muhammad; Park, Kang Ryoung

2018-01-01

The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets. PMID:29748495

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors.

PubMed

Arsalan, Muhammad; Naqvi, Rizwan Ali; Kim, Dong Seop; Nguyen, Phong Ha; Owais, Muhammad; Park, Kang Ryoung

2018-05-10

The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets.

KSC-07pd1164

NASA Image and Video Library

2007-05-14

KENNEDY SPACE CENTER, FLA. -- This young alligator approaches the railroad tracks where the train carrying solid rocket booster motor segments is approaching Kennedy Space Center. While enroute, solid rocket motor segments were involved in a derailment in Alabama. The rail cars carrying these segments remained upright and were undamaged. An inspection determined these segment cars could continue on to Florida. The segments themselves will undergo further evaluation at Kennedy before they are cleared for flight. Other segments involved in the derailment will be returned to a plant in Utah for further evaluation. Photo credit: NASA/Kim Shiflett

KSC-07pd1165

NASA Image and Video Library

2007-05-14

KENNEDY SPACE CENTER, FLA. -- This young alligator climbs on the railroad tracks where the train carrying solid rocket booster motor segments is approaching Kennedy Space Center. While enroute, solid rocket motor segments were involved in a derailment in Alabama. The rail cars carrying these segments remained upright and were undamaged. An inspection determined these segment cars could continue on to Florida. The segments themselves will undergo further evaluation at Kennedy before they are cleared for flight. Other segments involved in the derailment will be returned to a plant in Utah for further evaluation. Photo credit: NASA/Kim Shiflett

Speckle-Tracking Layer-Specific Analysis of Myocardial Deformation and Evaluation of Scar Transmurality in Chronic Ischemic Heart Disease.

PubMed

Tarascio, Michela; Leo, Laura Anna; Klersy, Catherine; Murzilli, Romina; Moccetti, Tiziano; Faletra, Francesco Fulvio

2017-07-01

Identification of the extent of scar transmurality in chronic ischemic heart disease is important because it correlates with viability. The aim of this retrospective study was to evaluate whether layer-specific two-dimensional speckle-tracking echocardiography allows distinction of scar presence and transmurality. A total of 70 subjects, 49 with chronic ischemic cardiomyopathy and 21 healthy subjects, underwent two-dimensional speckle-tracking echocardiography and late gadolinium-enhanced cardiac magnetic resonance. Scar extent was determined as the relative amount of hyperenhancement using late gadolinium-enhanced cardiac magnetic resonance in an 18-segment model (0% hyperenhancement = normal; 1%-50% = subendocardial scar; 51%-100% = transmural scar). In the same 18-segment model, peak systolic circumferential strain and longitudinal strain were calculated separately for the endocardial and epicardial layers as well as the full-wall myocardial thickness. All strain parameters showed cutoff values (area under the curve > 0.69) that allowed the discrimination of normal versus scar segments but not of transmural versus subendocardial scars. This was true for all strain parameters analyzed, without differences in efficacy between longitudinal and circumferential strain and subendocardial, subepicardial, and full-wall-thickness strain values. Circumferential and longitudinal strain in normal segments showed transmural and basoapical gradients (greatest values at the subendocardial layer and apex). In segments with scar, transmural gradient was maintained, whereas basoapical gradient was lost because the reduction of strain values in the presence of the scar was greater at the apex. The two-dimensional speckle-tracking echocardiographic values distinguish scar presence but not transmurality; thus, they are not useful predictors of scar segment viability. It remains unclear why there is a greater strain value reduction in the presence of a scar at the apical level. Copyright © 2017 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.

Northeast Artificial Intelligence Consortium Annual Report. Volume 7. 1988 Research in Automated Photointerpretation

DTIC Science & Technology

1989-10-01

weight based on how powerful the corresponding feature is for object recognition and discrimination. For example, consider an arbitrary weight, denoted...quality of the segmentation, how powerful the features and spatial constraints in the knowledge base are (as far as object recognition is concern...that are powerful for object recognition and discrimination. At this point, this selection is performed heuristically through trial-and-error. As a

Combinatorial approaches to gene recognition.

PubMed

Roytberg, M A; Astakhova, T V; Gelfand, M S

1997-01-01

Recognition of genes via exon assembly approaches leads naturally to the use of dynamic programming. We consider the general graph-theoretical formulation of the exon assembly problem and analyze in detail some specific variants: multicriterial optimization in the case of non-linear gene-scoring functions; context-dependent schemes for scoring exons and related procedures for exon filtering; and highly specific recognition of arbitrary gene segments, oligonucleotide probes and polymerase chain reaction (PCR) primers.

Word segmentation in phonemically identical and prosodically different sequences using cochlear implants: A case study.

PubMed

Basirat, Anahita

2017-01-01

Cochlear implant (CI) users frequently achieve good speech understanding based on phoneme and word recognition. However, there is a significant variability between CI users in processing prosody. The aim of this study was to examine the abilities of an excellent CI user to segment continuous speech using intonational cues. A post-lingually deafened adult CI user and 22 normal hearing (NH) subjects segmented phonemically identical and prosodically different sequences in French such as 'l'affiche' (the poster) versus 'la fiche' (the sheet), both [lafiʃ]. All participants also completed a minimal pair discrimination task. Stimuli were presented in auditory-only and audiovisual presentation modalities. The performance of the CI user in the minimal pair discrimination task was 97% in the auditory-only and 100% in the audiovisual condition. In the segmentation task, contrary to the NH participants, the performance of the CI user did not differ from the chance level. Visual speech did not improve word segmentation. This result suggests that word segmentation based on intonational cues is challenging when using CIs even when phoneme/word recognition is very well rehabilitated. This finding points to the importance of the assessment of CI users' skills in prosody processing and the need for specific interventions focusing on this aspect of speech communication.

KSC-04PD-0137

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. Workers ride the rails along with a container enclosing a segment of a solid rocket booster being moved to the main track. Several segments are being returned to Utah for testing. The segments were part of the STS-114 stack. It is the first time actual flight segments that had been stacked for flight in the VAB are being returned for testing. They will undergo firing, which will enable inspectors to check the viability of the solid and verify the life expectancy for stacked segments.

Italians Use Abstract Knowledge about Lexical Stress during Spoken-Word Recognition

ERIC Educational Resources Information Center

Sulpizio, Simone; McQueen, James M.

2012-01-01

In two eye-tracking experiments in Italian, we investigated how acoustic information and stored knowledge about lexical stress are used during the recognition of tri-syllabic spoken words. Experiment 1 showed that Italians use acoustic cues to a word's stress pattern rapidly in word recognition, but only for words with antepenultimate stress.…

The Slow Developmental Time Course of Real-Time Spoken Word Recognition

ERIC Educational Resources Information Center

Rigler, Hannah; Farris-Trimble, Ashley; Greiner, Lea; Walker, Jessica; Tomblin, J. Bruce; McMurray, Bob

2015-01-01

This study investigated the developmental time course of spoken word recognition in older children using eye tracking to assess how the real-time processing dynamics of word recognition change over development. We found that 9-year-olds were slower to activate the target words and showed more early competition from competitor words than…

Practical vision based degraded text recognition system

NASA Astrophysics Data System (ADS)

Mohammad, Khader; Agaian, Sos; Saleh, Hani

2011-02-01

Rapid growth and progress in the medical, industrial, security and technology fields means more and more consideration for the use of camera based optical character recognition (OCR) Applying OCR to scanned documents is quite mature, and there are many commercial and research products available on this topic. These products achieve acceptable recognition accuracy and reasonable processing times especially with trained software, and constrained text characteristics. Even though the application space for OCR is huge, it is quite challenging to design a single system that is capable of performing automatic OCR for text embedded in an image irrespective of the application. Challenges for OCR systems include; images are taken under natural real world conditions, Surface curvature, text orientation, font, size, lighting conditions, and noise. These and many other conditions make it extremely difficult to achieve reasonable character recognition. Performance for conventional OCR systems drops dramatically as the degradation level of the text image quality increases. In this paper, a new recognition method is proposed to recognize solid or dotted line degraded characters. The degraded text string is localized and segmented using a new algorithm. The new method was implemented and tested using a development framework system that is capable of performing OCR on camera captured images. The framework allows parameter tuning of the image-processing algorithm based on a training set of camera-captured text images. Novel methods were used for enhancement, text localization and the segmentation algorithm which enables building a custom system that is capable of performing automatic OCR which can be used for different applications. The developed framework system includes: new image enhancement, filtering, and segmentation techniques which enabled higher recognition accuracies, faster processing time, and lower energy consumption, compared with the best state of the art published techniques. The system successfully produced impressive OCR accuracies (90% -to- 93%) using customized systems generated by our development framework in two industrial OCR applications: water bottle label text recognition and concrete slab plate text recognition. The system was also trained for the Arabic language alphabet, and demonstrated extremely high recognition accuracy (99%) for Arabic license name plate text recognition with processing times of 10 seconds. The accuracy and run times of the system were compared to conventional and many states of art methods, the proposed system shows excellent results.

Success of segmentation in a sequence of images tracking the growth of endogenously fluorescent kidneys

NASA Astrophysics Data System (ADS)

Goldberg, Robert R.; Goldberg, Michael R.

1999-05-01

A previous paper by the authors presented an algorithm that successfully segmented organs grown in vitro from their surroundings. It was noticed that one difficulty in standard dyeing techniques for the analysis of contours in organs was due to the fact that the antigen necessary to bind with the fluorescent dye was not uniform throughout the cell borders. To address these concerns, a new fluorescent technique was utilized. A transgenic mouse line was genetically engineered utilizing the hoxb7/gfp (green fluorescent protein). Whereas the original technique (fixed and blocking) required a numerous number of noise removal filtering and sophisticated segmentation techniques, segmentation on the GFP kidney required only an adaptive binary threshold technique which yielded excellent results without the need for specific noise reduction. This is important for tracking the growth of kidney development through time.

Afar-wide Crustal Strain Field from Multiple InSAR Tracks

NASA Astrophysics Data System (ADS)

Pagli, C.; Wright, T. J.; Wang, H.; Calais, E.; Bennati Rassion, L. S.; Ebinger, C. J.; Lewi, E.

2010-12-01

Onset of a rifting episode in the Dabbahu volcanic segment, Afar (Ethiopia), in 2005 renewed interest in crustal deformation studies in the area. As a consequence, an extensive geodetic data set, including InSAR and GPS measurements have been acquired over Afar and hold great potential towards improving our understanding of the extensional processes that operate during the final stages of continental rupture. The current geodetic observational and modelling strategy has focused on detailed, localised studies of dyke intrusions and eruptions mainly in the Dabbahu segment. However, an eruption in the Erta ‘Ale volcanic segment in 2008, and cluster of earthquakes observed in the Tat Ale segment, are testament to activity elsewhere in Afar. Here we make use of the vast geodetic dataset available to obtain strain information over the whole Afar depression. A systematic analysis of all the volcanic segments, including Dabbahu, Manda-Hararo, Alayta, Tat ‘Ale Erta Ale and the Djibouti deformation zone, is undertaken. We use InSAR data from multiple tracks together with available GPS measurements to obtain a velocity field model for Afar. We use over 300 radar images acquired by the Envisat satellite in both descending and ascending orbits, from 12 distinct tracks in image and wide swath modes, spanning the time period from October 2005 to present time. We obtain the line-of-sight deformation rates from each InSAR track using a network approach and then combine the InSAR velocities with the GPS observations, as suggested by Wright and Wang (2010) following the method of England and Molnar (1997). A mesh is constructed over the Afar area and then we solve for the horizontal and vertical velocities on each node. The resultant full 3D Afar-wide velocity field shows where current strains are being accumulated within the various volcanic segments of Afar, the width of the plate boundary deformation zone and possible connections between distinct volcanic segments on a regional scale. A comparison of crustal strains from the geodetic analysis with the seismicity data will also be made.

The evaluation of single-view and multi-view fusion 3D echocardiography using image-driven segmentation and tracking.

PubMed

Rajpoot, Kashif; Grau, Vicente; Noble, J Alison; Becher, Harald; Szmigielski, Cezary

2011-08-01

Real-time 3D echocardiography (RT3DE) promises a more objective and complete cardiac functional analysis by dynamic 3D image acquisition. Despite several efforts towards automation of left ventricle (LV) segmentation and tracking, these remain challenging research problems due to the poor-quality nature of acquired images usually containing missing anatomical information, speckle noise, and limited field-of-view (FOV). Recently, multi-view fusion 3D echocardiography has been introduced as acquiring multiple conventional single-view RT3DE images with small probe movements and fusing them together after alignment. This concept of multi-view fusion helps to improve image quality and anatomical information and extends the FOV. We now take this work further by comparing single-view and multi-view fused images in a systematic study. In order to better illustrate the differences, this work evaluates image quality and information content of single-view and multi-view fused images using image-driven LV endocardial segmentation and tracking. The image-driven methods were utilized to fully exploit image quality and anatomical information present in the image, thus purposely not including any high-level constraints like prior shape or motion knowledge in the analysis approaches. Experiments show that multi-view fused images are better suited for LV segmentation and tracking, while relatively more failures and errors were observed on single-view images. Copyright © 2011 Elsevier B.V. All rights reserved.

Convolutional networks for vehicle track segmentation

DOE PAGES

Quach, Tu-Thach

2017-08-19

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are unable to capture natural track features such as continuity and parallelism. More powerful, but computationally expensive models can be used in offline settings. We present an approach that uses dilated convolutional networks consisting of a series of 3-by-3 convolutions to segment vehicle tracks. The design of our networks considers the fact that remote sensing applications tend to operate inmore » low power and have limited training data. As a result, we aim for small, efficient networks that can be trained end-to-end to learn natural track features entirely from limited training data. We demonstrate that our 6-layer network, trained on just 90 images, is computationally efficient and improves the F-score on a standard dataset to 0.992, up from 0.959 obtained by the current state-of-the-art method.« less

Convolutional networks for vehicle track segmentation

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

Quach, Tu-Thach

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are unable to capture natural track features such as continuity and parallelism. More powerful, but computationally expensive models can be used in offline settings. We present an approach that uses dilated convolutional networks consisting of a series of 3-by-3 convolutions to segment vehicle tracks. The design of our networks considers the fact that remote sensing applications tend to operate inmore » low power and have limited training data. As a result, we aim for small, efficient networks that can be trained end-to-end to learn natural track features entirely from limited training data. We demonstrate that our 6-layer network, trained on just 90 images, is computationally efficient and improves the F-score on a standard dataset to 0.992, up from 0.959 obtained by the current state-of-the-art method.« less

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.

Assessment of Automated Analyses of Cell Migration on Flat and Nanostructured Surfaces

PubMed Central

Grădinaru, Cristian; Łopacińska, Joanna M.; Huth, Johannes; Kestler, Hans A.; Flyvbjerg, Henrik; Mølhave, Kristian

2012-01-01

Motility studies of cells often rely on computer software that analyzes time-lapse recorded movies and establishes cell trajectories fully automatically. This raises the question of reproducibility of results, since different programs could yield significantly different results of such automated analysis. The fact that the segmentation routines of such programs are often challenged by nanostructured surfaces makes the question more pertinent. Here we illustrate how it is possible to track cells on bright field microscopy images with image analysis routines implemented in an open-source cell tracking program, PACT (Program for Automated Cell Tracking). We compare the automated motility analysis of three cell tracking programs, PACT, Autozell, and TLA, using the same movies as input for all three programs. We find that different programs track overlapping, but different subsets of cells due to different segmentation methods. Unfortunately, population averages based on such different cell populations, differ significantly in some cases. Thus, results obtained with one software package are not necessarily reproducible by other software. PMID:24688640

Recognition of ships for long-term tracking

NASA Astrophysics Data System (ADS)

van den Broek, Sebastiaan P.; Bouma, Henri; Veerman, Henny E. T.; Benoist, Koen W.; den Hollander, Richard J. M.; Schwering, Piet B. W.

2014-06-01

Long-term tracking is important for maritime situational awareness to identify currently observed ships as earlier encounters. In cases of, for example, piracy and smuggling, past location and behavior analysis are useful to determine whether a ship is of interest. Furthermore, it is beneficial to make this assessment with sensors (such as cameras) at a distance, to avoid costs of bringing an own asset closer to the ship for verification. The emphasis of the research presented in this paper, is on the use of several feature extraction and matching methods for recognizing ships from electro-optical imagery within different categories of vessels. We compared central moments, SIFT with localization and SIFT with Fisher Vectors. From the evaluation on imagery of ships, an indication of discriminative power is obtained between and within different categories of ships. This is used to assess the usefulness in persistent tracking, from short intervals (track improvement) to larger intervals (re-identifying ships). The result of this assessment on real data is used in a simulation environment to determine how track continuity is improved. The simulations showed that even limited recognition will improve tracking, connecting both tracks at short intervals as well as over several days.

Target recognitions in multiple-camera closed-circuit television using color constancy

NASA Astrophysics Data System (ADS)

Soori, Umair; Yuen, Peter; Han, Ji Wen; Ibrahim, Izzati; Chen, Wentao; Hong, Kan; Merfort, Christian; James, David; Richardson, Mark

2013-04-01

People tracking in crowded scenes from closed-circuit television (CCTV) footage has been a popular and challenging task in computer vision. Due to the limited spatial resolution in the CCTV footage, the color of people's dress may offer an alternative feature for their recognition and tracking. However, there are many factors, such as variable illumination conditions, viewing angles, and camera calibration, that may induce illusive modification of intrinsic color signatures of the target. Our objective is to recognize and track targets in multiple camera views using color as the detection feature, and to understand if a color constancy (CC) approach may help to reduce these color illusions due to illumination and camera artifacts and thereby improve target recognition performance. We have tested a number of CC algorithms using various color descriptors to assess the efficiency of target recognition from a real multicamera Imagery Library for Intelligent Detection Systems (i-LIDS) data set. Various classifiers have been used for target detection, and the figure of merit to assess the efficiency of target recognition is achieved through the area under the receiver operating characteristics (AUROC). We have proposed two modifications of luminance-based CC algorithms: one with a color transfer mechanism and the other using a pixel-wise sigmoid function for an adaptive dynamic range compression, a method termed enhanced luminance reflectance CC (ELRCC). We found that both algorithms improve the efficiency of target recognitions substantially better than that of the raw data without CC treatment, and in some cases the ELRCC improves target tracking by over 100% within the AUROC assessment metric. The performance of the ELRCC has been assessed over 10 selected targets from three different camera views of the i-LIDS footage, and the averaged target recognition efficiency over all these targets is found to be improved by about 54% in AUROC after the data are processed by the proposed ELRCC algorithm. This amount of improvement represents a reduction of probability of false alarm by about a factor of 5 at the probability of detection of 0.5. Our study concerns mainly the detection of colored targets; and issues for the recognition of white or gray targets will be addressed in a forthcoming study.

Dual Use of Image Based Tracking Techniques: Laser Eye Surgery and Low Vision Prosthesis

NASA Technical Reports Server (NTRS)

Juday, Richard D.; Barton, R. Shane

1994-01-01

With a concentration on Fourier optics pattern recognition, we have developed several methods of tracking objects in dynamic imagery to automate certain space applications such as orbital rendezvous and spacecraft capture, or planetary landing. We are developing two of these techniques for Earth applications in real-time medical image processing. The first is warping of a video image, developed to evoke shift invariance to scale and rotation in correlation pattern recognition. The technology is being applied to compensation for certain field defects in low vision humans. The second is using the optical joint Fourier transform to track the translation of unmodeled scenes. Developed as an image fixation tool to assist in calculating shape from motion, it is being applied to tracking motions of the eyeball quickly enough to keep a laser photocoagulation spot fixed on the retina, thus avoiding collateral damage.

Dual use of image based tracking techniques: Laser eye surgery and low vision prosthesis

NASA Technical Reports Server (NTRS)

Juday, Richard D.

1994-01-01

With a concentration on Fourier optics pattern recognition, we have developed several methods of tracking objects in dynamic imagery to automate certain space applications such as orbital rendezvous and spacecraft capture, or planetary landing. We are developing two of these techniques for Earth applications in real-time medical image processing. The first is warping of a video image, developed to evoke shift invariance to scale and rotation in correlation pattern recognition. The technology is being applied to compensation for certain field defects in low vision humans. The second is using the optical joint Fourier transform to track the translation of unmodeled scenes. Developed as an image fixation tool to assist in calculating shape from motion, it is being applied to tracking motions of the eyeball quickly enough to keep a laser photocoagulation spot fixed on the retina, thus avoiding collateral damage.

ASM Based Synthesis of Handwritten Arabic Text Pages

PubMed Central

Al-Hamadi, Ayoub; Elzobi, Moftah; El-etriby, Sherif; Ghoneim, Ahmed

2015-01-01

Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system that automatically turns Arabic Unicode text into synthetic images of handwritten documents and detailed ground truth. Active Shape Models (ASMs) based on 28046 online samples were used for character synthesis and statistical properties were extracted from the IESK-arDB database to simulate baselines and word slant or skew. In the synthesis step ASM based representations are composed to words and text pages, smoothed by B-Spline interpolation and rendered considering writing speed and pen characteristics. Finally, we use the synthetic data to validate a segmentation method. An experimental comparison with the IESK-arDB database encourages to train and test document analysis related methods on synthetic samples, whenever no sufficient natural ground truthed data is available. PMID:26295059

An Iris Segmentation Algorithm based on Edge Orientation for Off-angle Iris Recognition

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

Karakaya, Mahmut; Barstow, Del R; Santos-Villalobos, Hector J

Iris recognition is known as one of the most accurate and reliable biometrics. However, the accuracy of iris recognition systems depends on the quality of data capture and is negatively affected by several factors such as angle, occlusion, and dilation. In this paper, we present a segmentation algorithm for off-angle iris images that uses edge detection, edge elimination, edge classification, and ellipse fitting techniques. In our approach, we first detect all candidate edges in the iris image by using the canny edge detector; this collection contains edges from the iris and pupil boundaries as well as eyelash, eyelids, iris texturemore » etc. Edge orientation is used to eliminate the edges that cannot be part of the iris or pupil. Then, we classify the remaining edge points into two sets as pupil edges and iris edges. Finally, we randomly generate subsets of iris and pupil edge points, fit ellipses for each subset, select ellipses with similar parameters, and average to form the resultant ellipses. Based on the results from real experiments, the proposed method shows effectiveness in segmentation for off-angle iris images.« less

ASM Based Synthesis of Handwritten Arabic Text Pages.

PubMed

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-Etriby, Sherif; Ghoneim, Ahmed

2015-01-01

Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system that automatically turns Arabic Unicode text into synthetic images of handwritten documents and detailed ground truth. Active Shape Models (ASMs) based on 28046 online samples were used for character synthesis and statistical properties were extracted from the IESK-arDB database to simulate baselines and word slant or skew. In the synthesis step ASM based representations are composed to words and text pages, smoothed by B-Spline interpolation and rendered considering writing speed and pen characteristics. Finally, we use the synthetic data to validate a segmentation method. An experimental comparison with the IESK-arDB database encourages to train and test document analysis related methods on synthetic samples, whenever no sufficient natural ground truthed data is available.

Wheel load cycle tag for rail : final report.

DOT National Transportation Integrated Search

2015-12-01

The Federal Railroad Administration (FRA) has determined that there is a research need to collect and analyze statistical usage : data to help ascertain the cumulative load-induced fatigue on rail track segments. The estimation of rail segment burden...

Real time markerless motion tracking using linked kinematic chains

DOEpatents

Luck, Jason P [Arvada, CO; Small, Daniel E [Albuquerque, NM

2007-08-14

A markerless method is described for tracking the motion of subjects in a three dimensional environment using a model based on linked kinematic chains. The invention is suitable for tracking robotic, animal or human subjects in real-time using a single computer with inexpensive video equipment, and does not require the use of markers or specialized clothing. A simple model of rigid linked segments is constructed of the subject and tracked using three dimensional volumetric data collected by a multiple camera video imaging system. A physics based method is then used to compute forces to align the model with subsequent volumetric data sets in real-time. The method is able to handle occlusion of segments and accommodates joint limits, velocity constraints, and collision constraints and provides for error recovery. The method further provides for elimination of singularities in Jacobian based calculations, which has been problematic in alternative methods.

Hierarchically Structured Non-Intrusive Sign Language Recognition. Chapter 2

NASA Technical Reports Server (NTRS)

Zieren, Jorg; Zieren, Jorg; Kraiss, Karl-Friedrich

2007-01-01

This work presents a hierarchically structured approach at the nonintrusive recognition of sign language from a monocular frontal view. Robustness is achieved through sophisticated localization and tracking methods, including a combined EM/CAMSHIFT overlap resolution procedure and the parallel pursuit of multiple hypotheses about hands position and movement. This allows handling of ambiguities and automatically corrects tracking errors. A biomechanical skeleton model and dynamic motion prediction using Kalman filters represents high level knowledge. Classification is performed by Hidden Markov Models. 152 signs from German sign language were recognized with an accuracy of 97.6%.

Video Segmentation Descriptors for Event Recognition

DTIC Science & Technology

2014-12-08

Velastin, 3D Extended Histogram of Oriented Gradients (3DHOG) for Classification of Road Users in Urban Scenes , BMVC, 2009. [3] M.-Y. Chen and A. Hauptmann...computed on 3D volume outputted by the hierarchical segmentation . Each video is described as follows. Each supertube is temporally divided in n-frame...strength of these descriptors is their adaptability to the scene variations since they are grounded on a video segmentation . This makes them naturally robust

Recognition and characterization of networks of water bodies in the Arctic ice-wedge polygonal tundra using high-resolution satellite imagery

NASA Astrophysics Data System (ADS)

Skurikhin, A. N.; Gangodagamage, C.; Rowland, J. C.; Wilson, C. J.

2013-12-01

Arctic lowland landscapes underlain by permafrost are often characterized by polygon-like patterns such as ice-wedge polygons outlined by networks of ice wedges and complemented with polygon rims, troughs, shallow ponds and thermokarst lakes. Polygonal patterns and corresponding features are relatively easy to recognize in high spatial resolution satellite imagery by a human, but their automated recognition is challenging due to the variability in their spectral appearance, the irregularity of individual trough spacing and orientation within the patterns, and a lack of unique spectral response attributable to troughs with widths commonly between 1 m and 2 m. Accurate identification of fine scale elements of ice-wedge polygonal tundra is important as their imprecise recognition may bias estimates of water, heat and carbon fluxes in large-scale climate models. Our focus is on the problem of identification of Arctic polygonal tundra fine-scale landscape elements (as small as 1 m - 2 m width). The challenge of the considered problem is that while large water bodies (e.g. lakes and rivers) can be recognized based on spectral response, reliable recognition of troughs is more difficult. Troughs do not have unique spectral signature, their appearance is noisy (edges are not strong), their width is small, and they often form connected networks with ponds and lakes, and thus they have overlapping spectral response with other water bodies and surrounding non-water bodies. We present a semi-automated approach to identify and classify Arctic polygonal tundra landscape components across the range of spatial scales, such as troughs, ponds, river- and lake-like objects, using high spatial resolution satellite imagery. The novelty of the approach lies in: (1) the combined use of segmentation and shape-based classification to identify a broad range of water bodies, including troughs, and (2) the use of high-resolution WorldView-2 satellite imagery (with resolution of 0.6 m) for this identification. The approach starts by segmenting water bodies from an image, which are then categorized using shape-based classification. Segmentation uses combination of pan sharpened multispectral bands and is based on the active contours without edges technique. The segmentation is robust to noise and can detect objects with weak boundaries that is important for extraction of troughs. We then categorize the segmented regions via shape based classification. Because segmentation accuracy is the main factor impacting the quality of the shape-based classification, for segmentation accuracy assessment we created reference image using WorldView-2 satellite image of ice-wedge polygonal tundra. Reference image contained manually labelled image regions which cover components of drainage networks, such as troughs, ponds, rivers and lakes. The evaluation has shown that the approach provides a good accuracy of segmentation and reasonable classification results. The overall accuracy of the segmentation is approximately 95%, the segmentation user's and producer's accuracies are approximately 92% and 97% respectively.

KSC-2009-5062

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., the upper segment of the transportation canister is lowered toward the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft. It will be installed onto the lower segments already in place. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

KSC-2009-5063

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., the upper segment of the transportation canister is lowered over the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft. It will be installed onto the lower segments already in place. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

KSC-2009-5064

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., the upper segment of the transportation canister is lowered over the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft. It will be installed onto the lower segments already in place. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

The A2iA French handwriting recognition system at the Rimes-ICDAR2011 competition

NASA Astrophysics Data System (ADS)

Menasri, Farès; Louradour, Jérôme; Bianne-Bernard, Anne-Laure; Kermorvant, Christopher

2012-01-01

This paper describes the system for the recognition of French handwriting submitted by A2iA to the competition organized at ICDAR2011 using the Rimes database. This system is composed of several recognizers based on three different recognition technologies, combined using a novel combination method. A framework multi-word recognition based on weighted finite state transducers is presented, using an explicit word segmentation, a combination of isolated word recognizers and a language model. The system was tested both for isolated word recognition and for multi-word line recognition and submitted to the RIMES-ICDAR2011 competition. This system outperformed all previously proposed systems on these tasks.

Structural evolution of glycan recognition by a family of potent HIV antibodies.

PubMed

Garces, Fernando; Sok, Devin; Kong, Leopold; McBride, Ryan; Kim, Helen J; Saye-Francisco, Karen F; Julien, Jean-Philippe; Hua, Yuanzi; Cupo, Albert; Moore, John P; Paulson, James C; Ward, Andrew B; Burton, Dennis R; Wilson, Ian A

2014-09-25

The HIV envelope glycoprotein (Env) is densely covered with self-glycans that should help shield it from recognition by the human immune system. Here, we examine how a particularly potent family of broadly neutralizing antibodies (Abs) has evolved common and distinct structural features to counter the glycan shield and interact with both glycan and protein components of HIV Env. The inferred germline antibody already harbors potential binding pockets for a glycan and a short protein segment. Affinity maturation then leads to divergent evolutionary branches that either focus on a single glycan and protein segment (e.g., Ab PGT124) or engage multiple glycans (e.g., Abs PGT121-123). Furthermore, other surrounding glycans are avoided by selecting an appropriate initial antibody shape that prevents steric hindrance. Such molecular recognition lessons are important for engineering proteins that can recognize or accommodate glycans. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

Reconstructing the flight kinematics of swarming and mating in wild mosquitoes

PubMed Central

Butail, Sachit; Manoukis, Nicholas; Diallo, Moussa; Ribeiro, José M.; Lehmann, Tovi; Paley, Derek A.

2012-01-01

We describe a novel tracking system for reconstructing three-dimensional tracks of individual mosquitoes in wild swarms and present the results of validating the system by filming swarms and mating events of the malaria mosquito Anopheles gambiae in Mali. The tracking system is designed to address noisy, low frame-rate (25 frames per second) video streams from a stereo camera system. Because flying A. gambiae move at 1–4 m s−1, they appear as faded streaks in the images or sometimes do not appear at all. We provide an adaptive algorithm to search for missing streaks and a likelihood function that uses streak endpoints to extract velocity information. A modified multi-hypothesis tracker probabilistically addresses occlusions and a particle filter estimates the trajectories. The output of the tracking algorithm is a set of track segments with an average length of 0.6–1 s. The segments are verified and combined under human supervision to create individual tracks up to the duration of the video (90 s). We evaluate tracking performance using an established metric for multi-target tracking and validate the accuracy using independent stereo measurements of a single swarm. Three-dimensional reconstructions of A. gambiae swarming and mating events are presented. PMID:22628212

Development of A Two-Stage Procedure for the Automatic Recognition of Dysfluencies in the Speech of Children Who Stutter: I. Psychometric Procedures Appropriate for Selection of Training Material for Lexical Dysfluency Classifiers

PubMed Central

Howell, Peter; Sackin, Stevie; Glenn, Kazan

2007-01-01

This program of work is intended to develop automatic recognition procedures to locate and assess stuttered dysfluencies. This and the following article together, develop and test recognizers for repetitions and prolongations. The automatic recognizers classify the speech in two stages: In the first, the speech is segmented and in the second the segments are categorized. The units that are segmented are words. Here assessments by human judges on the speech of 12 children who stutter are described using a corresponding procedure. The accuracy of word boundary placement across judges, categorization of the words as fluent, repetition or prolongation, and duration of the different fluency categories are reported. These measures allow reliable instances of repetitions and prolongations to be selected for training and assessing the recognizers in the subsequent paper. PMID:9328878

Biomorphic networks: approach to invariant feature extraction and segmentation for ATR

NASA Astrophysics Data System (ADS)

Baek, Andrew; Farhat, Nabil H.

1998-10-01

Invariant features in two dimensional binary images are extracted in a single layer network of locally coupled spiking (pulsating) model neurons with prescribed synapto-dendritic response. The feature vector for an image is represented as invariant structure in the aggregate histogram of interspike intervals obtained by computing time intervals between successive spikes produced from each neuron over a given period of time and combining such intervals from all neurons in the network into a histogram. Simulation results show that the feature vectors are more pattern-specific and invariant under translation, rotation, and change in scale or intensity than achieved in earlier work. We also describe an application of such networks to segmentation of line (edge-enhanced or silhouette) images. The biomorphic spiking network's capabilities in segmentation and invariant feature extraction may prove to be, when they are combined, valuable in Automated Target Recognition (ATR) and other automated object recognition systems.

Pattern Recognition Of Blood Vessel Networks In Ocular Fundus Images

NASA Astrophysics Data System (ADS)

Akita, K.; Kuga, H.

1982-11-01

We propose a computer method of recognizing blood vessel networks in color ocular fundus images which are used in the mass diagnosis of adult diseases such as hypertension and diabetes. A line detection algorithm is applied to extract the blood vessels, and the skeleton patterns of them are made to analyze and describe their structures. The recognition of line segments of arteries and/or veins in the vessel networks consists of three stages. First, a few segments which satisfy a certain constraint are picked up and discriminated as arteries or veins. This is the initial labeling. Then the remaining unknown ones are labeled by utilizing the physical level knowledge. We propose two schemes for this stage : a deterministic labeling and a probabilistic relaxation labeling. Finally the label of each line segment is checked so as to minimize the total number of labeling contradictions. Some experimental results are also presented.

Segmentation and tracking of lung nodules via graph-cuts incorporating shape prior and motion from 4D CT.

PubMed

Cha, Jungwon; Farhangi, Mohammad Mehdi; Dunlap, Neal; Amini, Amir A

2018-01-01

We have developed a robust tool for performing volumetric and temporal analysis of nodules from respiratory gated four-dimensional (4D) CT. The method could prove useful in IMRT of lung cancer. We modified the conventional graph-cuts method by adding an adaptive shape prior as well as motion information within a signed distance function representation to permit more accurate and automated segmentation and tracking of lung nodules in 4D CT data. Active shape models (ASM) with signed distance function were used to capture the shape prior information, preventing unwanted surrounding tissues from becoming part of the segmented object. The optical flow method was used to estimate the local motion and to extend three-dimensional (3D) segmentation to 4D by warping a prior shape model through time. The algorithm has been applied to segmentation of well-circumscribed, vascularized, and juxtapleural lung nodules from respiratory gated CT data. In all cases, 4D segmentation and tracking for five phases of high-resolution CT data took approximately 10 min on a PC workstation with AMD Phenom II and 32 GB of memory. The method was trained based on 500 breath-held 3D CT data from the LIDC data base and was tested on 17 4D lung nodule CT datasets consisting of 85 volumetric frames. The validation tests resulted in an average Dice Similarity Coefficient (DSC) = 0.68 for all test data. An important by-product of the method is quantitative volume measurement from 4D CT from end-inspiration to end-expiration which will also have important diagnostic value. The algorithm performs robust segmentation of lung nodules from 4D CT data. Signed distance ASM provides the shape prior information which based on the iterative graph-cuts framework is adaptively refined to best fit the input data, preventing unwanted surrounding tissue from merging with the segmented object. © 2017 American Association of Physicists in Medicine.

Metric Learning for Hyperspectral Image Segmentation

NASA Technical Reports Server (NTRS)

Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca

2011-01-01

We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

Echogenicity based approach to detect, segment and track the common carotid artery in 2D ultrasound images.

PubMed

Narayan, Nikhil S; Marziliano, Pina

2015-08-01

Automatic detection and segmentation of the common carotid artery in transverse ultrasound (US) images of the thyroid gland play a vital role in the success of US guided intervention procedures. We propose in this paper a novel method to accurately detect, segment and track the carotid in 2D and 2D+t US images of the thyroid gland using concepts based on tissue echogenicity and ultrasound image formation. We first segment the hypoechoic anatomical regions of interest using local phase and energy in the input image. We then make use of a Hessian based blob like analysis to detect the carotid within the segmented hypoechoic regions. The carotid artery is segmented by making use of least squares ellipse fit for the edge points around the detected carotid candidate. Experiments performed on a multivendor dataset of 41 images show that the proposed algorithm can segment the carotid artery with high sensitivity (99.6 ±m 0.2%) and specificity (92.9 ±m 0.1%). Further experiments on a public database containing 971 images of the carotid artery showed that the proposed algorithm can achieve a detection accuracy of 95.2% with a 2% increase in performance when compared to the state-of-the-art method.

Eyes and ears: Using eye tracking and pupillometry to understand challenges to speech recognition.

PubMed

Van Engen, Kristin J; McLaughlin, Drew J

2018-05-04

Although human speech recognition is often experienced as relatively effortless, a number of common challenges can render the task more difficult. Such challenges may originate in talkers (e.g., unfamiliar accents, varying speech styles), the environment (e.g. noise), or in listeners themselves (e.g., hearing loss, aging, different native language backgrounds). Each of these challenges can reduce the intelligibility of spoken language, but even when intelligibility remains high, they can place greater processing demands on listeners. Noisy conditions, for example, can lead to poorer recall for speech, even when it has been correctly understood. Speech intelligibility measures, memory tasks, and subjective reports of listener difficulty all provide critical information about the effects of such challenges on speech recognition. Eye tracking and pupillometry complement these methods by providing objective physiological measures of online cognitive processing during listening. Eye tracking records the moment-to-moment direction of listeners' visual attention, which is closely time-locked to unfolding speech signals, and pupillometry measures the moment-to-moment size of listeners' pupils, which dilate in response to increased cognitive load. In this paper, we review the uses of these two methods for studying challenges to speech recognition. Copyright © 2018. Published by Elsevier B.V.

Human body contour data based activity recognition.

PubMed

Myagmarbayar, Nergui; Yuki, Yoshida; Imamoglu, Nevrez; Gonzalez, Jose; Otake, Mihoko; Yu, Wenwei

2013-01-01

This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate.

Reinforcement learning in computer vision

NASA Astrophysics Data System (ADS)

Bernstein, A. V.; Burnaev, E. V.

2018-04-01

Nowadays, machine learning has become one of the basic technologies used in solving various computer vision tasks such as feature detection, image segmentation, object recognition and tracking. In many applications, various complex systems such as robots are equipped with visual sensors from which they learn state of surrounding environment by solving corresponding computer vision tasks. Solutions of these tasks are used for making decisions about possible future actions. It is not surprising that when solving computer vision tasks we should take into account special aspects of their subsequent application in model-based predictive control. Reinforcement learning is one of modern machine learning technologies in which learning is carried out through interaction with the environment. In recent years, Reinforcement learning has been used both for solving such applied tasks as processing and analysis of visual information, and for solving specific computer vision problems such as filtering, extracting image features, localizing objects in scenes, and many others. The paper describes shortly the Reinforcement learning technology and its use for solving computer vision problems.

A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier

PubMed Central

Jaafar, Haryati; Ibrahim, Salwani; Ramli, Dzati Athiar

2015-01-01

Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%. PMID:26113861

Implicit Processing of Phonotactic Cues: Evidence from Electrophysiological and Vascular Responses

ERIC Educational Resources Information Center

Rossi, Sonja; Jurgenson, Ina B.; Hanulikova, Adriana; Telkemeyer, Silke; Wartenburger, Isabell; Obrig, Hellmuth

2011-01-01

Spoken word recognition is achieved via competition between activated lexical candidates that match the incoming speech input. The competition is modulated by prelexical cues that are important for segmenting the auditory speech stream into linguistic units. One such prelexical cue that listeners rely on in spoken word recognition is phonotactics.…

Got Rhythm...For Better and for Worse. Cross-Modal Effects of Auditory Rhythm on Visual Word Recognition

ERIC Educational Resources Information Center

Brochard, Renaud; Tassin, Maxime; Zagar, Daniel

2013-01-01

The present research aimed to investigate whether, as previously observed with pictures, background auditory rhythm would also influence visual word recognition. In a lexical decision task, participants were presented with bisyllabic visual words, segmented into two successive groups of letters, while an irrelevant strongly metric auditory…

Development of an ultra-portable ride quality meter.

DOT National Transportation Integrated Search

2012-12-01

FRAs Office of Research and Development has funded the development of an ultra-portable ride quality meter (UPRQM) under the Small Business and Innovative Research (SBIR) program. Track inspectors can use the UPRQM to locate segments of track that...

Segment-based acoustic models for continuous speech recognition

NASA Astrophysics Data System (ADS)

Ostendorf, Mari; Rohlicek, J. R.

1993-07-01

This research aims to develop new and more accurate stochastic models for speaker-independent continuous speech recognition, by extending previous work in segment-based modeling and by introducing a new hierarchical approach to representing intra-utterance statistical dependencies. These techniques, which are more costly than traditional approaches because of the large search space associated with higher order models, are made feasible through rescoring a set of HMM-generated N-best sentence hypotheses. We expect these different modeling techniques to result in improved recognition performance over that achieved by current systems, which handle only frame-based observations and assume that these observations are independent given an underlying state sequence. In the fourth quarter of the project, we have completed the following: (1) ported our recognition system to the Wall Street Journal task, a standard task in the ARPA community; (2) developed an initial dependency-tree model of intra-utterance observation correlation; and (3) implemented baseline language model estimation software. Our initial results on the Wall Street Journal task are quite good and represent significantly improved performance over most HMM systems reporting on the Nov. 1992 5k vocabulary test set.

Image Understanding Research

DTIC Science & Technology

1991-02-01

investigating the possibility of parallel cutting out - look at the ears of the giraffe ). The recognition implementation. The data structure of a...0.1, 311 model segments -line fit. tol. -super segments/ giraffe 310 l .4,2,3,4,5,6,8 310 (b) Scene (c) Detected Animals panther 298 1.4,2,3.4,5,6,8 282

Video indexing based on image and sound

NASA Astrophysics Data System (ADS)

Faudemay, Pascal; Montacie, Claude; Caraty, Marie-Jose

1997-10-01

Video indexing is a major challenge for both scientific and economic reasons. Information extraction can sometimes be easier from sound channel than from image channel. We first present a multi-channel and multi-modal query interface, to query sound, image and script through 'pull' and 'push' queries. We then summarize the segmentation phase, which needs information from the image channel. Detection of critical segments is proposed. It should speed-up both automatic and manual indexing. We then present an overview of the information extraction phase. Information can be extracted from the sound channel, through speaker recognition, vocal dictation with unconstrained vocabularies, and script alignment with speech. We present experiment results for these various techniques. Speaker recognition methods were tested on the TIMIT and NTIMIT database. Vocal dictation as experimented on newspaper sentences spoken by several speakers. Script alignment was tested on part of a carton movie, 'Ivanhoe'. For good quality sound segments, error rates are low enough for use in indexing applications. Major issues are the processing of sound segments with noise or music, and performance improvement through the use of appropriate, low-cost architectures or networks of workstations.

A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos.

PubMed

Arbelle, Assaf; Reyes, Jose; Chen, Jia-Yun; Lahav, Galit; Riklin Raviv, Tammy

2018-04-22

We present a novel computational framework for the analysis of high-throughput microscopy videos of living cells. The proposed framework is generally useful and can be applied to different datasets acquired in a variety of laboratory settings. This is accomplished by tying together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. In contrast to most existing approaches, which aim to be general, no assumption of cell shape is made. Spatial, temporal, and cross-sectional variation of the analysed data are accommodated by two key contributions. First, time series analysis is exploited to estimate the temporal cell shape uncertainty in addition to cell trajectory. Second, a fast marching (FM) algorithm is used to integrate the inferred cell properties with the observed image measurements in order to obtain image likelihood for cell segmentation, and association. The proposed approach has been tested on eight different time-lapse microscopy data sets, some of which are high-throughput, demonstrating promising results for the detection, segmentation and association of planar cells. Our results surpass the state of the art for the Fluo-C2DL-MSC data set of the Cell Tracking Challenge (Maška et al., 2014). Copyright © 2018 Elsevier B.V. All rights reserved.

Significance of parametric spectral ratio methods in detection and recognition of whispered speech

NASA Astrophysics Data System (ADS)

Mathur, Arpit; Reddy, Shankar M.; Hegde, Rajesh M.

2012-12-01

In this article the significance of a new parametric spectral ratio method that can be used to detect whispered speech segments within normally phonated speech is described. Adaptation methods based on the maximum likelihood linear regression (MLLR) are then used to realize a mismatched train-test style speech recognition system. This proposed parametric spectral ratio method computes a ratio spectrum of the linear prediction (LP) and the minimum variance distortion-less response (MVDR) methods. The smoothed ratio spectrum is then used to detect whispered segments of speech within neutral speech segments effectively. The proposed LP-MVDR ratio method exhibits robustness at different SNRs as indicated by the whisper diarization experiments conducted on the CHAINS and the cell phone whispered speech corpus. The proposed method also performs reasonably better than the conventional methods for whisper detection. In order to integrate the proposed whisper detection method into a conventional speech recognition engine with minimal changes, adaptation methods based on the MLLR are used herein. The hidden Markov models corresponding to neutral mode speech are adapted to the whispered mode speech data in the whispered regions as detected by the proposed ratio method. The performance of this method is first evaluated on whispered speech data from the CHAINS corpus. The second set of experiments are conducted on the cell phone corpus of whispered speech. This corpus is collected using a set up that is used commercially for handling public transactions. The proposed whisper speech recognition system exhibits reasonably better performance when compared to several conventional methods. The results shown indicate the possibility of a whispered speech recognition system for cell phone based transactions.

Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen; Liu, Yuan; Liang, Fuxun; Wang, Yongjun

2017-04-01

In recent years, updating the inventory of road infrastructures based on field work is labor intensive, time consuming, and costly. Fortunately, vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. However, robust recognition of road facilities from huge volumes of 3D point clouds is still a challenging issue because of complicated and incomplete structures, occlusions and varied point densities. Most existing methods utilize point or object based features to recognize object candidates, and can only extract limited types of objects with a relatively low recognition rate, especially for incomplete and small objects. To overcome these drawbacks, this paper proposes a semantic labeling framework by combing multiple aggregation levels (point-segment-object) of features and contextual features to recognize road facilities, such as road surfaces, road boundaries, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, and cars, for highway infrastructure inventory. The proposed method first identifies ground and non-ground points, and extracts road surfaces facilities from ground points. Non-ground points are segmented into individual candidate objects based on the proposed multi-rule region growing method. Then, the multiple aggregation levels of features and the contextual features (relative positions, relative directions, and spatial patterns) associated with each candidate object are calculated and fed into a SVM classifier to label the corresponding candidate object. The recognition performance of combining multiple aggregation levels and contextual features was compared with single level (point, segment, or object) based features using large-scale highway scene point clouds. Comparative studies demonstrated that the proposed semantic labeling framework significantly improves road facilities recognition precision (90.6%) and recall (91.2%), particularly for incomplete and small objects.

KSC-04PD-0136

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. An engine pulls the container enclosing a segment of a solid rocket booster from the Rotation Processing and Surge Facility. The container will join others on the main track for a trip to Utah where the segments will undergo firing. The segments were part of the STS-114 stack. It is the first time actual flight segments that had been stacked for flight in the VAB are being returned for testing. They will undergo firing, which will enable inspectors to check the viability of the solid and verify the life expectancy for stacked segments.

Automatic measurement and representation of prosodic features

NASA Astrophysics Data System (ADS)

Ying, Goangshiuan Shawn

Effective measurement and representation of prosodic features of the acoustic signal for use in automatic speech recognition and understanding systems is the goal of this work. Prosodic features-stress, duration, and intonation-are variations of the acoustic signal whose domains are beyond the boundaries of each individual phonetic segment. Listeners perceive prosodic features through a complex combination of acoustic correlates such as intensity, duration, and fundamental frequency (F0). We have developed new tools to measure F0 and intensity features. We apply a probabilistic global error correction routine to an Average Magnitude Difference Function (AMDF) pitch detector. A new short-term frequency-domain Teager energy algorithm is used to measure the energy of a speech signal. We have conducted a series of experiments performing lexical stress detection on words in continuous English speech from two speech corpora. We have experimented with two different approaches, a segment-based approach and a rhythm unit-based approach, in lexical stress detection. The first approach uses pattern recognition with energy- and duration-based measurements as features to build Bayesian classifiers to detect the stress level of a vowel segment. In the second approach we define rhythm unit and use only the F0-based measurement and a scoring system to determine the stressed segment in the rhythm unit. A duration-based segmentation routine was developed to break polysyllabic words into rhythm units. The long-term goal of this work is to develop a system that can effectively detect the stress pattern for each word in continuous speech utterances. Stress information will be integrated as a constraint for pruning the word hypotheses in a word recognition system based on hidden Markov models.

Automated recognition and tracking of aerosol threat plumes with an IR camera pod

NASA Astrophysics Data System (ADS)

Fauth, Ryan; Powell, Christopher; Gruber, Thomas; Clapp, Dan

2012-06-01

Protection of fixed sites from chemical, biological, or radiological aerosol plume attacks depends on early warning so that there is time to take mitigating actions. Early warning requires continuous, autonomous, and rapid coverage of large surrounding areas; however, this must be done at an affordable cost. Once a potential threat plume is detected though, a different type of sensor (e.g., a more expensive, slower sensor) may be cued for identification purposes, but the problem is to quickly identify all of the potential threats around the fixed site of interest. To address this problem of low cost, persistent, wide area surveillance, an IR camera pod and multi-image stitching and processing algorithms have been developed for automatic recognition and tracking of aerosol plumes. A rugged, modular, static pod design, which accommodates as many as four micro-bolometer IR cameras for 45deg to 180deg of azimuth coverage, is presented. Various OpenCV1 based image-processing algorithms, including stitching of multiple adjacent FOVs, recognition of aerosol plume objects, and the tracking of aerosol plumes, are presented using process block diagrams and sample field test results, including chemical and biological simulant plumes. Methods for dealing with the background removal, brightness equalization between images, and focus quality for optimal plume tracking are also discussed.

A vision-based approach for tramway rail extraction

NASA Astrophysics Data System (ADS)

Zwemer, Matthijs H.; van de Wouw, Dennis W. J. M.; Jaspers, Egbert; Zinger, Sveta; de With, Peter H. N.

2015-03-01

The growing traffic density in cities fuels the desire for collision assessment systems on public transportation. For this application, video analysis is broadly accepted as a cornerstone. For trams, the localization of tramway tracks is an essential ingredient of such a system, in order to estimate a safety margin for crossing traffic participants. Tramway-track detection is a challenging task due to the urban environment with clutter, sharp curves and occlusions of the track. In this paper, we present a novel and generic system to detect the tramway track in advance of the tram position. The system incorporates an inverse perspective mapping and a-priori geometry knowledge of the rails to find possible track segments. The contribution of this paper involves the creation of a new track reconstruction algorithm which is based on graph theory. To this end, we define track segments as vertices in a graph, in which edges represent feasible connections. This graph is then converted to a max-cost arborescence graph, and the best path is selected according to its location and additional temporal information based on a maximum a-posteriori estimate. The proposed system clearly outperforms a railway-track detector. Furthermore, the system performance is validated on 3,600 manually annotated frames. The obtained results are promising, where straight tracks are found in more than 90% of the images and complete curves are still detected in 35% of the cases.

Event Recognition Based on Deep Learning in Chinese Texts

PubMed Central

Zhang, Yajun; Liu, Zongtian; Zhou, Wen

2016-01-01

Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%. PMID:27501231

Event Recognition Based on Deep Learning in Chinese Texts.

PubMed

Zhang, Yajun; Liu, Zongtian; Zhou, Wen

2016-01-01

Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.

Metric Learning to Enhance Hyperspectral Image Segmentation

NASA Technical Reports Server (NTRS)

Thompson, David R.; Castano, Rebecca; Bue, Brian; Gilmore, Martha S.

2013-01-01

Unsupervised hyperspectral image segmentation can reveal spatial trends that show the physical structure of the scene to an analyst. They highlight borders and reveal areas of homogeneity and change. Segmentations are independently helpful for object recognition, and assist with automated production of symbolic maps. Additionally, a good segmentation can dramatically reduce the number of effective spectra in an image, enabling analyses that would otherwise be computationally prohibitive. Specifically, using an over-segmentation of the image instead of individual pixels can reduce noise and potentially improve the results of statistical post-analysis. In this innovation, a metric learning approach is presented to improve the performance of unsupervised hyperspectral image segmentation. The prototype demonstrations attempt a superpixel segmentation in which the image is conservatively over-segmented; that is, the single surface features may be split into multiple segments, but each individual segment, or superpixel, is ensured to have homogenous mineralogy.

Real-time Awake Animal Motion Tracking System for SPECT Imaging

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

Goddard Jr, James Samuel; Baba, Justin S; Lee, Seung Joon

Enhancements have been made in the development of a real-time optical pose measurement and tracking system that provides 3D position and orientation data for a single photon emission computed tomography (SPECT) imaging system for awake, unanesthetized, unrestrained small animals. Three optical cameras with infrared (IR) illumination view the head movements of an animal enclosed in a transparent burrow. Markers placed on the head provide landmark points for image segmentation. Strobed IR LED s are synchronized to the cameras and illuminate the markers to prevent motion blur for each set of images. The system using the three cameras automatically segments themore » markers, detects missing data, rejects false reflections, performs trinocular marker correspondence, and calculates the 3D pose of the animal s head. Improvements have been made in methods for segmentation, tracking, and 3D calculation to give higher speed and more accurate measurements during a scan. The optical hardware has been installed within a Siemens MicroCAT II small animal scanner at Johns Hopkins without requiring functional changes to the scanner operation. The system has undergone testing using both phantoms and live mice and has been characterized in terms of speed, accuracy, robustness, and reliability. Experimental data showing these motion tracking results are given.« less

Application of maximum entropy to statistical inference for inversion of data from a single track segment.

PubMed

Stotts, Steven A; Koch, Robert A

2017-08-01

In this paper an approach is presented to estimate the constraint required to apply maximum entropy (ME) for statistical inference with underwater acoustic data from a single track segment. Previous algorithms for estimating the ME constraint require multiple source track segments to determine the constraint. The approach is relevant for addressing model mismatch effects, i.e., inaccuracies in parameter values determined from inversions because the propagation model does not account for all acoustic processes that contribute to the measured data. One effect of model mismatch is that the lowest cost inversion solution may be well outside a relatively well-known parameter value's uncertainty interval (prior), e.g., source speed from track reconstruction or towed source levels. The approach requires, for some particular parameter value, the ME constraint to produce an inferred uncertainty interval that encompasses the prior. Motivating this approach is the hypothesis that the proposed constraint determination procedure would produce a posterior probability density that accounts for the effect of model mismatch on inferred values of other inversion parameters for which the priors might be quite broad. Applications to both measured and simulated data are presented for model mismatch that produces minimum cost solutions either inside or outside some priors.

An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture.

PubMed

Doulamis, A; Doulamis, N; Ntalianis, K; Kollias, S

2003-01-01

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).

Advances to the development of a basic Mexican sign-to-speech and text language translator

NASA Astrophysics Data System (ADS)

Garcia-Bautista, G.; Trujillo-Romero, F.; Diaz-Gonzalez, G.

2016-09-01

Sign Language (SL) is the basic alternative communication method between deaf people. However, most of the hearing people have trouble understanding the SL, making communication with deaf people almost impossible and taking them apart from daily activities. In this work we present an automatic basic real-time sign language translator capable of recognize a basic list of Mexican Sign Language (MSL) signs of 10 meaningful words, letters (A-Z) and numbers (1-10) and translate them into speech and text. The signs were collected from a group of 35 MSL signers executed in front of a Microsoft Kinect™ Sensor. The hand gesture recognition system use the RGB-D camera to build and storage data point clouds, color and skeleton tracking information. In this work we propose a method to obtain the representative hand trajectory pattern information. We use Euclidean Segmentation method to obtain the hand shape and Hierarchical Centroid as feature extraction method for images of numbers and letters. A pattern recognition method based on a Back Propagation Artificial Neural Network (ANN) is used to interpret the hand gestures. Finally, we use K-Fold Cross Validation method for training and testing stages. Our results achieve an accuracy of 95.71% on words, 98.57% on numbers and 79.71% on letters. In addition, an interactive user interface was designed to present the results in voice and text format.

Structural basis underlying CAC RNA recognition by the RRM domain of dimeric RNA-binding protein RBPMS

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

Teplova, Marianna; Farazi, Thalia A.; Tuschl, Thomas

Abstract RNA-binding protein with multiple splicing (designated RBPMS) is a higher vertebrate mRNA-binding protein containing a single RNA recognition motif (RRM). RBPMS has been shown to be involved in mRNA transport, localization and stability, with key roles in axon guidance, smooth muscle plasticity, as well as regulation of cancer cell proliferation and migration. We report on structure-function studies of the RRM domain of RBPMS bound to a CAC-containing single-stranded RNA. These results provide insights into potential topologies of complexes formed by the RBPMS RRM domain and the tandem CAC repeat binding sites as detected by photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation. Thesemore » studies establish that the RRM domain of RBPMS forms a symmetrical dimer in the free state, with each monomer binding sequence-specifically to all three nucleotides of a CAC segment in the RNA bound state. Structure-guided mutations within the dimerization and RNA-binding interfaces of RBPMS RRM on RNA complex formation resulted in both disruption of dimerization and a decrease in RNA-binding affinity as observed by size exclusion chromatography and isothermal titration calorimetry. As anticipated from biochemical binding studies, over-expression of dimerization or RNA-binding mutants of Flag-HA-tagged RBPMS were no longer able to track with stress granules in HEK293 cells, thereby documenting the deleterious effects of such mutationsin vivo.« less

Recognition of Modified Conditioning Sounds by Competitively Trained Guinea Pigs

PubMed Central

Ojima, Hisayuki; Horikawa, Junsei

2016-01-01

The guinea pig (GP) is an often-used species in hearing research. However, behavioral studies are rare, especially in the context of sound recognition, because of difficulties in training these animals. We examined sound recognition in a social competitive setting in order to examine whether this setting could be used as an easy model. Two starved GPs were placed in the same training arena and compelled to compete for food after hearing a conditioning sound (CS), which was a repeat of almost identical sound segments. Through a 2-week intensive training, animals were trained to demonstrate a set of distinct behaviors solely to the CS. Then, each of them was subjected to generalization tests for recognition of sounds that had been modified from the CS in spectral, fine temporal and tempo (i.e., intersegment interval, ISI) dimensions. Results showed that they discriminated between the CS and band-rejected test sounds but had no preference for a particular frequency range for the recognition. In contrast, sounds modified in the fine temporal domain were largely perceived to be in the same category as the CS, except for the test sound generated by fully reversing the CS in time. Animals also discriminated sounds played at different tempos. Test sounds with ISIs shorter than that of the multi-segment CS were discriminated from the CS, while test sounds with ISIs longer than that of the CS segments were not. For the shorter ISIs, most animals initiated apparently positive food-access behavior as they did in response to the CS, but discontinued it during the sound-on period probably because of later recognition of tempo. Interestingly, the population range and mean of the delay time before animals initiated the food-access behavior were very similar among different ISI test sounds. This study, for the first time, demonstrates a wide aspect of sound discrimination abilities of the GP and will provide a way to examine tempo perception mechanisms using this animal species. PMID:26858617

Neural network face recognition using wavelets

NASA Astrophysics Data System (ADS)

Karunaratne, Passant V.; Jouny, Ismail I.

1997-04-01

The recognition of human faces is a phenomenon that has been mastered by the human visual system and that has been researched extensively in the domain of computer neural networks and image processing. This research is involved in the study of neural networks and wavelet image processing techniques in the application of human face recognition. The objective of the system is to acquire a digitized still image of a human face, carry out pre-processing on the image as required, an then, given a prior database of images of possible individuals, be able to recognize the individual in the image. The pre-processing segment of the system includes several procedures, namely image compression, denoising, and feature extraction. The image processing is carried out using Daubechies wavelets. Once the images have been passed through the wavelet-based image processor they can be efficiently analyzed by means of a neural network. A back- propagation neural network is used for the recognition segment of the system. The main constraints of the system is with regard to the characteristics of the images being processed. The system should be able to carry out effective recognition of the human faces irrespective of the individual's facial-expression, presence of extraneous objects such as head-gear or spectacles, and face/head orientation. A potential application of this face recognition system would be as a secondary verification method in an automated teller machine.

Youth Attitude Tracking Study: Spring 1980.

DTIC Science & Technology

1980-08-01

integral part of service adver- tising, especially for the Marine Corps. Slogans always have been an effective means of generating and sustaining brand ... awareness . Tracking the recognition of service advertising slogans, therefore, is another means of assessing the effective- ness of service advertising

Finding Acoustic Regularities in Speech: Applications to Phonetic Recognition

DTIC Science & Technology

1988-12-01

University Press, Indiana, I 1977. [12] N. Chomsky and M. Halle, The Sound Patterns of English, Harper and Row, New York, 1968. l 129 I BIBLIOGRAPHY [13] Y.L...segments are related to the phonemes by a grammar which is determined using. automated procedures operating on a set of training data. Thus important...segments which are described completely in acoustic terms. Next, these acous- tic segments are related to the phonemes by a grammar which is determined

Binocular Vision-Based Position and Pose of Hand Detection and Tracking in Space

NASA Astrophysics Data System (ADS)

Jun, Chen; Wenjun, Hou; Qing, Sheng

After the study of image segmentation, CamShift target tracking algorithm and stereo vision model of space, an improved algorithm based of Frames Difference and a new space point positioning model were proposed, a binocular visual motion tracking system was constructed to verify the improved algorithm and the new model. The problem of the spatial location and pose of the hand detection and tracking have been solved.

Distributed Fusion in Sensor Networks with Information Genealogy

DTIC Science & Technology

2011-06-28

image processing [2], acoustic and speech recognition [3], multitarget tracking [4], distributed fusion [5], and Bayesian inference [6-7]. For...Adaptation for Distant-Talking Speech Recognition." in Proc Acoustics. Speech , and Signal Processing, 2004 |4| Y Bar-Shalom and T 1-. Fortmann...used in speech recognition and other classification applications [8]. But their use in underwater mine classification is limited. In this paper, we

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.

Perioperative strategy in colonic surgery; LAparoscopy and/or FAst track multimodal management versus standard care (LAFA trial)

PubMed Central

Wind, Jan; Hofland, Jan; Preckel, Benedikt; Hollmann, Markus W; Bossuyt, Patrick MM; Gouma, Dirk J; van Berge Henegouwen, Mark I; Fuhring, Jan Willem; Dejong, Cornelis HC; van Dam, Ronald M; Cuesta, Miguel A; Noordhuis, Astrid; de Jong, Dick; van Zalingen, Edith; Engel, Alexander F; Goei, T Hauwy; de Stoppelaar, I Erica; van Tets, Willem F; van Wagensveld, Bart A; Swart, Annemiek; van den Elsen, Maarten JLJ; Gerhards, Michael F; de Wit, Laurens Th; Siepel, Muriel AM; van Geloven, Anna AW; Juttmann, Jan-Willem; Clevers, Wilfred; Bemelman, Willem A

2006-01-01

Background Recent developments in large bowel surgery are the introduction of laparoscopic surgery and the implementation of multimodal fast track recovery programs. Both focus on a faster recovery and shorter hospital stay. The randomized controlled multicenter LAFA-trial (LAparoscopy and/or FAst track multimodal management versus standard care) was conceived to determine whether laparoscopic surgery, fast track perioperative care or a combination of both is to be preferred over open surgery with standard care in patients having segmental colectomy for malignant disease. Methods/design The LAFA-trial is a double blinded, multicenter trial with a 2 × 2 balanced factorial design. Patients eligible for segmental colectomy for malignant colorectal disease i.e. right and left colectomy and anterior resection will be randomized to either open or laparoscopic colectomy, and to either standard care or the fast track program. This factorial design produces four treatment groups; open colectomy with standard care (a), open colectomy with fast track program (b), laparoscopic colectomy with standard care (c), and laparoscopic surgery with fast track program (d). Primary outcome parameter is postoperative hospital length of stay including readmission within 30 days. Secondary outcome parameters are quality of life two and four weeks after surgery, overall hospital costs, morbidity, patient satisfaction and readmission rate. Based on a mean postoperative hospital stay of 9 +/- 2.5 days a group size of 400 patients (100 each arm) can reliably detect a minimum difference of 1 day between the four arms (alfa = 0.95, beta = 0.8). With 100 patients in each arm a difference of 10% in subscales of the Short Form 36 (SF-36) questionnaire and social functioning can be detected. Discussion The LAFA-trial is a randomized controlled multicenter trial that will provide evidence on the merits of fast track perioperative care and laparoscopic colorectal surgery in patients having segmental colectomy for malignant disease. PMID:17134506

Automated detection of videotaped neonatal seizures of epileptic origin.

PubMed

Karayiannis, Nicolaos B; Xiong, Yaohua; Tao, Guozhi; Frost, James D; Wise, Merrill S; Hrachovy, Richard A; Mizrahi, Eli M

2006-06-01

This study aimed at the development of a seizure-detection system by training neural networks with quantitative motion information extracted from short video segments of neonatal seizures of the myoclonic and focal clonic types and random infant movements. The motion of the infants' body parts was quantified by temporal motion-strength signals extracted from video segments by motion-segmentation methods based on optical flow computation. The area of each frame occupied by the infants' moving body parts was segmented by clustering the motion parameters obtained by fitting an affine model to the pixel velocities. The motion of the infants' body parts also was quantified by temporal motion-trajectory signals extracted from video recordings by robust motion trackers based on block-motion models. These motion trackers were developed to adjust autonomously to illumination and contrast changes that may occur during the video-frame sequence. Video segments were represented by quantitative features obtained by analyzing motion-strength and motion-trajectory signals in both the time and frequency domains. Seizure recognition was performed by conventional feed-forward neural networks, quantum neural networks, and cosine radial basis function neural networks, which were trained to detect neonatal seizures of the myoclonic and focal clonic types and to distinguish them from random infant movements. The computational tools and procedures developed for automated seizure detection were evaluated on a set of 240 video segments of 54 patients exhibiting myoclonic seizures (80 segments), focal clonic seizures (80 segments), and random infant movements (80 segments). Regardless of the decision scheme used for interpreting the responses of the trained neural networks, all the neural network models exhibited sensitivity and specificity>90%. For one of the decision schemes proposed for interpreting the responses of the trained neural networks, the majority of the trained neural-network models exhibited sensitivity>90% and specificity>95%. In particular, cosine radial basis function neural networks achieved the performance targets of this phase of the project (i.e., sensitivity>95% and specificity>95%). The best among the motion segmentation and tracking methods developed in this study produced quantitative features that constitute a reliable basis for detecting neonatal seizures. The performance targets of this phase of the project were achieved by combining the quantitative features obtained by analyzing motion-strength signals with those produced by analyzing motion-trajectory signals. The computational procedures and tools developed in this study to perform off-line analysis of short video segments will be used in the next phase of this project, which involves the integration of these procedures and tools into a system that can process and analyze long video recordings of infants monitored for seizures in real time.

Hand gesture recognition in confined spaces with partial observability and occultation constraints

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen

2016-05-01

Human activity detection and recognition capabilities have broad applications for military and homeland security. These tasks are very complicated, however, especially when multiple persons are performing concurrent activities in confined spaces that impose significant obstruction, occultation, and observability uncertainty. In this paper, our primary contribution is to present a dedicated taxonomy and kinematic ontology that are developed for in-vehicle group human activities (IVGA). Secondly, we describe a set of hand-observable patterns that represents certain IVGA examples. Thirdly, we propose two classifiers for hand gesture recognition and compare their performance individually and jointly. Finally, we present a variant of Hidden Markov Model for Bayesian tracking, recognition, and annotation of hand motions, which enables spatiotemporal inference to human group activity perception and understanding. To validate our approach, synthetic (graphical data from virtual environment) and real physical environment video imagery are employed to verify the performance of these hand gesture classifiers, while measuring their efficiency and effectiveness based on the proposed Hidden Markov Model for tracking and interpreting dynamic spatiotemporal IVGA scenarios.

A state-based approach to trend recognition and failure prediction for the Space Station Freedom

NASA Technical Reports Server (NTRS)

Nelson, Kyle S.; Hadden, George D.

1992-01-01

A state-based reasoning approach to trend recognition and failure prediction for the Altitude Determination, and Control System (ADCS) of the Space Station Freedom (SSF) is described. The problem domain is characterized by features (e.g., trends and impending failures) that develop over a variety of time spans, anywhere from several minutes to several years. Our state-based reasoning approach, coupled with intelligent data screening, allows features to be tracked as they develop in a time-dependent manner. That is, each state machine has the ability to encode a time frame for the feature it detects. As features are detected, they are recorded and can be used as input to other state machines, creating a hierarchical feature recognition scheme. Furthermore, each machine can operate independently of the others, allowing simultaneous tracking of features. State-based reasoning was implemented in the trend recognition and the prognostic modules of a prototype Space Station Freedom Maintenance and Diagnostic System (SSFMDS) developed at Honeywell's Systems and Research Center.

49 CFR Appendix D to Part 213 - Minimally Compliant Analytical Track (MCAT) Simulations Used for Qualifying Vehicles To Operate...

Code of Federal Regulations, 2014 CFR

2014-10-01

... key responses observed during qualification testing. (b) MCAT layout. MCAT consists of nine segments, each designed to test a vehicle's performance in response to a specific type of track perturbation. The basic layout of MCAT is shown in figure 1 of this appendix, by type of track (curving or tangent), class...

49 CFR Appendix D to Part 213 - Minimally Compliant Analytical Track (MCAT) Simulations Used for Qualifying Vehicles To Operate...

Code of Federal Regulations, 2013 CFR

2013-10-01

... key responses observed during qualification testing. (b) MCAT layout. MCAT consists of nine segments, each designed to test a vehicle's performance in response to a specific type of track perturbation. The basic layout of MCAT is shown in figure 1 of this appendix, by type of track (curving or tangent), class...

Multi-Feature Based Information Extraction of Urban Green Space Along Road

NASA Astrophysics Data System (ADS)

Zhao, H. H.; Guan, H. Y.

2018-04-01

Green space along road of QuickBird image was studied in this paper based on multi-feature-marks in frequency domain. The magnitude spectrum of green along road was analysed, and the recognition marks of the tonal feature, contour feature and the road were built up by the distribution of frequency channels. Gabor filters in frequency domain were used to detect the features based on the recognition marks built up. The detected features were combined as the multi-feature-marks, and watershed based image segmentation were conducted to complete the extraction of green space along roads. The segmentation results were evaluated by Fmeasure with P = 0.7605, R = 0.7639, F = 0.7622.

Improved Healing of Large, Osseous, Segmental Defects by Reverse Dynamization: Evaluation in a Sheep Model

DTIC Science & Technology

2015-10-01

IFM ) through the separated bone cortices (fracture gap). In research funded by a CDMRP Idea Development Award, we used a rat segmental defect...491, 2011. [3] V. Glatt, M . Miller, a Ivkovic, F. Liu, N. Parry, D. Griffin, M . Vrahas, and C. Evans, “Improved healing of large segmental defects...2012. [4] M . Mehta, S . Checa, J. Lienau, D. Hutmacher, and G. N. Duda, “In vivo tracking of segmental bone defect healing reveals that callus

KSC-04PD-0139

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. The red NASA engine backs up with its cargo of containers in order to change tracks. The containers enclose segments of a solid rocket booster being returned to Utah for testing. The segments were part of the STS-114 stack. It is the first time actual flight segments that had been stacked for flight in the VAB are being returned for testing. They will undergo firing, which will enable inspectors to check the viability of the solid and verify the life expectancy for stacked segments.

A Novel Phonology- and Radical-Coded Chinese Sign Language Recognition Framework Using Accelerometer and Surface Electromyography Sensors

PubMed Central

Cheng, Juan; Chen, Xun; Liu, Aiping; Peng, Hu

2015-01-01

Sign language recognition (SLR) is an important communication tool between the deaf and the external world. It is highly necessary to develop a worldwide continuous and large-vocabulary-scale SLR system for practical usage. In this paper, we propose a novel phonology- and radical-coded Chinese SLR framework to demonstrate the feasibility of continuous SLR using accelerometer (ACC) and surface electromyography (sEMG) sensors. The continuous Chinese characters, consisting of coded sign gestures, are first segmented into active segments using EMG signals by means of moving average algorithm. Then, features of each component are extracted from both ACC and sEMG signals of active segments (i.e., palm orientation represented by the mean and variance of ACC signals, hand movement represented by the fixed-point ACC sequence, and hand shape represented by both the mean absolute value (MAV) and autoregressive model coefficients (ARs)). Afterwards, palm orientation is first classified, distinguishing “Palm Downward” sign gestures from “Palm Inward” ones. Only the “Palm Inward” gestures are sent for further hand movement and hand shape recognition by dynamic time warping (DTW) algorithm and hidden Markov models (HMM) respectively. Finally, component recognition results are integrated to identify one certain coded gesture. Experimental results demonstrate that the proposed SLR framework with a vocabulary scale of 223 characters can achieve an averaged recognition accuracy of 96.01% ± 0.83% for coded gesture recognition tasks and 92.73% ± 1.47% for character recognition tasks. Besides, it demonstrats that sEMG signals are rather consistent for a given hand shape independent of hand movements. Hence, the number of training samples will not be significantly increased when the vocabulary scale increases, since not only the number of the completely new proposed coded gestures is constant and limited, but also the transition movement which connects successive signs needs no training samples to model even though the same coded gesture performed in different characters. This work opens up a possible new way to realize a practical Chinese SLR system. PMID:26389907

A Novel Phonology- and Radical-Coded Chinese Sign Language Recognition Framework Using Accelerometer and Surface Electromyography Sensors.

PubMed

Cheng, Juan; Chen, Xun; Liu, Aiping; Peng, Hu

2015-09-15

Sign language recognition (SLR) is an important communication tool between the deaf and the external world. It is highly necessary to develop a worldwide continuous and large-vocabulary-scale SLR system for practical usage. In this paper, we propose a novel phonology- and radical-coded Chinese SLR framework to demonstrate the feasibility of continuous SLR using accelerometer (ACC) and surface electromyography (sEMG) sensors. The continuous Chinese characters, consisting of coded sign gestures, are first segmented into active segments using EMG signals by means of moving average algorithm. Then, features of each component are extracted from both ACC and sEMG signals of active segments (i.e., palm orientation represented by the mean and variance of ACC signals, hand movement represented by the fixed-point ACC sequence, and hand shape represented by both the mean absolute value (MAV) and autoregressive model coefficients (ARs)). Afterwards, palm orientation is first classified, distinguishing "Palm Downward" sign gestures from "Palm Inward" ones. Only the "Palm Inward" gestures are sent for further hand movement and hand shape recognition by dynamic time warping (DTW) algorithm and hidden Markov models (HMM) respectively. Finally, component recognition results are integrated to identify one certain coded gesture. Experimental results demonstrate that the proposed SLR framework with a vocabulary scale of 223 characters can achieve an averaged recognition accuracy of 96.01% ± 0.83% for coded gesture recognition tasks and 92.73% ± 1.47% for character recognition tasks. Besides, it demonstrats that sEMG signals are rather consistent for a given hand shape independent of hand movements. Hence, the number of training samples will not be significantly increased when the vocabulary scale increases, since not only the number of the completely new proposed coded gestures is constant and limited, but also the transition movement which connects successive signs needs no training samples to model even though the same coded gesture performed in different characters. This work opens up a possible new way to realize a practical Chinese SLR system.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

PubMed Central

Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

2016-01-01

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. PMID:26840313

Highlights of satellite-based forest change recognition and tracking using the ForWarn System

Treesearch

Steven P. Norman; William W. Hargrove; Joseph P. Spruce; William M. Christie; Sean W. Schroeder

2013-01-01

For a higher resolution version of this file, please use the following link: www.geobabble.orgSatellite-based remote sensing can assist forest managers with their need to recognize disturbances and track recovery. Despite the long...

New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks

NASA Astrophysics Data System (ADS)

Morillot, Olivier; Likforman-Sulem, Laurence; Grosicki, Emmanuèle

2013-04-01

Many preprocessing techniques have been proposed for isolated word recognition. However, recently, recognition systems have dealt with text blocks and their compound text lines. In this paper, we propose a new preprocessing approach to efficiently correct baseline skew and fluctuations. Our approach is based on a sliding window within which the vertical position of the baseline is estimated. Segmentation of text lines into subparts is, thus, avoided. Experiments conducted on a large publicly available database (Rimes), with a BLSTM (bidirectional long short-term memory) recurrent neural network recognition system, show that our baseline correction approach highly improves performance.

Methodology for the Evaluation of the Algorithms for Text Line Segmentation Based on Extended Binary Classification

NASA Astrophysics Data System (ADS)

Brodic, D.

2011-01-01

Text line segmentation represents the key element in the optical character recognition process. Hence, testing of text line segmentation algorithms has substantial relevance. All previously proposed testing methods deal mainly with text database as a template. They are used for testing as well as for the evaluation of the text segmentation algorithm. In this manuscript, methodology for the evaluation of the algorithm for text segmentation based on extended binary classification is proposed. It is established on the various multiline text samples linked with text segmentation. Their results are distributed according to binary classification. Final result is obtained by comparative analysis of cross linked data. At the end, its suitability for different types of scripts represents its main advantage.

Fully convolutional network with cluster for semantic segmentation

NASA Astrophysics Data System (ADS)

Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin

2018-04-01

At present, image semantic segmentation technology has been an active research topic for scientists in the field of computer vision and artificial intelligence. Especially, the extensive research of deep neural network in image recognition greatly promotes the development of semantic segmentation. This paper puts forward a method based on fully convolutional network, by cluster algorithm k-means. The cluster algorithm using the image's low-level features and initializing the cluster centers by the super-pixel segmentation is proposed to correct the set of points with low reliability, which are mistakenly classified in great probability, by the set of points with high reliability in each clustering regions. This method refines the segmentation of the target contour and improves the accuracy of the image segmentation.

Recognition of Time-Compressed and Natural Speech with Selective Temporal Enhancements by Young and Elderly Listeners

ERIC Educational Resources Information Center

Gordon-Salant, Sandra; Fitzgibbons, Peter J.; Friedman, Sarah A.

2007-01-01

Purpose: The goal of this experiment was to determine whether selective slowing of speech segments improves recognition performance by young and elderly listeners. The hypotheses were (a) the benefits of time expansion occur for rapid speech but not for natural-rate speech, (b) selective time expansion of consonants produces greater score…

Introduction and Overview of the Vicens-Reddy Speech Recognition System.

ERIC Educational Resources Information Center

Kameny, Iris; Ritea, H.

The Vicens-Reddy System is unique in the sense that it approaches the problem of speech recognition as a whole, rather than treating particular aspects of the problems as in previous attempts. For example, where earlier systems treated only segmentation of speech into phoneme groups, or detected phonemes in a given context, the Vicens-Reddy System…

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

NASA Technical Reports Server (NTRS)

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

1987-01-01

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

Attention and L2 Learners' Segmentation of Complex Sentences

ERIC Educational Resources Information Center

Hagiwara, Akiko

2010-01-01

The main objective of the current study is to investigate L2 Japanese learners' ability to segment complex sentences from aural input. Elementary- and early intermediate-level L2 learners in general have not developed the ability to use syntactic cues to interpret the meaning of sentences they hear. In the case of Japanese, recognition of…

Phoneme Segmenting Alignment with the Common Core Foundational Skills Standard Two: Grades K-1. Technical Report #1227

ERIC Educational Resources Information Center

Sáez, Leilani; Irvin, P. Shawn; Alonzo, Julie; Tindal, Gerald

2012-01-01

In 2006, the easyCBM reading assessment system was developed to support the progress monitoring of phoneme segmenting, letter names and sounds recognition, word reading, passage reading fluency, and comprehension skill development in elementary schools. More recently, the Common Core Standards in English Language Arts have been introduced as a…

Could quantitative longitudinal peak systolic strain help in the detection of left ventricular wall motion abnormalities in our daily echocardiographic practice?

PubMed

Benyounes, Nadia; Lang, Sylvie; Gout, Olivier; Ancédy, Yann; Etienney, Arnaud; Cohen, Ariel

2016-10-01

Transthoracic echocardiography is the most commonly used tool for the detection of left ventricular wall motion (LVWM) abnormalities using "naked eye evaluation". This subjective and operator-dependent technique requires a high level of clinical training and experience. Two-dimensional speckle-tracking echocardiography (2D-STE), which is less operator-dependent, has been proposed for this purpose. However, the role of on-line segmental longitudinal peak systolic strain (LPSS) values in the prediction of LVWM has not been fully evaluated. To test segmental LPSS for predicting LVWM abnormalities in routine echocardiography laboratory practice. LVWM was evaluated by an experienced cardiologist, during routine practice, in 620 patients; segmental LPSS values were then calculated. In this work, reflecting real life, 99.6% of segments were successfully tracked. Mean (95% confidence interval [CI]) segmental LPSS values for normal basal (n=3409), mid (n=3468) and apical (n=3466) segments were -16.7% (-16.9% to -16.5%), -18.2% (-18.3% to -18.0%) and -21.1% (-21.3% to -20.9%), respectively. Mean (95% CI) segmental LPSS values for hypokinetic basal (n=114), mid (n=116) and apical (n=90) segments were -7.7% (-9.0% to -6.3%), -10.1% (-11.1% to -9.0%) and -9.3% (-10.5% to -8.1%), respectively. Mean (95% CI) segmental LPSS values for akinetic basal (n=128), mid (n=95) and apical (n=91) segments were -6.6% (-8.0% to -5.1%), -6.1% (-7.7% to -4.6%) and -4.2% (-5.4% to -3.0%), respectively. LPSS allowed the differentiation between normal and abnormal segments at basal, mid and apical levels. An LPSS value≥-12% detected abnormal segmental motion with a sensitivity of 78% for basal, 70% for mid and 82% for apical segments. Segmental LPSS values may help to differentiate between normal and abnormal left ventricular segments. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Wavelet-based polarimetry analysis

NASA Astrophysics Data System (ADS)

Ezekiel, Soundararajan; Harrity, Kyle; Farag, Waleed; Alford, Mark; Ferris, David; Blasch, Erik

2014-06-01

Wavelet transformation has become a cutting edge and promising approach in the field of image and signal processing. A wavelet is a waveform of effectively limited duration that has an average value of zero. Wavelet analysis is done by breaking up the signal into shifted and scaled versions of the original signal. The key advantage of a wavelet is that it is capable of revealing smaller changes, trends, and breakdown points that are not revealed by other techniques such as Fourier analysis. The phenomenon of polarization has been studied for quite some time and is a very useful tool for target detection and tracking. Long Wave Infrared (LWIR) polarization is beneficial for detecting camouflaged objects and is a useful approach when identifying and distinguishing manmade objects from natural clutter. In addition, the Stokes Polarization Parameters, which are calculated from 0°, 45°, 90°, 135° right circular, and left circular intensity measurements, provide spatial orientations of target features and suppress natural features. In this paper, we propose a wavelet-based polarimetry analysis (WPA) method to analyze Long Wave Infrared Polarimetry Imagery to discriminate targets such as dismounts and vehicles from background clutter. These parameters can be used for image thresholding and segmentation. Experimental results show the wavelet-based polarimetry analysis is efficient and can be used in a wide range of applications such as change detection, shape extraction, target recognition, and feature-aided tracking.

A robust approach towards unknown transformation, regional adjacency graphs, multigraph matching, segmentation video frames from unnamed aerial vehicles (UAV)

NASA Astrophysics Data System (ADS)

Gohatre, Umakant Bhaskar; Patil, Venkat P.

2018-04-01

In computer vision application, the multiple object detection and tracking, in real-time operation is one of the important research field, that have gained a lot of attentions, in last few years for finding non stationary entities in the field of image sequence. The detection of object is advance towards following the moving object in video and then representation of object is step to track. The multiple object recognition proof is one of the testing assignment from detection multiple objects from video sequence. The picture enrollment has been for quite some time utilized as a reason for the location the detection of moving multiple objects. The technique of registration to discover correspondence between back to back casing sets in view of picture appearance under inflexible and relative change. The picture enrollment is not appropriate to deal with event occasion that can be result in potential missed objects. In this paper, for address such problems, designs propose novel approach. The divided video outlines utilizing area adjancy diagram of visual appearance and geometric properties. Then it performed between graph sequences by using multi graph matching, then getting matching region labeling by a proposed graph coloring algorithms which assign foreground label to respective region. The plan design is robust to unknown transformation with significant improvement in overall existing work which is related to moving multiple objects detection in real time parameters.

Biologically based machine vision: signal analysis of monopolar cells in the visual system of Musca domestica.

PubMed

Newton, Jenny; Barrett, Steven F; Wilcox, Michael J; Popp, Stephanie

2002-01-01

Machine vision for navigational purposes is a rapidly growing field. Many abilities such as object recognition and target tracking rely on vision. Autonomous vehicles must be able to navigate in dynamic enviroments and simultaneously locate a target position. Traditional machine vision often fails to react in real time because of large computational requirements whereas the fly achieves complex orientation and navigation with a relatively small and simple brain. Understanding how the fly extracts visual information and how neurons encode and process information could lead us to a new approach for machine vision applications. Photoreceptors in the Musca domestica eye that share the same spatial information converge into a structure called the cartridge. The cartridge consists of the photoreceptor axon terminals and monopolar cells L1, L2, and L4. It is thought that L1 and L2 cells encode edge related information relative to a single cartridge. These cells are thought to be equivalent to vertebrate bipolar cells, producing contrast enhancement and reduction of information sent to L4. Monopolar cell L4 is thought to perform image segmentation on the information input from L1 and L2 and also enhance edge detection. A mesh of interconnected L4's would correlate the output from L1 and L2 cells of adjacent cartridges and provide a parallel network for segmenting an object's edges. The focus of this research is to excite photoreceptors of the common housefly, Musca domestica, with different visual patterns. The electrical response of monopolar cells L1, L2, and L4 will be recorded using intracellular recording techniques. Signal analysis will determine the neurocircuitry to detect and segment images.

Lineage mapper: A versatile cell and particle tracker

NASA Astrophysics Data System (ADS)

Chalfoun, Joe; Majurski, Michael; Dima, Alden; Halter, Michael; Bhadriraju, Kiran; Brady, Mary

2016-11-01

The ability to accurately track cells and particles from images is critical to many biomedical problems. To address this, we developed Lineage Mapper, an open-source tracker for time-lapse images of biological cells, colonies, and particles. Lineage Mapper tracks objects independently of the segmentation method, detects mitosis in confluence, separates cell clumps mistakenly segmented as a single cell, provides accuracy and scalability even on terabyte-sized datasets, and creates division and/or fusion lineages. Lineage Mapper has been tested and validated on multiple biological and simulated problems. The software is available in ImageJ and Matlab at isg.nist.gov.

Wall-motion tracking in fetal echocardiography-Influence of frame rate on longitudinal strain analysis assessed by two-dimensional speckle tracking.

PubMed

Enzensberger, Christian; Achterberg, Friederike; Graupner, Oliver; Wolter, Aline; Herrmann, Johannes; Axt-Fliedner, Roland

2017-06-01

Frame rates (FR) used for strain analysis assessed by speckle tracking in fetal echocardiography show a considerable variation. The aim of this study was to investigate the influence of the FR on strain analysis in 2D speckle tracking. Fetal echocardiography was performed prospectively on a Toshiba Aplio 500 system and a Toshiba Artida system, respectively. Based on an apical or basal four-chamber view of the fetal heart, cine loops were stored with a FR of 30 fps (Aplio 500) and 60 fps (Artida/Aplio 500). For both groups (30fps and 60fps), global and segmental longitudinal peak systolic strain (LPSS) values of both, left (LV) and right ventricle (RV), were assessed by 2D wall-motion tracking. A total of 101 fetuses, distributed to three study groups, were included. The mean gestational age was 25.2±5.0 weeks. Mean global LPSS values for RV in the 30 fps group and in the 60 fps group were -16.07% and -16.47%, respectively. Mean global LPSS values for LV in the 30 fps group and in the 60 fps group were -17.54% and -17.06%, respectively. Comparing global and segmental LPSS values of both, the RV and LV, did not show any statistically significant differences within the two groups. Performance of myocardial 2D strain analysis by wall-motion tracking was feasible with 30 and 60 fps. Obtained global and segmental LPSS values of both ventricles were relatively independent from acquisition rate. © 2017, Wiley Periodicals, Inc.

A novel feature-tracking echocardiographic method for the quantitation of regional myocardial function: validation in an animal model of ischemia-reperfusion.

PubMed

Pirat, Bahar; Khoury, Dirar S; Hartley, Craig J; Tiller, Les; Rao, Liyun; Schulz, Daryl G; Nagueh, Sherif F; Zoghbi, William A

2008-02-12

The aim of this study was to validate a novel, angle-independent, feature-tracking method for the echocardiographic quantitation of regional function. A new echocardiographic method, Velocity Vector Imaging (VVI) (syngo Velocity Vector Imaging technology, Siemens Medical Solutions, Ultrasound Division, Mountain View, California), has been introduced, based on feature tracking-incorporating speckle and endocardial border tracking, that allows the quantitation of endocardial strain, strain rate (SR), and velocity. Seven dogs were studied during baseline, and various interventions causing alterations in regional function: dobutamine, 5-min coronary occlusion with reperfusion up to 1 h, followed by dobutamine and esmolol infusions. Echocardiographic images were acquired from short- and long-axis views of the left ventricle. Segment-length sonomicrometry crystals were used as the reference method. Changes in systolic strain in ischemic segments were tracked well with VVI during the different states of regional function. There was a good correlation between circumferential and longitudinal systolic strain by VVI and sonomicrometry (r = 0.88 and r = 0.83, respectively, p < 0.001). Strain measurements in the nonischemic basal segments also demonstrated a significant correlation between the 2 methods (r = 0.65, p < 0.001). Similarly, a significant relation was observed for circumferential and longitudinal SR between the 2 methods (r = 0.94, p < 0.001 and r = 0.90, p < 0.001, respectively). The endocardial velocity relation to changes in strain by sonomicrometry was weaker owing to significant cardiac translation. Velocity Vector Imaging, a new feature-tracking method, can accurately assess regional myocardial function at the endocardial level and is a promising clinical tool for the simultaneous quantification of regional and global myocardial function.

An audiovisual emotion recognition system

NASA Astrophysics Data System (ADS)

Han, Yi; Wang, Guoyin; Yang, Yong; He, Kun

2007-12-01

Human emotions could be expressed by many bio-symbols. Speech and facial expression are two of them. They are both regarded as emotional information which is playing an important role in human-computer interaction. Based on our previous studies on emotion recognition, an audiovisual emotion recognition system is developed and represented in this paper. The system is designed for real-time practice, and is guaranteed by some integrated modules. These modules include speech enhancement for eliminating noises, rapid face detection for locating face from background image, example based shape learning for facial feature alignment, and optical flow based tracking algorithm for facial feature tracking. It is known that irrelevant features and high dimensionality of the data can hurt the performance of classifier. Rough set-based feature selection is a good method for dimension reduction. So 13 speech features out of 37 ones and 10 facial features out of 33 ones are selected to represent emotional information, and 52 audiovisual features are selected due to the synchronization when speech and video fused together. The experiment results have demonstrated that this system performs well in real-time practice and has high recognition rate. Our results also show that the work in multimodules fused recognition will become the trend of emotion recognition in the future.

The research of edge extraction and target recognition based on inherent feature of objects

NASA Astrophysics Data System (ADS)

Xie, Yu-chan; Lin, Yu-chi; Huang, Yin-guo

2008-03-01

Current research on computer vision often needs specific techniques for particular problems. Little use has been made of high-level aspects of computer vision, such as three-dimensional (3D) object recognition, that are appropriate for large classes of problems and situations. In particular, high-level vision often focuses mainly on the extraction of symbolic descriptions, and pays little attention to the speed of processing. In order to extract and recognize target intelligently and rapidly, in this paper we developed a new 3D target recognition method based on inherent feature of objects in which cuboid was taken as model. On the basis of analysis cuboid nature contour and greyhound distributing characteristics, overall fuzzy evaluating technique was utilized to recognize and segment the target. Then Hough transform was used to extract and match model's main edges, we reconstruct aim edges by stereo technology in the end. There are three major contributions in this paper. Firstly, the corresponding relations between the parameters of cuboid model's straight edges lines in an image field and in the transform field were summed up. By those, the aimless computations and searches in Hough transform processing can be reduced greatly and the efficiency is improved. Secondly, as the priori knowledge about cuboids contour's geometry character known already, the intersections of the component extracted edges are taken, and assess the geometry of candidate edges matches based on the intersections, rather than the extracted edges. Therefore the outlines are enhanced and the noise is depressed. Finally, a 3-D target recognition method is proposed. Compared with other recognition methods, this new method has a quick response time and can be achieved with high-level computer vision. The method present here can be used widely in vision-guide techniques to strengthen its intelligence and generalization, which can also play an important role in object tracking, port AGV, robots fields. The results of simulation experiments and theory analyzing demonstrate that the proposed method could suppress noise effectively, extracted target edges robustly, and achieve the real time need. Theory analysis and experiment shows the method is reasonable and efficient.

Sub-micron accurate track navigation method ``Navi'' for the analysis of Nuclear Emulsion

NASA Astrophysics Data System (ADS)

Yoshioka, T.; Yoshida, J.; Kodama, K.

2011-03-01

Sub-micron accurate track navigation in Nuclear Emulsion is realized by using low energy signals detected by automated Nuclear Emulsion read-out systems. Using those much dense ``noise'', about 104 times larger than the real tracks, the accuracy of the track position navigation reaches to be sub micron only by using the information of a microscope field of view, 200 micron times 200 micron. This method is applied to OPERA analysis in Japan, i.e. support of human eye checks of the candidate tracks, confirmation of neutrino interaction vertexes and to embed missing track segments to the track data read-out by automated systems.

Applications of magnetic resonance image segmentation in neurology

NASA Astrophysics Data System (ADS)

Heinonen, Tomi; Lahtinen, Antti J.; Dastidar, Prasun; Ryymin, Pertti; Laarne, Paeivi; Malmivuo, Jaakko; Laasonen, Erkki; Frey, Harry; Eskola, Hannu

1999-05-01

After the introduction of digital imagin devices in medicine computerized tissue recognition and classification have become important in research and clinical applications. Segmented data can be applied among numerous research fields including volumetric analysis of particular tissues and structures, construction of anatomical modes, 3D visualization, and multimodal visualization, hence making segmentation essential in modern image analysis. In this research project several PC based software were developed in order to segment medical images, to visualize raw and segmented images in 3D, and to produce EEG brain maps in which MR images and EEG signals were integrated. The software package was tested and validated in numerous clinical research projects in hospital environment.

The Corporate Library and Issues Management.

ERIC Educational Resources Information Center

Lancaster, F. W.; Loescher, Jane

1994-01-01

Discussion of corporate library services and the role of the librarian focuses on the recognition and tracking of issues of potential significance to the corporation, or issues management. Topics addressed include environmental scanning of relevant literature, and the use of databases to track issues. (16 references) (LRW)

Eye tracking reveals a crucial role for facial motion in recognition of faces by infants

PubMed Central

Xiao, Naiqi G.; Quinn, Paul C.; Liu, Shaoying; Ge, Liezhong; Pascalis, Olivier; Lee, Kang

2015-01-01

Current knowledge about face processing in infancy comes largely from studies using static face stimuli, but faces that infants see in the real world are mostly moving ones. To bridge this gap, 3-, 6-, and 9-month-old Asian infants (N = 118) were familiarized with either moving or static Asian female faces and then their face recognition was tested with static face images. Eye tracking methodology was used to record eye movements during familiarization and test phases. The results showed a developmental change in eye movement patterns, but only for the moving faces. In addition, the more infants shifted their fixations across facial regions, the better was their face recognition, but only for the moving faces. The results suggest that facial movement influences the way faces are encoded from early in development. PMID:26010387

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks.

PubMed

Yu, Lequan; Chen, Hao; Dou, Qi; Qin, Jing; Heng, Pheng-Ann

2017-04-01

Automated melanoma recognition in dermoscopy images is a very challenging task due to the low contrast of skin lesions, the huge intraclass variation of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions, and the existence of many artifacts in the image. In order to meet these challenges, we propose a novel method for melanoma recognition by leveraging very deep convolutional neural networks (CNNs). Compared with existing methods employing either low-level hand-crafted features or CNNs with shallower architectures, our substantially deeper networks (more than 50 layers) can acquire richer and more discriminative features for more accurate recognition. To take full advantage of very deep networks, we propose a set of schemes to ensure effective training and learning under limited training data. First, we apply the residual learning to cope with the degradation and overfitting problems when a network goes deeper. This technique can ensure that our networks benefit from the performance gains achieved by increasing network depth. Then, we construct a fully convolutional residual network (FCRN) for accurate skin lesion segmentation, and further enhance its capability by incorporating a multi-scale contextual information integration scheme. Finally, we seamlessly integrate the proposed FCRN (for segmentation) and other very deep residual networks (for classification) to form a two-stage framework. This framework enables the classification network to extract more representative and specific features based on segmented results instead of the whole dermoscopy images, further alleviating the insufficiency of training data. The proposed framework is extensively evaluated on ISBI 2016 Skin Lesion Analysis Towards Melanoma Detection Challenge dataset. Experimental results demonstrate the significant performance gains of the proposed framework, ranking the first in classification and the second in segmentation among 25 teams and 28 teams, respectively. This study corroborates that very deep CNNs with effective training mechanisms can be employed to solve complicated medical image analysis tasks, even with limited training data.

Unification of automatic target tracking and automatic target recognition

NASA Astrophysics Data System (ADS)

Schachter, Bruce J.

2014-06-01

The subject being addressed is how an automatic target tracker (ATT) and an automatic target recognizer (ATR) can be fused together so tightly and so well that their distinctiveness becomes lost in the merger. This has historically not been the case outside of biology and a few academic papers. The biological model of ATT∪ATR arises from dynamic patterns of activity distributed across many neural circuits and structures (including retina). The information that the brain receives from the eyes is "old news" at the time that it receives it. The eyes and brain forecast a tracked object's future position, rather than relying on received retinal position. Anticipation of the next moment - building up a consistent perception - is accomplished under difficult conditions: motion (eyes, head, body, scene background, target) and processing limitations (neural noise, delays, eye jitter, distractions). Not only does the human vision system surmount these problems, but it has innate mechanisms to exploit motion in support of target detection and classification. Biological vision doesn't normally operate on snapshots. Feature extraction, detection and recognition are spatiotemporal. When vision is viewed as a spatiotemporal process, target detection, recognition, tracking, event detection and activity recognition, do not seem as distinct as they are in current ATT and ATR designs. They appear as similar mechanism taking place at varying time scales. A framework is provided for unifying ATT and ATR.

KSC00pp1030

NASA Image and Video Library

2000-07-22

The railroad tracks are under repair at Kennedy Space Center. This section of track is located on KSC property, just north of the NASA Causeway in the KSC Industrial Area. The repairs were required following the minor derailment of two solid rocket booster segment cars on July 18

KSC-00pp1031

NASA Image and Video Library

2000-07-27

Railroad track repairs have been completed at Kennedy Space Center. This section of track is located on KSC property, just north of the NASA Causeway in the KSC Industrial Area. The repairs were required following the minor derailment of two solid rocket booster segment cars on July 18

KSC-00pp1029

NASA Image and Video Library

2000-07-22

The railroad tracks are under repair at Kennedy Space Center. This section of track is located on KSC property, just north of the NASA Causeway in the KSC Industrial Area. The repairs were required following the minor derailment of two solid rocket booster segment cars on July 18

KSC-00pp1032

NASA Image and Video Library

2000-07-27

Railroad track repairs have been completed at Kennedy Space Center. This section of track is located on KSC property, just north of the NASA Causeway in the KSC Industrial Area. The repairs were required following the minor derailment of two solid rocket booster segment cars on July 18

KSC-00pp1030

NASA Image and Video Library

2000-07-22

The railroad tracks are under repair at Kennedy Space Center. This section of track is located on KSC property, just north of the NASA Causeway in the KSC Industrial Area. The repairs were required following the minor derailment of two solid rocket booster segment cars on July 18

KSC00pp1031

NASA Image and Video Library

2000-07-27

Railroad track repairs have been completed at Kennedy Space Center. This section of track is located on KSC property, just north of the NASA Causeway in the KSC Industrial Area. The repairs were required following the minor derailment of two solid rocket booster segment cars on July 18

KSC00pp1032

NASA Image and Video Library

2000-07-27

Railroad track repairs have been completed at Kennedy Space Center. This section of track is located on KSC property, just north of the NASA Causeway in the KSC Industrial Area. The repairs were required following the minor derailment of two solid rocket booster segment cars on July 18

KSC00pp1029

NASA Image and Video Library

2000-07-22

The railroad tracks are under repair at Kennedy Space Center. This section of track is located on KSC property, just north of the NASA Causeway in the KSC Industrial Area. The repairs were required following the minor derailment of two solid rocket booster segment cars on July 18

Tracking Organs Composed of One or Multiple Regions Using Geodesic Active Region Models

NASA Astrophysics Data System (ADS)

Martínez, A.; Jiménez, J. J.

In radiotherapy treatment it is very important to find out the target organs on the medical image sequence in order to determine and apply the proper dose. The techniques to achieve this goal can be classified into extrinsic and intrinsic. Intrinsic techniques only use image processing with medical images associated to the radiotherapy treatment, as we deal in this chapter. To accurately perform this organ tracking it is necessary to find out segmentation and tracking models that were able to be applied to several image modalities involved on a radiotherapy session (CT See Modality , MRI , etc.). The movements of the organs are mainly affected by two factors: breathing and involuntary movements associated with the internal organs or patient positioning. Among the several alternatives to track the organs of interest, a model based on geodesic active regions is proposed. This model has been tested over CT images from the pelvic, cardiac, and thoracic area. A new model for the segmentation of organs composed by more than one region is proposed.

Nearly automatic motion capture system for tracking octopus arm movements in 3D space.

PubMed

Zelman, Ido; Galun, Meirav; Akselrod-Ballin, Ayelet; Yekutieli, Yoram; Hochner, Binyamin; Flash, Tamar

2009-08-30

Tracking animal movements in 3D space is an essential part of many biomechanical studies. The most popular technique for human motion capture uses markers placed on the skin which are tracked by a dedicated system. However, this technique may be inadequate for tracking animal movements, especially when it is impossible to attach markers to the animal's body either because of its size or shape or because of the environment in which the animal performs its movements. Attaching markers to an animal's body may also alter its behavior. Here we present a nearly automatic markerless motion capture system that overcomes these problems and successfully tracks octopus arm movements in 3D space. The system is based on three successive tracking and processing stages. The first stage uses a recently presented segmentation algorithm to detect the movement in a pair of video sequences recorded by two calibrated cameras. In the second stage, the results of the first stage are processed to produce 2D skeletal representations of the moving arm. Finally, the 2D skeletons are used to reconstruct the octopus arm movement as a sequence of 3D curves varying in time. Motion tracking, segmentation and reconstruction are especially difficult problems in the case of octopus arm movements because of the deformable, non-rigid structure of the octopus arm and the underwater environment in which it moves. Our successful results suggest that the motion-tracking system presented here may be used for tracking other elongated objects.

Urdu Nasta'liq text recognition using implicit segmentation based on multi-dimensional long short term memory neural networks.

PubMed

Naz, Saeeda; Umar, Arif Iqbal; Ahmed, Riaz; Razzak, Muhammad Imran; Rashid, Sheikh Faisal; Shafait, Faisal

2016-01-01

The recognition of Arabic script and its derivatives such as Urdu, Persian, Pashto etc. is a difficult task due to complexity of this script. Particularly, Urdu text recognition is more difficult due to its Nasta'liq writing style. Nasta'liq writing style inherits complex calligraphic nature, which presents major issues to recognition of Urdu text owing to diagonality in writing, high cursiveness, context sensitivity and overlapping of characters. Therefore, the work done for recognition of Arabic script cannot be directly applied to Urdu recognition. We present Multi-dimensional Long Short Term Memory (MDLSTM) Recurrent Neural Networks with an output layer designed for sequence labeling for recognition of printed Urdu text-lines written in the Nasta'liq writing style. Experiments show that MDLSTM attained a recognition accuracy of 98% for the unconstrained Urdu Nasta'liq printed text, which significantly outperforms the state-of-the-art techniques.

Leaf Segmentation and Tracking in Arabidopsis thaliana Combined to an Organ-Scale Plant Model for Genotypic Differentiation

PubMed Central

Viaud, Gautier; Loudet, Olivier; Cournède, Paul-Henry

2017-01-01

A promising method for characterizing the phenotype of a plant as an interaction between its genotype and its environment is to use refined organ-scale plant growth models that use the observation of architectural traits, such as leaf area, containing a lot of information on the whole history of the functioning of the plant. The Phenoscope, a high-throughput automated platform, allowed the acquisition of zenithal images of Arabidopsis thaliana over twenty one days for 4 different genotypes. A novel image processing algorithm involving both segmentation and tracking of the plant leaves allows to extract areas of the latter. First, all the images in the series are segmented independently using a watershed-based approach. A second step based on ellipsoid-shaped leaves is then applied on the segments found to refine the segmentation. Taking into account all the segments at every time, the whole history of each leaf is reconstructed by choosing recursively through time the most probable segment achieving the best score, computed using some characteristics of the segment such as its orientation, its distance to the plant mass center and its area. These results are compared to manually extracted segments, showing a very good accordance in leaf rank and that they therefore provide low-biased data in large quantity for leaf areas. Such data can therefore be exploited to design an organ-scale plant model adapted from the existing GreenLab model for A. thaliana and subsequently parameterize it. This calibration of the model parameters should pave the way for differentiation between the Arabidopsis genotypes. PMID:28123392

Robust Real-Time and Rotation-Invariant American Sign Language Alphabet Recognition Using Range Camera

NASA Astrophysics Data System (ADS)

Lahamy, H.; Lichti, D.

2012-07-01

The automatic interpretation of human gestures can be used for a natural interaction with computers without the use of mechanical devices such as keyboards and mice. The recognition of hand postures have been studied for many years. However, most of the literature in this area has considered 2D images which cannot provide a full description of the hand gestures. In addition, a rotation-invariant identification remains an unsolved problem even with the use of 2D images. The objective of the current study is to design a rotation-invariant recognition process while using a 3D signature for classifying hand postures. An heuristic and voxelbased signature has been designed and implemented. The tracking of the hand motion is achieved with the Kalman filter. A unique training image per posture is used in the supervised classification. The designed recognition process and the tracking procedure have been successfully evaluated. This study has demonstrated the efficiency of the proposed rotation invariant 3D hand posture signature which leads to 98.24% recognition rate after testing 12723 samples of 12 gestures taken from the alphabet of the American Sign Language.

Lexical Competition in Non-Native Spoken-Word Recognition

ERIC Educational Resources Information Center

Weber, Andrea; Cutler, Anne

2004-01-01

Four eye-tracking experiments examined lexical competition in non-native spoken-word recognition. Dutch listeners hearing English fixated longer on distractor pictures with names containing vowels that Dutch listeners are likely to confuse with vowels in a target picture name ("pencil," given target "panda") than on less confusable distractors…

The Response Dynamics of Recognition Memory: Sensitivity and Bias

ERIC Educational Resources Information Center

Koop, Gregory J.; Criss, Amy H.

2016-01-01

Advances in theories of memory are hampered by insufficient metrics for measuring memory. The goal of this paper is to further the development of model-independent, sensitive empirical measures of the recognition decision process. We evaluate whether metrics from continuous mouse tracking, or response dynamics, uniquely identify response bias and…

Visual object recognition and tracking

NASA Technical Reports Server (NTRS)

Chang, Chu-Yin (Inventor); English, James D. (Inventor); Tardella, Neil M. (Inventor)

2010-01-01

This invention describes a method for identifying and tracking an object from two-dimensional data pictorially representing said object by an object-tracking system through processing said two-dimensional data using at least one tracker-identifier belonging to the object-tracking system for providing an output signal containing: a) a type of the object, and/or b) a position or an orientation of the object in three-dimensions, and/or c) an articulation or a shape change of said object in said three dimensions.

Prospective comparison of speckle tracking longitudinal bidimensional strain between two vendors.

PubMed

Castel, Anne-Laure; Szymanski, Catherine; Delelis, François; Levy, Franck; Menet, Aymeric; Mailliet, Amandine; Marotte, Nathalie; Graux, Pierre; Tribouilloy, Christophe; Maréchaux, Sylvestre

2014-02-01

Speckle tracking is a relatively new, largely angle-independent technique used for the evaluation of myocardial longitudinal strain (LS). However, significant differences have been reported between LS values obtained by speckle tracking with the first generation of software products. To compare LS values obtained with the most recently released equipment from two manufacturers. Systematic scanning with head-to-head acquisition with no modification of the patient's position was performed in 64 patients with equipment from two different manufacturers, with subsequent off-line post-processing for speckle tracking LS assessment (Philips QLAB 9.0 and General Electric [GE] EchoPAC BT12). The interobserver variability of each software product was tested on a randomly selected set of 20 echocardiograms from the study population. GE and Philips interobserver coefficients of variation (CVs) for global LS (GLS) were 6.63% and 5.87%, respectively, indicating good reproducibility. Reproducibility was very variable for regional and segmental LS values, with CVs ranging from 7.58% to 49.21% with both software products. The concordance correlation coefficient (CCC) between GLS values was high at 0.95, indicating substantial agreement between the two methods. While good agreement was observed between midwall and apical regional strains with the two software products, basal regional strains were poorly correlated. The agreement between the two software products at a segmental level was very variable; the highest correlation was obtained for the apical cap (CCC 0.90) and the poorest for basal segments (CCC range 0.31-0.56). A high level of agreement and reproducibility for global but not for basal regional or segmental LS was found with two vendor-dependent software products. This finding may help to reinforce clinical acceptance of GLS in everyday clinical practice. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Graphs and Tracks Revisited

NASA Astrophysics Data System (ADS)

Christian, Wolfgang; Belloni, Mario

2013-04-01

We have recently developed a Graphs and Tracks model based on an earlier program by David Trowbridge, as shown in Fig. 1. Our model can show position, velocity, acceleration, and energy graphs and can be used for motion-to-graphs exercises. Users set the heights of the track segments, and the model displays the motion of the ball on the track together with position, velocity, and acceleration graphs. This ready-to-run model is available in the ComPADRE OSP Collection at www.compadre.org/osp/items/detail.cfm?ID=12023.

49 CFR 236.1011 - PTC Implementation Plan content requirements.

Code of Federal Regulations, 2010 CFR

2010-10-01

... the following risk factors by track segment: (i) Segment traffic characteristics such as typical...; (4) How, to the extent practical, the PTC system will be implemented to address areas of greater risk to the public and railroad employees before areas of lesser risk; (5) The sequence and schedule in...

49 CFR 236.1011 - PTC Implementation Plan content requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

... decisions, and shall at a minimum address the following risk factors by track segment: (i) Segment traffic... implemented to address areas of greater risk to the public and railroad employees before areas of lesser risk... risk, including ruling grades and extreme curvature; (6) The following information relating to rolling...

Adults' Self-Directed Learning of an Artificial Lexicon: The Dynamics of Neighborhood Reorganization

ERIC Educational Resources Information Center

Bardhan, Neil Prodeep

2010-01-01

Artificial lexicons have previously been used to examine the time course of the learning and recognition of spoken words, the role of segment type in word learning, and the integration of context during spoken word recognition. However, in all of these studies the experimenter determined the frequency and order of the words to be learned. In three…

Transnational Geographies of Academic Distinction: The Role of Social Capital in the Recognition and Evaluation of "Overseas" Credentials

ERIC Educational Resources Information Center

Waters, Johanna L.

2009-01-01

This paper examines the role of specific and place-based social capital in the recognition and evaluation of international credentials. Whilst research on labour market segmentation has contributed towards an understanding of the spatial variability of the value of human capital, very little attention has been paid to the ways in which the…

A Set of Handwriting Features for Use in Automated Writer Identification.

PubMed

Miller, John J; Patterson, Robert Bradley; Gantz, Donald T; Saunders, Christopher P; Walch, Mark A; Buscaglia, JoAnn

2017-05-01

A writer's biometric identity can be characterized through the distribution of physical feature measurements ("writer's profile"); a graph-based system that facilitates the quantification of these features is described. To accomplish this quantification, handwriting is segmented into basic graphical forms ("graphemes"), which are "skeletonized" to yield the graphical topology of the handwritten segment. The graph-based matching algorithm compares the graphemes first by their graphical topology and then by their geometric features. Graphs derived from known writers can be compared against graphs extracted from unknown writings. The process is computationally intensive and relies heavily upon statistical pattern recognition algorithms. This article focuses on the quantification of these physical features and the construction of the associated pattern recognition methods for using the features to discriminate among writers. The graph-based system described in this article has been implemented in a highly accurate and approximately language-independent biometric recognition system of writers of cursive documents. © 2017 American Academy of Forensic Sciences.

Arabic sign language recognition based on HOG descriptor

NASA Astrophysics Data System (ADS)

Ben Jmaa, Ahmed; Mahdi, Walid; Ben Jemaa, Yousra; Ben Hamadou, Abdelmajid

2017-02-01

We present in this paper a new approach for Arabic sign language (ArSL) alphabet recognition using hand gesture analysis. This analysis consists in extracting a histogram of oriented gradient (HOG) features from a hand image and then using them to generate an SVM Models. Which will be used to recognize the ArSL alphabet in real-time from hand gesture using a Microsoft Kinect camera. Our approach involves three steps: (i) Hand detection and localization using a Microsoft Kinect camera, (ii) hand segmentation and (iii) feature extraction using Arabic alphabet recognition. One each input image first obtained by using a depth sensor, we apply our method based on hand anatomy to segment hand and eliminate all the errors pixels. This approach is invariant to scale, to rotation and to translation of the hand. Some experimental results show the effectiveness of our new approach. Experiment revealed that the proposed ArSL system is able to recognize the ArSL with an accuracy of 90.12%.

Recognition of speaker-dependent continuous speech with KEAL

NASA Astrophysics Data System (ADS)

Mercier, G.; Bigorgne, D.; Miclet, L.; Le Guennec, L.; Querre, M.

1989-04-01

A description of the speaker-dependent continuous speech recognition system KEAL is given. An unknown utterance, is recognized by means of the followng procedures: acoustic analysis, phonetic segmentation and identification, word and sentence analysis. The combination of feature-based, speaker-independent coarse phonetic segmentation with speaker-dependent statistical classification techniques is one of the main design features of the acoustic-phonetic decoder. The lexical access component is essentially based on a statistical dynamic programming technique which aims at matching a phonemic lexical entry containing various phonological forms, against a phonetic lattice. Sentence recognition is achieved by use of a context-free grammar and a parsing algorithm derived from Earley's parser. A speaker adaptation module allows some of the system parameters to be adjusted by matching known utterances with their acoustical representation. The task to be performed, described by its vocabulary and its grammar, is given as a parameter of the system. Continuously spoken sentences extracted from a 'pseudo-Logo' language are analyzed and results are presented.

Exploring local regularities for 3D object recognition

NASA Astrophysics Data System (ADS)

Tian, Huaiwen; Qin, Shengfeng

2016-11-01

In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles(L-MSDA), localized minimizing standard deviation of segment magnitudes(L-MSDSM), localized minimum standard deviation of areas of child faces (L-MSDAF), localized minimum sum of segment magnitudes of common edges (L-MSSM), and localized minimum sum of areas of child face (L-MSAF). Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities: L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness.

The National Aeronautics and Space Administration (NASA) Tracking and Data Relay Satellite System (TDRSS) program Economic and programmatic, considerations

NASA Technical Reports Server (NTRS)

Aller, R. O.

1985-01-01

The Tracking and Data Relay Satellite System (TDRSS) represents the principal element of a new space-based tracking and communication network which will support NASA spaceflight missions in low earth orbit. In its complete configuration, the TDRSS network will include a space segment consisting of three highly specialized communication satellites in geosynchronous orbit, a ground segment consisting of an earth terminal, and associated data handling and control facilities. The TDRSS network has the objective to provide communication and data relay services between the earth-orbiting spacecraft and their ground-based mission control and data handling centers. The first TDRSS spacecraft has been now in service for two years. The present paper is concerned with the TDRSS experience from the perspective of the various programmatic and economic considerations which relate to the program.

ETHOWATCHER: validation of a tool for behavioral and video-tracking analysis in laboratory animals.

PubMed

Crispim Junior, Carlos Fernando; Pederiva, Cesar Nonato; Bose, Ricardo Chessini; Garcia, Vitor Augusto; Lino-de-Oliveira, Cilene; Marino-Neto, José

2012-02-01

We present a software (ETHOWATCHER(®)) developed to support ethography, object tracking and extraction of kinematic variables from digital video files of laboratory animals. The tracking module allows controlled segmentation of the target from the background, extracting image attributes used to calculate the distance traveled, orientation, length, area and a path graph of the experimental animal. The ethography module allows recording of catalog-based behaviors from environment or from video files continuously or frame-by-frame. The output reports duration, frequency and latency of each behavior and the sequence of events in a time-segmented format, set by the user. Validation tests were conducted on kinematic measurements and on the detection of known behavioral effects of drugs. This software is freely available at www.ethowatcher.ufsc.br. Copyright © 2011 Elsevier Ltd. All rights reserved.

Facial recognition in education system

NASA Astrophysics Data System (ADS)

Krithika, L. B.; Venkatesh, K.; Rathore, S.; Kumar, M. Harish

2017-11-01

Human beings exploit emotions comprehensively for conveying messages and their resolution. Emotion detection and face recognition can provide an interface between the individuals and technologies. The most successful applications of recognition analysis are recognition of faces. Many different techniques have been used to recognize the facial expressions and emotion detection handle varying poses. In this paper, we approach an efficient method to recognize the facial expressions to track face points and distances. This can automatically identify observer face movements and face expression in image. This can capture different aspects of emotion and facial expressions.

49 CFR 214.319 - Working limits, generally.

Code of Federal Regulations, 2010 CFR

2010-10-01

... control over working limits for the purpose of establishing on-track safety. (b) Only one roadway worker shall have control over working limits on any one segment of track. (c) All affected roadway workers..., DEPARTMENT OF TRANSPORTATION RAILROAD WORKPLACE SAFETY Roadway Worker Protection § 214.319 Working limits...

Aerodynamics of Tracked Ram Air Cushion Vehicles - Effects of Pitch Attitude and Upper Surface Flow

DOT National Transportation Integrated Search

1979-12-01

Three types of experiments were conducted on geometrically similar model of a Tracked Ram Air Cushion Vehicle (TRACV). The first consisted of wind tunnel tests with the vehicle model positioned within a short segment of stationary guideway. In the se...

Software for roof defects recognition on aerial photographs

NASA Astrophysics Data System (ADS)

Yudin, D.; Naumov, A.; Dolzhenko, A.; Patrakova, E.

2018-05-01

The article presents information on software for roof defects recognition on aerial photographs, made with air drones. An areal image segmentation mechanism is described. It allows detecting roof defects – unsmoothness that causes water stagnation after rain. It is shown that HSV-transformation approach allows quick detection of stagnation areas, their size and perimeters, but is sensitive to shadows and changes of the roofing-types. Deep Fully Convolutional Network software solution eliminates this drawback. The tested data set consists of the roofing photos with defects and binary masks for them. FCN approach gave acceptable results of image segmentation in Dice metric average value. This software can be used in inspection automation of roof conditions in the production sector and housing and utilities infrastructure.

Preventive maintenance. 'Problem recognition style' can be used to segment the market and promote healthier lifestyles.

PubMed

Jayanti, R K

1997-01-01

Problem recognition styles--desired state types (DSTs) and actual state types (ASTs)--have an effect on preventive health care decision making. Segmenting the market along these lines can help marketers position products and services to educate and attract people who will not see a doctor unless there is something wrong with them. Both groups expect the same benefits from preventive health care actions, but ASTs fail to act on those expectations. Therefore, marketing strategy touting the benefits of preventive health care might be futile. Educational promotional campaigns aimed at both DSTs and ASTs also are wasteful because DSTs already possess much health knowledge, lead wellness-oriented lifestyles, and practice preventive health behaviors.

Introduction of statistical information in a syntactic analyzer for document image recognition

NASA Astrophysics Data System (ADS)

Maroneze, André O.; Coüasnon, Bertrand; Lemaitre, Aurélie

2011-01-01

This paper presents an improvement to document layout analysis systems, offering a possible solution to Sayre's paradox (which states that an element "must be recognized before it can be segmented; and it must be segmented before it can be recognized"). This improvement, based on stochastic parsing, allows integration of statistical information, obtained from recognizers, during syntactic layout analysis. We present how this fusion of numeric and symbolic information in a feedback loop can be applied to syntactic methods to improve document description expressiveness. To limit combinatorial explosion during exploration of solutions, we devised an operator that allows optional activation of the stochastic parsing mechanism. Our evaluation on 1250 handwritten business letters shows this method allows the improvement of global recognition scores.

Optical and digital pattern recognition; Proceedings of the Meeting, Los Angeles, CA, Jan. 13-15, 1987

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Editor); Schenker, Paul (Editor)

1987-01-01

The papers presented in this volume provide an overview of current research in both optical and digital pattern recognition, with a theme of identifying overlapping research problems and methodologies. Topics discussed include image analysis and low-level vision, optical system design, object analysis and recognition, real-time hybrid architectures and algorithms, high-level image understanding, and optical matched filter design. Papers are presented on synthetic estimation filters for a control system; white-light correlator character recognition; optical AI architectures for intelligent sensors; interpreting aerial photographs by segmentation and search; and optical information processing using a new photopolymer.

Mexican sign language recognition using normalized moments and artificial neural networks

NASA Astrophysics Data System (ADS)

Solís-V., J.-Francisco; Toxqui-Quitl, Carina; Martínez-Martínez, David; H.-G., Margarita

2014-09-01

This work presents a framework designed for the Mexican Sign Language (MSL) recognition. A data set was recorded with 24 static signs from the MSL using 5 different versions, this MSL dataset was captured using a digital camera in incoherent light conditions. Digital Image Processing was used to segment hand gestures, a uniform background was selected to avoid using gloved hands or some special markers. Feature extraction was performed by calculating normalized geometric moments of gray scaled signs, then an Artificial Neural Network performs the recognition using a 10-fold cross validation tested in weka, the best result achieved 95.83% of recognition rate.

Shape-specific perceptual learning in a figure-ground segregation task.

PubMed

Yi, Do-Joon; Olson, Ingrid R; Chun, Marvin M

2006-03-01

What does perceptual experience contribute to figure-ground segregation? To study this question, we trained observers to search for symmetric dot patterns embedded in random dot backgrounds. Training improved shape segmentation, but learning did not completely transfer either to untrained locations or to untrained shapes. Such partial specificity persisted for a month after training. Interestingly, training on shapes in empty backgrounds did not help segmentation of the trained shapes in noisy backgrounds. Our results suggest that perceptual training increases the involvement of early sensory neurons in the segmentation of trained shapes, and that successful segmentation requires perceptual skills beyond shape recognition alone.

A landmark recognition and tracking experiment for flight on the Shuttle/Advanced Technology Laboratory (ATL)

NASA Technical Reports Server (NTRS)

Welch, J. D.

1975-01-01

The preliminary design of an experiment for landmark recognition and tracking from the Shuttle/Advanced Technology Laboratory is described. It makes use of parallel coherent optical processing to perform correlation tests between landmarks observed passively with a telescope and previously made holographic matched filters. The experimental equipment including the optics, the low power laser, the random access file of matched filters and the electro-optical readout device are described. A real time optically excited liquid crystal device is recommended for performing the input non-coherent optical to coherent optical interface function. A development program leading to a flight experiment in 1981 is outlined.

CAVIAR: a 45k neuron, 5M synapse, 12G connects/s AER hardware sensory-processing- learning-actuating system for high-speed visual object recognition and tracking.

PubMed

Serrano-Gotarredona, Rafael; Oster, Matthias; Lichtsteiner, Patrick; Linares-Barranco, Alejandro; Paz-Vicente, Rafael; Gomez-Rodriguez, Francisco; Camunas-Mesa, Luis; Berner, Raphael; Rivas-Perez, Manuel; Delbruck, Tobi; Liu, Shih-Chii; Douglas, Rodney; Hafliger, Philipp; Jimenez-Moreno, Gabriel; Civit Ballcels, Anton; Serrano-Gotarredona, Teresa; Acosta-Jimenez, Antonio J; Linares-Barranco, Bernabé

2009-09-01

This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45k neurons (spiking cells), up to 5M synapses, performs 12G synaptic operations per second, and achieves millisecond object recognition and tracking latencies.

Glucose administration prior to a divided attention task improves tracking performance but not word recognition: evidence against differential memory enhancement?

PubMed

Scholey, Andrew B; Sünram-Lea, Sandra I; Greer, Joanna; Elliott, Jade; Kennedy, David O

2009-01-01

The cognition-enhancing effects of glucose administration to humans have been well-documented; however, it remains unclear whether this effect preferentially targets episodic memory or other cognitive domains. The effect of glucose on the allocation of attentional resources during memory encoding was assessed using a sensitive dual-attention paradigm. One hundred and twenty volunteers (mean age 21.60, SD 4.89, 77 females) took part in this randomised, double-blind, placebo-controlled, parallel groups study where each consumed a 25-g glucose drink or a placebo. Half of the participants in each drink condition attempted to track a moving on-screen target during auditory word presentation. The distance between the cursor and the tracking target was used as an index of attentional cost during encoding. Effects of drink and tracking on recognition memory and drink on tracking performance were assessed. Self-rated appetite and mood were co-monitored. Co-performing the tracking task significantly impaired memory performance irrespective of drink condition. In the placebo-tracking condition, there was a cost to tracking manifest as greater deviation from target during and immediately following word presentation. Compared with placebo, the glucose drink significantly improved tracking performance during encoding. There were significant time-related changes in thirst and alertness ratings but these were not differentially affected by drink or tracking conditions. Tracking but not memory was enhanced by glucose. This finding suggests that, under certain task conditions, glucose administrations does not preferentially enhance memory performance. One mechanism through which glucose acts as a cognition enhancer is through allowing greater allocation of attentional resources.

Fusion-based multi-target tracking and localization for intelligent surveillance systems

NASA Astrophysics Data System (ADS)

Rababaah, Haroun; Shirkhodaie, Amir

2008-04-01

In this paper, we have presented two approaches addressing visual target tracking and localization in complex urban environment. The two techniques presented in this paper are: fusion-based multi-target visual tracking, and multi-target localization via camera calibration. For multi-target tracking, the data fusion concepts of hypothesis generation/evaluation/selection, target-to-target registration, and association are employed. An association matrix is implemented using RGB histograms for associated tracking of multi-targets of interests. Motion segmentation of targets of interest (TOI) from the background was achieved by a Gaussian Mixture Model. Foreground segmentation, on other hand, was achieved by the Connected Components Analysis (CCA) technique. The tracking of individual targets was estimated by fusing two sources of information, the centroid with the spatial gating, and the RGB histogram association matrix. The localization problem is addressed through an effective camera calibration technique using edge modeling for grid mapping (EMGM). A two-stage image pixel to world coordinates mapping technique is introduced that performs coarse and fine location estimation of moving TOIs. In coarse estimation, an approximate neighborhood of the target position is estimated based on nearest 4-neighbor method, and in fine estimation, we use Euclidean interpolation to localize the position within the estimated four neighbors. Both techniques were tested and shown reliable results for tracking and localization of Targets of interests in complex urban environment.

Image-based automatic recognition of larvae

NASA Astrophysics Data System (ADS)

Sang, Ru; Yu, Guiying; Fan, Weijun; Guo, Tiantai

2010-08-01

As the main objects, imagoes have been researched in quarantine pest recognition in these days. However, pests in their larval stage are latent, and the larvae spread abroad much easily with the circulation of agricultural and forest products. It is presented in this paper that, as the new research objects, larvae are recognized by means of machine vision, image processing and pattern recognition. More visional information is reserved and the recognition rate is improved as color image segmentation is applied to images of larvae. Along with the characteristics of affine invariance, perspective invariance and brightness invariance, scale invariant feature transform (SIFT) is adopted for the feature extraction. The neural network algorithm is utilized for pattern recognition, and the automatic identification of larvae images is successfully achieved with satisfactory results.

Background feature descriptor for offline handwritten numeral recognition

NASA Astrophysics Data System (ADS)

Ming, Delie; Wang, Hao; Tian, Tian; Jie, Feiran; Lei, Bo

2011-11-01

This paper puts forward an offline handwritten numeral recognition method based on background structural descriptor (sixteen-value numerical background expression). Through encoding the background pixels in the image according to a certain rule, 16 different eigenvalues were generated, which reflected the background condition of every digit, then reflected the structural features of the digits. Through pattern language description of images by these features, automatic segmentation of overlapping digits and numeral recognition can be realized. This method is characterized by great deformation resistant ability, high recognition speed and easy realization. Finally, the experimental results and conclusions are presented. The experimental results of recognizing datasets from various practical application fields reflect that with this method, a good recognition effect can be achieved.

Eye tracking reveals a crucial role for facial motion in recognition of faces by infants.

PubMed

Xiao, Naiqi G; Quinn, Paul C; Liu, Shaoying; Ge, Liezhong; Pascalis, Olivier; Lee, Kang

2015-06-01

Current knowledge about face processing in infancy comes largely from studies using static face stimuli, but faces that infants see in the real world are mostly moving ones. To bridge this gap, 3-, 6-, and 9-month-old Asian infants (N = 118) were familiarized with either moving or static Asian female faces, and then their face recognition was tested with static face images. Eye-tracking methodology was used to record eye movements during the familiarization and test phases. The results showed a developmental change in eye movement patterns, but only for the moving faces. In addition, the more infants shifted their fixations across facial regions, the better their face recognition was, but only for the moving faces. The results suggest that facial movement influences the way faces are encoded from early in development. (c) 2015 APA, all rights reserved).

An optical processor for object recognition and tracking

NASA Technical Reports Server (NTRS)

Sloan, J.; Udomkesmalee, S.

1987-01-01

The design and development of a miniaturized optical processor that performs real time image correlation are described. The optical correlator utilizes the Vander Lugt matched spatial filter technique. The correlation output, a focused beam of light, is imaged onto a CMOS photodetector array. In addition to performing target recognition, the device also tracks the target. The hardware, composed of optical and electro-optical components, occupies only 590 cu cm of volume. A complete correlator system would also include an input imaging lens. This optical processing system is compact, rugged, requires only 3.5 watts of operating power, and weighs less than 3 kg. It represents a major achievement in miniaturizing optical processors. When considered as a special-purpose processing unit, it is an attractive alternative to conventional digital image recognition processing. It is conceivable that the combined technology of both optical and ditital processing could result in a very advanced robot vision system.

Missing from the Institutional Data Picture: Non-Tenure-Track Faculty

ERIC Educational Resources Information Center

Kezar, Adrianna; Maxey, Daniel

2012-01-01

Institutional researchers might know more about non-tenure-track (NTT) faculty than leaders on many college campuses, particularly four-year institutions and research universities (Cross and Goldenberg, 2009). As such, institutional researchers play an important role in educating campus leaders about this growing segment of the academic workforce.…

Real-Time Gaze Tracking for Public Displays

NASA Astrophysics Data System (ADS)

Sippl, Andreas; Holzmann, Clemens; Zachhuber, Doris; Ferscha, Alois

In this paper, we explore the real-time tracking of human gazes in front of large public displays. The aim of our work is to estimate at which area of a display one ore more people are looking at a time, independently from the distance and angle to the display as well as the height of the tracked people. Gaze tracking is relevant for a variety of purposes, including the automatic recognition of the user's focus of attention, or the control of interactive applications with gaze gestures. The scope of the present paper is on the former, and we show how gaze tracking can be used for implicit interaction in the pervasive advertising domain. We have developed a prototype for this purpose, which (i) uses an overhead mounted camera to distinguish four gaze areas on a large display, (ii) works for a wide range of positions in front of the display, and (iii) provides an estimation of the currently gazed quarters in real time. A detailed description of the prototype as well as the results of a user study with 12 participants, which show the recognition accuracy for different positions in front of the display, are presented.

Arabic handwritten: pre-processing and segmentation

NASA Astrophysics Data System (ADS)

Maliki, Makki; Jassim, Sabah; Al-Jawad, Naseer; Sellahewa, Harin

2012-06-01

This paper is concerned with pre-processing and segmentation tasks that influence the performance of Optical Character Recognition (OCR) systems and handwritten/printed text recognition. In Arabic, these tasks are adversely effected by the fact that many words are made up of sub-words, with many sub-words there associated one or more diacritics that are not connected to the sub-word's body; there could be multiple instances of sub-words overlap. To overcome these problems we investigate and develop segmentation techniques that first segment a document into sub-words, link the diacritics with their sub-words, and removes possible overlapping between words and sub-words. We shall also investigate two approaches for pre-processing tasks to estimate sub-words baseline, and to determine parameters that yield appropriate slope correction, slant removal. We shall investigate the use of linear regression on sub-words pixels to determine their central x and y coordinates, as well as their high density part. We also develop a new incremental rotation procedure to be performed on sub-words that determines the best rotation angle needed to realign baselines. We shall demonstrate the benefits of these proposals by conducting extensive experiments on publicly available databases and in-house created databases. These algorithms help improve character segmentation accuracy by transforming handwritten Arabic text into a form that could benefit from analysis of printed text.

Foot-mounted inertial measurement unit for activity classification.

PubMed

Ghobadi, Mostafa; Esfahani, Ehsan T

2014-01-01

This paper proposes a classification technique for daily base activity recognition for human monitoring during physical therapy in home. The proposed method estimates the foot motion using single inertial measurement unit, then segments the motion into steps classify them by template-matching as walking, stairs up or stairs down steps. The results show a high accuracy of activity recognition. Unlike previous works which are limited to activity recognition, the proposed approach is more qualitative by providing similarity index of any activity to its desired template which can be used to assess subjects improvement.

Development of Face Recognition in Infant Chimpanzees (Pan Troglodytes)

ERIC Educational Resources Information Center

Myowa-Yamakoshi, M.; Yamaguchi, M.K.; Tomonaga, M.; Tanaka, M.; Matsuzawa, T.

2005-01-01

In this paper, we assessed the developmental changes in face recognition by three infant chimpanzees aged 1-18 weeks, using preferential-looking procedures that measured the infants' eye- and head-tracking of moving stimuli. In Experiment 1, we prepared photographs of the mother of each infant and an ''average'' chimpanzee face using…

Representation of 3-Dimenstional Objects by the Rat Perirhinal Cortex

PubMed Central

Burke, S.N.; Maurer, A.P.; Hartzell, A.L.; Nematollahi, S.; Uprety, A.; Wallace, J.L.; Barnes, C.A.

2012-01-01

The perirhinal cortex (PRC) is known to play an important role in object recognition. Little is known, however, regarding the activity of PRC neurons during the presentation of stimuli that are commonly used for recognition memory tasks in rodents, that is, 3-dimensional objects. Rats in the present study were exposed to 3-dimensional objects while they traversed a circular track for food reward. Under some behavioral conditions the track contained novel objects, familiar objects, or no objects. Approximately 38% of PRC neurons demonstrated ‘object fields’ (a selective increase in firing at the location of one or more objects). Although the rats spent more time exploring the objects when they were novel compared to familiar, indicating successful recognition memory, the proportion of object fields and the firing rates of PRC neurons were not affected by the rats’ previous experience with the objects. Together these data indicate that the activity of PRC cells is powerfully affected by the presence of objects while animals navigate through an environment, but under these conditions, the firing patterns are not altered by the relative novelty of objects during successful object recognition. PMID:22987680

Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates

NASA Technical Reports Server (NTRS)

Mikic, I.; Krucinski, S.; Thomas, J. D.

1998-01-01

This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability.

On the recognition of complex structures: Computer software using artificial intelligence applied to pattern recognition

NASA Technical Reports Server (NTRS)

Yakimovsky, Y.

1974-01-01

An approach to simultaneous interpretation of objects in complex structures so as to maximize a combined utility function is presented. Results of the application of a computer software system to assign meaning to regions in a segmented image based on the principles described in this paper and on a special interactive sequential classification learning system, which is referenced, are demonstrated.

The Development and Evaluation of a Teleprocessed Computer-Assisted Instruction Course in the Recognition of Malarial Parasites. Final Report; May 1, 1967 - June 30, 1968.

ERIC Educational Resources Information Center

Mitzel, Harold E.

A computer-assisted instruction course in the recognition of malarial parasites was developed and evaluated. The course includes stage discrimination, species discrimination, and case histories. Segments developed use COURSEWRITER as an author language and are presented via a display terminal that permits two-way communication with an IBM computer…

49 CFR 213.110 - Gage restraint measurement systems.

Code of Federal Regulations, 2011 CFR

2011-10-01

... applying an increasing lateral force from 0 to 4,000 pounds on the web/base fillet of each rail... on which GRMS technology will be implemented; and (2) The track owner notifies the appropriate FRA... (2) The most recent record of million gross tons of traffic per year over the identified segment(s...

49 CFR 213.110 - Gage restraint measurement systems.

Code of Federal Regulations, 2010 CFR

2010-10-01

... applying an increasing lateral force from 0 to 4,000 pounds on the web/base fillet of each rail... on which GRMS technology will be implemented; and (2) The track owner notifies the appropriate FRA... (2) The most recent record of million gross tons of traffic per year over the identified segment(s...

49 CFR 213.237 - Inspection of rail.

Code of Federal Regulations, 2014 CFR

2014-10-01

.... (1) To change the designation of a rail inspection segment or to establish a new segment pursuant to... shall remain in effect until the track owner's new designation is approved or conditionally approved by... 25, 2014 has not accumulated more than a total of 30 mgt in previous and new locations since its last...

Nanoscopic compartmentalization of membrane protein motion at the axon initial segment.

PubMed

Albrecht, David; Winterflood, Christian M; Sadeghi, Mohsen; Tschager, Thomas; Noé, Frank; Ewers, Helge

2016-10-10

The axon initial segment (AIS) is enriched in specific adaptor, cytoskeletal, and transmembrane molecules. During AIS establishment, a membrane diffusion barrier is formed between the axonal and somatodendritic domains. Recently, an axonal periodic pattern of actin, spectrin, and ankyrin forming 190-nm-spaced, ring-like structures has been discovered. However, whether this structure is related to the diffusion barrier function is unclear. Here, we performed single-particle tracking time-course experiments on hippocampal neurons during AIS development. We analyzed the mobility of lipid-anchored molecules by high-speed single-particle tracking and correlated positions of membrane molecules with the nanoscopic organization of the AIS cytoskeleton. We observe a strong reduction in mobility early in AIS development. Membrane protein motion in the AIS plasma membrane is confined to a repetitive pattern of ∼190-nm-spaced segments along the AIS axis as early as day in vitro 4, and this pattern alternates with actin rings. Mathematical modeling shows that diffusion barriers between the segments significantly reduce lateral diffusion along the axon. © 2016 Albrecht et al.

Container-code recognition system based on computer vision and deep neural networks

NASA Astrophysics Data System (ADS)

Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

2018-04-01

Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

Disruption of Specific RNA-RNA Interactions in a Double-Stranded RNA Virus Inhibits Genome Packaging and Virus Infectivity

PubMed Central

Fajardo, Teodoro; Sung, Po-Yu; Roy, Polly

2015-01-01

Bluetongue virus (BTV) causes hemorrhagic disease in economically important livestock. The BTV genome is organized into ten discrete double-stranded RNA molecules (S1-S10) which have been suggested to follow a sequential packaging pathway from smallest to largest segment during virus capsid assembly. To substantiate and extend these studies, we have investigated the RNA sorting and packaging mechanisms with a new experimental approach using inhibitory oligonucleotides. Putative packaging signals present in the 3’untranslated regions of BTV segments were targeted by a number of nuclease resistant oligoribonucleotides (ORNs) and their effects on virus replication in cell culture were assessed. ORNs complementary to the 3’ UTR of BTV RNAs significantly inhibited virus replication without affecting protein synthesis. Same ORNs were found to inhibit complex formation when added to a novel RNA-RNA interaction assay which measured the formation of supramolecular complexes between and among different RNA segments. ORNs targeting the 3’UTR of BTV segment 10, the smallest RNA segment, were shown to be the most potent and deletions or substitution mutations of the targeted sequences diminished the RNA complexes and abolished the recovery of viable viruses using reverse genetics. Cell-free capsid assembly/RNA packaging assay also confirmed that the inhibitory ORNs could interfere with RNA packaging and further substitution mutations within the putative RNA packaging sequence have identified the recognition sequence concerned. Exchange of 3’UTR between segments have further demonstrated that RNA recognition was segment specific, most likely acting as part of the secondary structure of the entire genomic segment. Our data confirm that genome packaging in this segmented dsRNA virus occurs via the formation of supramolecular complexes formed by the interaction of specific sequences located in the 3’ UTRs. Additionally, the inhibition of packaging in-trans with inhibitory ORNs suggests this that interaction is a bona fide target for the design of compounds with antiviral activity. PMID:26646790

Disruption of Specific RNA-RNA Interactions in a Double-Stranded RNA Virus Inhibits Genome Packaging and Virus Infectivity.

PubMed

Fajardo, Teodoro; Sung, Po-Yu; Roy, Polly

2015-12-01

Bluetongue virus (BTV) causes hemorrhagic disease in economically important livestock. The BTV genome is organized into ten discrete double-stranded RNA molecules (S1-S10) which have been suggested to follow a sequential packaging pathway from smallest to largest segment during virus capsid assembly. To substantiate and extend these studies, we have investigated the RNA sorting and packaging mechanisms with a new experimental approach using inhibitory oligonucleotides. Putative packaging signals present in the 3'untranslated regions of BTV segments were targeted by a number of nuclease resistant oligoribonucleotides (ORNs) and their effects on virus replication in cell culture were assessed. ORNs complementary to the 3' UTR of BTV RNAs significantly inhibited virus replication without affecting protein synthesis. Same ORNs were found to inhibit complex formation when added to a novel RNA-RNA interaction assay which measured the formation of supramolecular complexes between and among different RNA segments. ORNs targeting the 3'UTR of BTV segment 10, the smallest RNA segment, were shown to be the most potent and deletions or substitution mutations of the targeted sequences diminished the RNA complexes and abolished the recovery of viable viruses using reverse genetics. Cell-free capsid assembly/RNA packaging assay also confirmed that the inhibitory ORNs could interfere with RNA packaging and further substitution mutations within the putative RNA packaging sequence have identified the recognition sequence concerned. Exchange of 3'UTR between segments have further demonstrated that RNA recognition was segment specific, most likely acting as part of the secondary structure of the entire genomic segment. Our data confirm that genome packaging in this segmented dsRNA virus occurs via the formation of supramolecular complexes formed by the interaction of specific sequences located in the 3' UTRs. Additionally, the inhibition of packaging in-trans with inhibitory ORNs suggests this that interaction is a bona fide target for the design of compounds with antiviral activity.

Tracking and data relay satellite system (TDRSS) capabilities

NASA Astrophysics Data System (ADS)

Spearing, R. E.

1985-10-01

The Tracking and Data Relay Satellite System (TDRSS) is the latest implementation to tracking and data acquisition network for near-earth orbiting satellite support designed to meet the requirements of the current and projected (to the year 2000) satellite user community. The TDRSS consists of a space segment (SS) and a ground segment (GS) that fit within NASA's Space Network (SN) complex controlled at the Goddard Space Flight Center. The SS currently employs a single satellite, TDRS-1, with two additional satellites to be deployed in January 1986 and July 1986. The GS contains the communications and equipment required to manage the three TDR satellites and to transmit and receive information to and from TDRSS user satellites. Diagrams and tables illustrating the TDRSS signal characteristics, the situation of TDRSS within the SN, the SN operations and element interrelationships, as well as future plans for new missions are included.

Tracking and data relay satellite system (TDRSS) capabilities

NASA Technical Reports Server (NTRS)

Spearing, R. E.

1985-01-01

The Tracking and Data Relay Satellite System (TDRSS) is the latest implementation to tracking and data acquisition network for near-earth orbiting satellite support designed to meet the requirements of the current and projected (to the year 2000) satellite user community. The TDRSS consists of a space segment (SS) and a ground segment (GS) that fit within NASA's Space Network (SN) complex controlled at the Goddard Space Flight Center. The SS currently employs a single satellite, TDRS-1, with two additional satellites to be deployed in January 1986 and July 1986. The GS contains the communications and equipment required to manage the three TDR satellites and to transmit and receive information to and from TDRSS user satellites. Diagrams and tables illustrating the TDRSS signal characteristics, the situation of TDRSS within the SN, the SN operations and element interrelationships, as well as future plans for new missions are included.

SU-E-J-142: Performance Study of Automatic Image-Segmentation Algorithms in Motion Tracking Via MR-IGRT

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

Feng, Y; Olsen, J.; Parikh, P.

2014-06-01

Purpose: Evaluate commonly used segmentation algorithms on a commercially available real-time MR image guided radiotherapy (MR-IGRT) system (ViewRay), compare the strengths and weaknesses of each method, with the purpose of improving motion tracking for more accurate radiotherapy. Methods: MR motion images of bladder, kidney, duodenum, and liver tumor were acquired for three patients using a commercial on-board MR imaging system and an imaging protocol used during MR-IGRT. A series of 40 frames were selected for each case to cover at least 3 respiratory cycles. Thresholding, Canny edge detection, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE),more » along with the ViewRay treatment planning and delivery system (TPDS) were included in the comparisons. To evaluate the segmentation results, an expert manual contouring of the organs or tumor from a physician was used as a ground-truth. Metrics value of sensitivity, specificity, Jaccard similarity, and Dice coefficient were computed for comparison. Results: In the segmentation of single image frame, all methods successfully segmented the bladder and kidney, but only FKM, KHM and TPDS were able to segment the liver tumor and the duodenum. For segmenting motion image series, the TPDS method had the highest sensitivity, Jarccard, and Dice coefficients in segmenting bladder and kidney, while FKM and KHM had a slightly higher specificity. A similar pattern was observed when segmenting the liver tumor and the duodenum. The Canny method is not suitable for consistently segmenting motion frames in an automated process, while thresholding and RD-LSE cannot consistently segment a liver tumor and the duodenum. Conclusion: The study compared six different segmentation methods and showed the effectiveness of the ViewRay TPDS algorithm in segmenting motion images during MR-IGRT. Future studies include a selection of conformal segmentation methods based on image/organ-specific information, different filtering methods and their influences on the segmentation results. Parag Parikh receives research grant from ViewRay. Sasa Mutic has consulting and research agreements with ViewRay. Yanle Hu receives travel reimbursement from ViewRay. Iwan Kawrakow and James Dempsey are ViewRay employees.« less

Flexible methods for segmentation evaluation: results from CT-based luggage screening.

PubMed

Karimi, Seemeen; Jiang, Xiaoqian; Cosman, Pamela; Martz, Harry

2014-01-01

Imaging systems used in aviation security include segmentation algorithms in an automatic threat recognition pipeline. The segmentation algorithms evolve in response to emerging threats and changing performance requirements. Analysis of segmentation algorithms' behavior, including the nature of errors and feature recovery, facilitates their development. However, evaluation methods from the literature provide limited characterization of the segmentation algorithms. To develop segmentation evaluation methods that measure systematic errors such as oversegmentation and undersegmentation, outliers, and overall errors. The methods must measure feature recovery and allow us to prioritize segments. We developed two complementary evaluation methods using statistical techniques and information theory. We also created a semi-automatic method to define ground truth from 3D images. We applied our methods to evaluate five segmentation algorithms developed for CT luggage screening. We validated our methods with synthetic problems and an observer evaluation. Both methods selected the same best segmentation algorithm. Human evaluation confirmed the findings. The measurement of systematic errors and prioritization helped in understanding the behavior of each segmentation algorithm. Our evaluation methods allow us to measure and explain the accuracy of segmentation algorithms.

Timepix Device Efficiency for Pattern Recognition of Tracks Generated by Ionizing Radiation

NASA Astrophysics Data System (ADS)

Leroy, Claude; Asbah, Nedaa; Gagnon, Louis-Guilaume; Larochelle, Jean-Simon; Pospisil, Stanislav; Soueid, Paul

2014-06-01

A hybrid silicon pixelated TIMEPIX detector (256 × 256 pixels with 55 μm pitch) operated in Time Over Threshold (TOT) mode was exposed to radioactive sources (241Am, 106Ru, 137Cs), protons and alpha-particles after Rutherford Backscattering on a thin gold foil of protons and alpha-particles beams delivered by the Tandem Accelerator of Montreal University. Measurements were also performed with different mixed radiation fields of heavy charged particles (protons and alpha-particles), photons and electrons produced by simultaneous exposure of TIMEPIX to the radioactive sources and to protons beams on top of the radioactive sources. All measurements were performed in vacuum. The TOT mode of operation has allowed the direct measurement of the energy deposited in each pixel. The efficiency of track recognition with this device was tested by comparing the experimental activities (determined from number of tracks measurements) of the radioactive sources with their expected activities. The efficiency of track recognition of incident protons and alpha-particles of different energies as a function of the incidence angle was measured. The operation of TIMEPIX in TOT mode has allowed a 3D mapping of the charge sharing effect in the whole volume of the silicon sensor. The effect of the bias voltage on charge sharing was investigated as the level of charge sharing is related to the local profile of the electric field in the sensor. The results of the present measurements demonstrate the TIMEPIX capability of differentiating between different types of particles species from mixed radiation fields and measuring their energy deposition. Single track analysis gives a good precision (significantly better than the 55 μm size of one detector pixel) on the coordinates of the impact point of protons interacting in the TIMEPIX silicon layer.

A Suggested Automated Branch Program for Foreign Languages.

ERIC Educational Resources Information Center

Barrutia, Richard

1964-01-01

Completely automated and operated by student feedback, this program teaches and tests foreign language recognition and retention, gives repeated audiolingual practice on model structures, and allows the student to tailor the program to his individual needs. The program is recorded on four tape tracks (track 1 for the most correct answer, etc.).…

Pattern Recognition Analysis of Age-Related Retinal Ganglion Cell Signatures in the Human Eye

PubMed Central

Yoshioka, Nayuta; Zangerl, Barbara; Nivison-Smith, Lisa; Khuu, Sieu K.; Jones, Bryan W.; Pfeiffer, Rebecca L.; Marc, Robert E.; Kalloniatis, Michael

2017-01-01

Purpose To characterize macular ganglion cell layer (GCL) changes with age and provide a framework to assess changes in ocular disease. This study used data clustering to analyze macular GCL patterns from optical coherence tomography (OCT) in a large cohort of subjects without ocular disease. Methods Single eyes of 201 patients evaluated at the Centre for Eye Health (Sydney, Australia) were retrospectively enrolled (age range, 20–85); 8 × 8 grid locations obtained from Spectralis OCT macular scans were analyzed with unsupervised classification into statistically separable classes sharing common GCL thickness and change with age. The resulting classes and gridwise data were fitted with linear and segmented linear regression curves. Additionally, normalized data were analyzed to determine regression as a percentage. Accuracy of each model was examined through comparison of predicted 50-year-old equivalent macular GCL thickness for the entire cohort to a true 50-year-old reference cohort. Results Pattern recognition clustered GCL thickness across the macula into five to eight spatially concentric classes. F-test demonstrated segmented linear regression to be the most appropriate model for macular GCL change. The pattern recognition–derived and normalized model revealed less difference between the predicted macular GCL thickness and the reference cohort (average ± SD 0.19 ± 0.92 and −0.30 ± 0.61 μm) than a gridwise model (average ± SD 0.62 ± 1.43 μm). Conclusions Pattern recognition successfully identified statistically separable macular areas that undergo a segmented linear reduction with age. This regression model better predicted macular GCL thickness. The various unique spatial patterns revealed by pattern recognition combined with core GCL thickness data provide a framework to analyze GCL loss in ocular disease. PMID:28632847

Natural user interface as a supplement of the holographic Raman tweezers

NASA Astrophysics Data System (ADS)

Tomori, Zoltan; Kanka, Jan; Kesa, Peter; Jakl, Petr; Sery, Mojmir; Bernatova, Silvie; Antalik, Marian; Zemánek, Pavel

2014-09-01

Holographic Raman tweezers (HRT) manipulates with microobjects by controlling the positions of multiple optical traps via the mouse or joystick. Several attempts have appeared recently to exploit touch tablets, 2D cameras or Kinect game console instead. We proposed a multimodal "Natural User Interface" (NUI) approach integrating hands tracking, gestures recognition, eye tracking and speech recognition. For this purpose we exploited "Leap Motion" and "MyGaze" low-cost sensors and a simple speech recognition program "Tazti". We developed own NUI software which processes signals from the sensors and sends the control commands to HRT which subsequently controls the positions of trapping beams, micropositioning stage and the acquisition system of Raman spectra. System allows various modes of operation proper for specific tasks. Virtual tools (called "pin" and "tweezers") serving for the manipulation with particles are displayed on the transparent "overlay" window above the live camera image. Eye tracker identifies the position of the observed particle and uses it for the autofocus. Laser trap manipulation navigated by the dominant hand can be combined with the gestures recognition of the secondary hand. Speech commands recognition is useful if both hands are busy. Proposed methods make manual control of HRT more efficient and they are also a good platform for its future semi-automated and fully automated work.

A Novel Feature-Tracking Echocardiographic Method for the Quantitation of Regional Myocardial Function

PubMed Central

Pirat, Bahar; Khoury, Dirar S.; Hartley, Craig J.; Tiller, Les; Rao, Liyun; Schulz, Daryl G.; Nagueh, Sherif F.; Zoghbi, William A.

2012-01-01

Objectives The aim of this study was to validate a novel, angle-independent, feature-tracking method for the echocardiographic quantitation of regional function. Background A new echocardiographic method, Velocity Vector Imaging (VVI) (syngo Velocity Vector Imaging technology, Siemens Medical Solutions, Ultrasound Division, Mountain View, California), has been introduced, based on feature tracking—incorporating speckle and endocardial border tracking, that allows the quantitation of endocardial strain, strain rate (SR), and velocity. Methods Seven dogs were studied during baseline, and various interventions causing alterations in regional function: dobutamine, 5-min coronary occlusion with reperfusion up to 1 h, followed by dobutamine and esmolol infusions. Echocardiographic images were acquired from short- and long-axis views of the left ventricle. Segment-length sonomicrometry crystals were used as the reference method. Results Changes in systolic strain in ischemic segments were tracked well with VVI during the different states of regional function. There was a good correlation between circumferential and longitudinal systolic strain by VVI and sonomicrometry (r = 0.88 and r = 0.83, respectively, p < 0.001). Strain measurements in the nonischemic basal segments also demonstrated a significant correlation between the 2 methods (r = 0.65, p < 0.001). Similarly, a significant relation was observed for circumferential and longitudinal SR between the 2 methods (r = 0.94, p < 0.001 and r = 0.90, p < 0.001, respectively). The endocardial velocity relation to changes in strain by sonomicrometry was weaker owing to significant cardiac translation. Conclusions Velocity Vector Imaging, a new feature-tracking method, can accurately assess regional myocardial function at the endocardial level and is a promising clinical tool for the simultaneous quantification of regional and global myocardial function. PMID:18261685

Resolving occlusion and segmentation errors in multiple video object tracking

NASA Astrophysics Data System (ADS)

Cheng, Hsu-Yung; Hwang, Jenq-Neng

2009-02-01

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

A real-time tracking system of infrared dim and small target based on FPGA and DSP

NASA Astrophysics Data System (ADS)

Rong, Sheng-hui; Zhou, Hui-xin; Qin, Han-lin; Wang, Bing-jian; Qian, Kun

2014-11-01

A core technology in the infrared warning system is the detection tracking of dim and small targets with complicated background. Consequently, running the detection algorithm on the hardware platform has highly practical value in the military field. In this paper, a real-time detection tracking system of infrared dim and small target which is used FPGA (Field Programmable Gate Array) and DSP (Digital Signal Processor) as the core was designed and the corresponding detection tracking algorithm and the signal flow is elaborated. At the first stage, the FPGA obtain the infrared image sequence from the sensor, then it suppresses background clutter by mathematical morphology method and enhances the target intensity by Laplacian of Gaussian operator. At the second stage, the DSP obtain both the original image and the filtered image form the FPGA via the video port. Then it segments the target from the filtered image by an adaptive threshold segmentation method and gets rid of false target by pipeline filter. Experimental results show that our system can achieve higher detection rate and lower false alarm rate.

Simulation of the Formation of DNA Double Strand Breaks and Chromosome Aberrations in Irradiated Cells

NASA Technical Reports Server (NTRS)

Plante, Ianik; Ponomarev, Artem L.; Wu, Honglu; Blattnig, Steve; George, Kerry

2014-01-01

The formation of DNA double-strand breaks (DSBs) and chromosome aberrations is an important consequence of ionizing radiation. To simulate DNA double-strand breaks and the formation of chromosome aberrations, we have recently merged the codes RITRACKS (Relativistic Ion Tracks) and NASARTI (NASA Radiation Track Image). The program RITRACKS is a stochastic code developed to simulate detailed event-by-event radiation track structure: [1] This code is used to calculate the dose in voxels of 20 nm, in a volume containing simulated chromosomes, [2] The number of tracks in the volume is calculated for each simulation by sampling a Poisson distribution, with the distribution parameter obtained from the irradiation dose, ion type and energy. The program NASARTI generates the chromosomes present in a cell nucleus by random walks of 20 nm, corresponding to the size of the dose voxels, [3] The generated chromosomes are located within domains which may intertwine, and [4] Each segment of the random walks corresponds to approx. 2,000 DNA base pairs. NASARTI uses pre-calculated dose at each voxel to calculate the probability of DNA damage at each random walk segment. Using the location of double-strand breaks, possible rejoining between damaged segments is evaluated. This yields various types of chromosomes aberrations, including deletions, inversions, exchanges, etc. By performing the calculations using various types of radiations, it will be possible to obtain relative biological effectiveness (RBE) values for several types of chromosome aberrations.

Simultaneous maxillary distraction osteogenesis using a twin-track distraction device combined with alveolar bone grafting in cleft patients: preliminary report of a technique.

PubMed

Suzuki, Eduardo Yugo; Watanabe, Masayo; Buranastidporn, Boonsiva; Baba, Yoshiyuki; Ohyama, Kimie; Ishii, Masatoshi

2006-01-01

The simultaneous use of cleft reduction and maxillary advancement by distraction osteogenesis has not been applied routinely because of the difficulty in three-dimensional control and stabilization of the transported segments. This report describes a new approach of simultaneous bilateral alveolar cleft reduction and maxillary advancement by distraction osteogenesis combined with autogenous bone grafting. A custom-made Twin-Track device was used to allow bilateral alveolar cleft closure combined with simultaneous maxillary advancement, using distraction osteogenesis and a rigid external distraction system in a bilateral cleft lip and palate patient. After a maxillary Le Fort I osteotomy, autogenous iliac bone graft was placed in the cleft spaces before suturing. A latency period of six days was observed before activation. The rate of activation was one mm/d for the maxillary advancement and 0.5 mm/d for the segmental transport. Accordingly, the concave facial appearance was improved with acceptable occlusion, and complete bilateral cleft closure was attained. No adjustments were necessary to the vector of the transported segments during the activation and no complications were observed. The proposed Twin-Track device, based on the concept of track-guided bone transport, permitted three-dimensional control over the distraction processes allowing simultaneous cleft closure, maxillary distraction, and autogenous bone grafting. The combined simultaneous approach is extremely advantageous in correcting severe deformities, reducing the number of surgical interventions and, consequently, the total treatment time.

Remote Video Monitor of Vehicles in Cooperative Information Platform

NASA Astrophysics Data System (ADS)

Qin, Guofeng; Wang, Xiaoguo; Wang, Li; Li, Yang; Li, Qiyan

Detection of vehicles plays an important role in the area of the modern intelligent traffic management. And the pattern recognition is a hot issue in the area of computer vision. An auto- recognition system in cooperative information platform is studied. In the cooperative platform, 3G wireless network, including GPS, GPRS (CDMA), Internet (Intranet), remote video monitor and M-DMB networks are integrated. The remote video information can be taken from the terminals and sent to the cooperative platform, then detected by the auto-recognition system. The images are pretreated and segmented, including feature extraction, template matching and pattern recognition. The system identifies different models and gets vehicular traffic statistics. Finally, the implementation of the system is introduced.

Finger tips detection for two handed gesture recognition

NASA Astrophysics Data System (ADS)

Bhuyan, M. K.; Kar, Mithun Kumar; Neog, Debanga Raj

2011-10-01

In this paper, a novel algorithm is proposed for fingertips detection in view of two-handed static hand pose recognition. In our method, finger tips of both hands are detected after detecting hand regions by skin color-based segmentation. At first, the face is removed in the image by using Haar classifier and subsequently, the regions corresponding to the gesturing hands are isolated by a region labeling technique. Next, the key geometric features characterizing gesturing hands are extracted for two hands. Finally, for all possible/allowable finger movements, a probabilistic model is developed for pose recognition. Proposed method can be employed in a variety of applications like sign language recognition and human-robot-interactions etc.

The Role of Clarity and Blur in Guiding Visual Attention in Photographs

ERIC Educational Resources Information Center

Enns, James T.; MacDonald, Sarah C.

2013-01-01

Visual artists and photographers believe that a viewer's gaze can be guided by selective use of image clarity and blur, but there is little systematic research. In this study, participants performed several eye-tracking tasks with the same naturalistic photographs, including recognition memory for the entire photo, as well as recognition memory…

Video-based noncooperative iris image segmentation.

PubMed

Du, Yingzi; Arslanturk, Emrah; Zhou, Zhi; Belcher, Craig

2011-02-01

In this paper, we propose a video-based noncooperative iris image segmentation scheme that incorporates a quality filter to quickly eliminate images without an eye, employs a coarse-to-fine segmentation scheme to improve the overall efficiency, uses a direct least squares fitting of ellipses method to model the deformed pupil and limbic boundaries, and develops a window gradient-based method to remove noise in the iris region. A remote iris acquisition system is set up to collect noncooperative iris video images. An objective method is used to quantitatively evaluate the accuracy of the segmentation results. The experimental results demonstrate the effectiveness of this method. The proposed method would make noncooperative iris recognition or iris surveillance possible.

Track vertex reconstruction with neural networks at the first level trigger of Belle II

NASA Astrophysics Data System (ADS)

Neuhaus, Sara; Skambraks, Sebastian; Kiesling, Christian

2017-08-01

The track trigger is one of the main components of the Belle II first level trigger, taking input from the Central Drift Chamber (CDC). It consists of several stages, first combining hits to track segments, followed by a 2D track finding in the transverse plane and finally a 3D track reconstruction. The results of the track trigger are the track multiplicity, the momentum vector of each track and the longitudinal displacement of the origin or production vertex of each track ("z-vertex"). The latter allows to reject background tracks from outside of the interaction region and thus to suppress a large fraction of the machine background. This contribution focuses on the track finding stage using Hough transforms and on the z-vertex reconstruction with neural networks. We describe the algorithms and show performance studies on simulated events.

Tracking Activities in Complex Settings Using Smart Environment Technologies.

PubMed

Singla, Geetika; Cook, Diane J; Schmitter-Edgecombe, Maureen

2009-01-01

The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. A primary challenge that needs to be tackled to meet this need is the ability to recognize and track functional activities that people perform in their own homes and everyday settings. In this paper we look at approaches to perform real-time recognition of Activities of Daily Living. We enhance other related research efforts to develop approaches that are effective when activities are interrupted and interleaved. To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.

Research on target tracking in coal mine based on optical flow method

NASA Astrophysics Data System (ADS)

Xue, Hongye; Xiao, Qingwei

2015-03-01

To recognize, track and count the bolting machine in coal mine video images, a real-time target tracking method based on the Lucas-Kanade sparse optical flow is proposed in this paper. In the method, we judge whether the moving target deviate from its trajectory, predicate and correct the position of the moving target. The method solves the problem of failure to track the target or lose the target because of the weak light, uneven illumination and blocking. Using the VC++ platform and Opencv lib we complete the recognition and tracking. The validity of the method is verified by the result of the experiment.

Tier-Adjacency Is Not a Necessary Condition for Learning Phonotactic Dependencies

ERIC Educational Resources Information Center

Koo, Hahn; Callahan, Lydia

2012-01-01

One hypothesis raised by Newport and Aslin to explain how speakers learn dependencies between nonadjacent phonemes is that speakers track bigram probabilities between two segments that are adjacent to each other within a tier of their own. The hypothesis predicts that a dependency between segments separated from each other at the tier level cannot…

The Surprising Power of Statistical Learning: When Fragment Knowledge Leads to False Memories of Unheard Words

ERIC Educational Resources Information Center

Endress, Ansgar D.; Mehler, Jacques

2009-01-01

Word-segmentation, that is, the extraction of words from fluent speech, is one of the first problems language learners have to master. It is generally believed that statistical processes, in particular those tracking "transitional probabilities" (TPs), are important to word-segmentation. However, there is evidence that word forms are stored in…

Free-Form Region Description with Second-Order Pooling.

PubMed

Carreira, João; Caseiro, Rui; Batista, Jorge; Sminchisescu, Cristian

2015-06-01

Semantic segmentation and object detection are nowadays dominated by methods operating on regions obtained as a result of a bottom-up grouping process (segmentation) but use feature extractors developed for recognition on fixed-form (e.g. rectangular) patches, with full images as a special case. This is most likely suboptimal. In this paper we focus on feature extraction and description over free-form regions and study the relationship with their fixed-form counterparts. Our main contributions are novel pooling techniques that capture the second-order statistics of local descriptors inside such free-form regions. We introduce second-order generalizations of average and max-pooling that together with appropriate non-linearities, derived from the mathematical structure of their embedding space, lead to state-of-the-art recognition performance in semantic segmentation experiments without any type of local feature coding. In contrast, we show that codebook-based local feature coding is more important when feature extraction is constrained to operate over regions that include both foreground and large portions of the background, as typical in image classification settings, whereas for high-accuracy localization setups, second-order pooling over free-form regions produces results superior to those of the winning systems in the contemporary semantic segmentation challenges, with models that are much faster in both training and testing.

Real-time detecting and tracking ball with OpenCV and Kinect

NASA Astrophysics Data System (ADS)

Osiecki, Tomasz; Jankowski, Stanislaw

2016-09-01

This paper presents a way to detect and track ball with using the OpenCV and Kinect. Object and people recognition, tracking are more and more popular topics nowadays. Described solution makes it possible to detect ball based on the range, which is set by the user and capture information about ball position in three dimensions. It can be store in the computer and use for example to display trajectory of the ball.

Real Time Target Tracking in a Phantom Using Ultrasonic Imaging

NASA Astrophysics Data System (ADS)

Xiao, X.; Corner, G.; Huang, Z.

In this paper we present a real-time ultrasound image guidance method suitable for tracking the motion of tumors. A 2D ultrasound based motion tracking system was evaluated. A robot was used to control the focused ultrasound and position it at the target that has been segmented from a real-time ultrasound video. Tracking accuracy and precision were investigated using a lesion mimicking phantom. Experiments have been conducted and results show sufficient efficiency of the image guidance algorithm. This work could be developed as the foundation for combining the real time ultrasound imaging tracking and MRI thermometry monitoring non-invasive surgery.

Efficient graph-cut tattoo segmentation

NASA Astrophysics Data System (ADS)

Kim, Joonsoo; Parra, Albert; Li, He; Delp, Edward J.

2015-03-01

Law enforcement is interested in exploiting tattoos as an information source to identify, track and prevent gang-related crimes. Many tattoo image retrieval systems have been described. In a retrieval system tattoo segmentation is an important step for retrieval accuracy since segmentation removes background information in a tattoo image. Existing segmentation methods do not extract the tattoo very well when the background includes textures and color similar to skin tones. In this paper we describe a tattoo segmentation approach by determining skin pixels in regions near the tattoo. In these regions graph-cut segmentation using a skin color model and a visual saliency map is used to find skin pixels. After segmentation we determine which set of skin pixels are connected with each other that form a closed contour including a tattoo. The regions surrounded by the closed contours are considered tattoo regions. Our method segments tattoos well when the background includes textures and color similar to skin.

Effects of wrist tendon vibration on arm tracking in people poststroke.

PubMed

Conrad, Megan O; Scheidt, Robert A; Schmit, Brian D

2011-09-01

The goal of this study was to evaluate the influence of wrist tendon vibration on a multijoint elbow/shoulder tracking task. We hypothesized that tendon vibration applied at the wrist musculature would improve upper arm tracking performance in chronic stroke survivors through increased, Ia-afferent feedback to the central nervous system (CNS). To test this hypothesis, 10 chronic stroke and 5 neurologically intact subjects grasped the handle of a planar robot as they tracked a target through a horizontal figure-8 pattern. A total of 36 trials were completed by each subject. During the middle trials, 70-Hz tendon vibration was applied at the wrist flexor tendons. Position, velocity, and electromyography data were evaluated to compare the quality of arm movements before, during, and after trials with concurrent vibration. Despite tracking a target that moved at a constant velocity, hand trajectories appeared to be segmented, displaying alternating intervals of acceleration and deceleration. Segments were identifiable in tangential velocity data as single-peaked, bell-shaped speed pulses. When tendon vibration was applied at the wrist musculature, stroke subjects experienced improved tracking performance in that hand path lengths and peak speed variability decreased, whereas movement smoothness increased. These performance improvements were accompanied by decreases in the muscle activity during movement. Possible mechanisms behind improved movement control in response to tendon vibration may include improved sensorimotor integration or improved cortical modulation of spinal reflex activity.

Effects of wrist tendon vibration on arm tracking in people poststroke

PubMed Central

Conrad, Megan O.; Scheidt, Robert A.

2011-01-01

The goal of this study was to evaluate the influence of wrist tendon vibration on a multijoint elbow/shoulder tracking task. We hypothesized that tendon vibration applied at the wrist musculature would improve upper arm tracking performance in chronic stroke survivors through increased, Ia-afferent feedback to the central nervous system (CNS). To test this hypothesis, 10 chronic stroke and 5 neurologically intact subjects grasped the handle of a planar robot as they tracked a target through a horizontal figure-8 pattern. A total of 36 trials were completed by each subject. During the middle trials, 70-Hz tendon vibration was applied at the wrist flexor tendons. Position, velocity, and electromyography data were evaluated to compare the quality of arm movements before, during, and after trials with concurrent vibration. Despite tracking a target that moved at a constant velocity, hand trajectories appeared to be segmented, displaying alternating intervals of acceleration and deceleration. Segments were identifiable in tangential velocity data as single-peaked, bell-shaped speed pulses. When tendon vibration was applied at the wrist musculature, stroke subjects experienced improved tracking performance in that hand path lengths and peak speed variability decreased, whereas movement smoothness increased. These performance improvements were accompanied by decreases in the muscle activity during movement. Possible mechanisms behind improved movement control in response to tendon vibration may include improved sensorimotor integration or improved cortical modulation of spinal reflex activity. PMID:21697444

An Integrated Wireless Wearable Sensor System for Posture Recognition and Indoor Localization.

PubMed

Huang, Jian; Yu, Xiaoqiang; Wang, Yuan; Xiao, Xiling

2016-10-31

In order to provide better monitoring for the elderly or patients, we developed an integrated wireless wearable sensor system that can realize posture recognition and indoor localization in real time. Five designed sensor nodes which are respectively fixed on lower limbs and a standard Kalman filter are used to acquire basic attitude data. After the attitude angles of five body segments (two thighs, two shanks and the waist) are obtained, the pitch angles of the left thigh and waist are used to realize posture recognition. Based on all these attitude angles of body segments, we can also calculate the coordinates of six lower limb joints (two hip joints, two knee joints and two ankle joints). Then, a novel relative localization algorithm based on step length is proposed to realize the indoor localization of the user. Several sparsely distributed active Radio Frequency Identification (RFID) tags are used to correct the accumulative error in the relative localization algorithm and a set-membership filter is applied to realize the data fusion. The experimental results verify the effectiveness of the proposed algorithms.

An Integrated Wireless Wearable Sensor System for Posture Recognition and Indoor Localization

PubMed Central

Huang, Jian; Yu, Xiaoqiang; Wang, Yuan; Xiao, Xiling

2016-01-01

In order to provide better monitoring for the elderly or patients, we developed an integrated wireless wearable sensor system that can realize posture recognition and indoor localization in real time. Five designed sensor nodes which are respectively fixed on lower limbs and a standard Kalman filter are used to acquire basic attitude data. After the attitude angles of five body segments (two thighs, two shanks and the waist) are obtained, the pitch angles of the left thigh and waist are used to realize posture recognition. Based on all these attitude angles of body segments, we can also calculate the coordinates of six lower limb joints (two hip joints, two knee joints and two ankle joints). Then, a novel relative localization algorithm based on step length is proposed to realize the indoor localization of the user. Several sparsely distributed active Radio Frequency Identification (RFID) tags are used to correct the accumulative error in the relative localization algorithm and a set-membership filter is applied to realize the data fusion. The experimental results verify the effectiveness of the proposed algorithms. PMID:27809230

Fingerprint recognition system by use of graph matching

NASA Astrophysics Data System (ADS)

Shen, Wei; Shen, Jun; Zheng, Huicheng

2001-09-01

Fingerprint recognition is an important subject in biometrics to identify or verify persons by physiological characteristics, and has found wide applications in different domains. In the present paper, we present a finger recognition system that combines singular points and structures. The principal steps of processing in our system are: preprocessing and ridge segmentation, singular point extraction and selection, graph representation, and finger recognition by graphs matching. Our fingerprint recognition system is implemented and tested for many fingerprint images and the experimental result are satisfactory. Different techniques are used in our system, such as fast calculation of orientation field, local fuzzy dynamical thresholding, algebraic analysis of connections and fingerprints representation and matching by graphs. Wed find that for fingerprint database that is not very large, the recognition rate is very high even without using a prior coarse category classification. This system works well for both one-to-few and one-to-many problems.

Review of chart recognition in document images

NASA Astrophysics Data System (ADS)

Liu, Yan; Lu, Xiaoqing; Qin, Yeyang; Tang, Zhi; Xu, Jianbo

2013-01-01

As an effective information transmitting way, chart is widely used to represent scientific statistics datum in books, research papers, newspapers etc. Though textual information is still the major source of data, there has been an increasing trend of introducing graphs, pictures, and figures into the information pool. Text recognition techniques for documents have been accomplished using optical character recognition (OCR) software. Chart recognition techniques as a necessary supplement of OCR for document images are still an unsolved problem due to the great subjectiveness and variety of charts styles. This paper reviews the development process of chart recognition techniques in the past decades and presents the focuses of current researches. The whole process of chart recognition is presented systematically, which mainly includes three parts: chart segmentation, chart classification, and chart Interpretation. In each part, the latest research work is introduced. In the last, the paper concludes with a summary and promising future research direction.

Design and development of an ancient Chinese document recognition system

NASA Astrophysics Data System (ADS)

Peng, Liangrui; Xiu, Pingping; Ding, Xiaoqing

2003-12-01

The digitization of ancient Chinese documents presents new challenges to OCR (Optical Character Recognition) research field due to the large character set of ancient Chinese characters, variant font types, and versatile document layout styles, as these documents are historical reflections to the thousands of years of Chinese civilization. After analyzing the general characteristics of ancient Chinese documents, we present a solution for recognition of ancient Chinese documents with regular font-types and layout-styles. Based on the previous work on multilingual OCR in TH-OCR system, we focus on the design and development of two key technologies which include character recognition and page segmentation. Experimental results show that the developed character recognition kernel of 19,635 Chinese characters outperforms our original traditional Chinese recognition kernel; Benchmarked test on printed ancient Chinese books proves that the proposed system is effective for regular ancient Chinese documents.

Handwritten Word Recognition Using Multi-view Analysis

NASA Astrophysics Data System (ADS)

de Oliveira, J. J.; de A. Freitas, C. O.; de Carvalho, J. M.; Sabourin, R.

This paper brings a contribution to the problem of efficiently recognizing handwritten words from a limited size lexicon. For that, a multiple classifier system has been developed that analyzes the words from three different approximation levels, in order to get a computational approach inspired on the human reading process. For each approximation level a three-module architecture composed of a zoning mechanism (pseudo-segmenter), a feature extractor and a classifier is defined. The proposed application is the recognition of the Portuguese handwritten names of the months, for which a best recognition rate of 97.7% was obtained, using classifier combination.

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).

Ins and Outs of Multipartite Positive-Strand RNA Plant Viruses: Packaging versus Systemic Spread

PubMed Central

Dall’Ara, Mattia; Ratti, Claudio; Bouzoubaa, Salah E.; Gilmer, David

2016-01-01

Viruses possessing a non-segmented genome require a specific recognition of their nucleic acid to ensure its protection in a capsid. A similar feature exists for viruses having a segmented genome, usually consisting of viral genomic segments joined together into one viral entity. While this appears as a rule for animal viruses, the majority of segmented plant viruses package their genomic segments individually. To ensure a productive infection, all viral particles and thereby all segments have to be present in the same cell. Progression of the virus within the plant requires as well a concerted genome preservation to avoid loss of function. In this review, we will discuss the “life aspects” of chosen phytoviruses and argue for the existence of RNA-RNA interactions that drive the preservation of viral genome integrity while the virus progresses in the plant. PMID:27548199

Position estimation and driving of an autonomous vehicle by monocular vision

NASA Astrophysics Data System (ADS)

Hanan, Jay C.; Kayathi, Pavan; Hughlett, Casey L.

2007-04-01

Automatic adaptive tracking in real-time for target recognition provided autonomous control of a scale model electric truck. The two-wheel drive truck was modified as an autonomous rover test-bed for vision based guidance and navigation. Methods were implemented to monitor tracking error and ensure a safe, accurate arrival at the intended science target. Some methods are situation independent relying only on the confidence error of the target recognition algorithm. Other methods take advantage of the scenario of combined motion and tracking to filter out anomalies. In either case, only a single calibrated camera was needed for position estimation. Results from real-time autonomous driving tests on the JPL simulated Mars yard are presented. Recognition error was often situation dependent. For the rover case, the background was in motion and may be characterized to provide visual cues on rover travel such as rate, pitch, roll, and distance to objects of interest or hazards. Objects in the scene may be used as landmarks, or waypoints, for such estimations. As objects are approached, their scale increases and their orientation may change. In addition, particularly on rough terrain, these orientation and scale changes may be unpredictable. Feature extraction combined with the neural network algorithm was successful in providing visual odometry in the simulated Mars environment.

Object Recognition using Feature- and Color-Based Methods

NASA Technical Reports Server (NTRS)

Duong, Tuan; Duong, Vu; Stubberud, Allen

2008-01-01

An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognition system. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power.

Activity recognition using Video Event Segmentation with Text (VEST)

NASA Astrophysics Data System (ADS)

Holloway, Hillary; Jones, Eric K.; Kaluzniacki, Andrew; Blasch, Erik; Tierno, Jorge

2014-06-01

Multi-Intelligence (multi-INT) data includes video, text, and signals that require analysis by operators. Analysis methods include information fusion approaches such as filtering, correlation, and association. In this paper, we discuss the Video Event Segmentation with Text (VEST) method, which provides event boundaries of an activity to compile related message and video clips for future interest. VEST infers meaningful activities by clustering multiple streams of time-sequenced multi-INT intelligence data and derived fusion products. We discuss exemplar results that segment raw full-motion video (FMV) data by using extracted commentary message timestamps, FMV metadata, and user-defined queries.

Using Eye Tracking to Understand the Responses of Learners to Vocabulary Learning Strategy Instruction and Use

ERIC Educational Resources Information Center

Liu, Pei-Lin

2014-01-01

This study examined the influence of morphological instruction in an eye-tracking English vocabulary recognition task. Sixty-eight freshmen enrolled in an English course and received either traditional or morphological instruction for learning English vocabulary. The experimental part of the study was conducted over two-hour class periods for…

Theories of Spoken Word Recognition Deficits in Aphasia: Evidence from Eye-Tracking and Computational Modeling

ERIC Educational Resources Information Center

Mirman, Daniel; Yee, Eiling; Blumstein, Sheila E.; Magnuson, James S.

2011-01-01

We used eye-tracking to investigate lexical processing in aphasic participants by examining the fixation time course for rhyme (e.g., "carrot-parrot") and cohort (e.g., "beaker-beetle") competitors. Broca's aphasic participants exhibited larger rhyme competition effects than age-matched controls. A re-analysis of previously reported data (Yee,…

Eye-Tracking Study on Facial Emotion Recognition Tasks in Individuals with High-Functioning Autism Spectrum Disorders

ERIC Educational Resources Information Center

Tsang, Vicky

2018-01-01

The eye-tracking experiment was carried out to assess fixation duration and scan paths that individuals with and without high-functioning autism spectrum disorders employed when identifying simple and complex emotions. Participants viewed human photos of facial expressions and decided on the identification of emotion, the negative-positive emotion…

SU-E-J-168: Automated Pancreas Segmentation Based On Dynamic MRI

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

Gou, S; Rapacchi, S; Hu, P

2014-06-01

Purpose: MRI guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment 2D upper abdominal dynamic MRI is developed for this purpose. Methods: 2D coronal dynamic MR images of 2 healthy volunteers were acquired with a frame rate of 5 f/second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centers of the ROIs were annotated. These center locations were propagatedmore » to the next dynamic MRI frame. 4-neighborhood region transfer growth was performed from these initial seeds for rough segmentation. To improve the results, gradient, edge and shape constraints were applied to the ROIs before final refinement using morphological operations. Results from hGReS and 3 other automated segmentation methods using edge detection, region growth and level set were compared to manual contouring. Results: For the first patient, hGReS resulted in the organ segmentation accuracy as measure by the Dices index (0.77) for the pancreas. The accuracy was slightly superior to the level set method (0.72), and both are significantly more accurate than the edge detection (0.53) and region growth methods (0.42). For the second healthy volunteer, hGReS reliably segmented the pancreatic region, achieving a Dices index of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively, comparing to manual segmentation. Motion trajectories derived from the hGReS, level set and manual segmentation methods showed high correlation to respiratory motion calculated using a lung blood vessel as the reference while the other two methods showed substantial motion tracking errors. hGReS was 10 times faster than level set. Conclusion: We have shown the feasibility of automated segmentation of the pancreas anatomy based on dynamic MRI.« less

Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regression.

PubMed

Cherry, Kevin M; Peplinski, Brandon; Kim, Lauren; Wang, Shijun; Lu, Le; Zhang, Weidong; Liu, Jianfei; Wei, Zhuoshi; Summers, Ronald M

2015-01-01

Given the potential importance of marginal artery localization in automated registration in computed tomography colonography (CTC), we have devised a semi-automated method of marginal vessel detection employing sequential Monte Carlo tracking (also known as particle filtering tracking) by multiple cue fusion based on intensity, vesselness, organ detection, and minimum spanning tree information for poorly enhanced vessel segments. We then employed a random forest algorithm for intelligent cue fusion and decision making which achieved high sensitivity and robustness. After applying a vessel pruning procedure to the tracking results, we achieved statistically significantly improved precision compared to a baseline Hessian detection method (2.7% versus 75.2%, p<0.001). This method also showed statistically significantly improved recall rate compared to a 2-cue baseline method using fewer vessel cues (30.7% versus 67.7%, p<0.001). These results demonstrate that marginal artery localization on CTC is feasible by combining a discriminative classifier (i.e., random forest) with a sequential Monte Carlo tracking mechanism. In so doing, we present the effective application of an anatomical probability map to vessel pruning as well as a supplementary spatial coordinate system for colonic segmentation and registration when this task has been confounded by colon lumen collapse. Published by Elsevier B.V.

ESC-Track: A computer workflow for 4-D segmentation, tracking, lineage tracing and dynamic context analysis of ESCs.

PubMed

Fernández-de-Manúel, Laura; Díaz-Díaz, Covadonga; Jiménez-Carretero, Daniel; Torres, Miguel; Montoya, María C

2017-05-01

Embryonic stem cells (ESCs) can be established as permanent cell lines, and their potential to differentiate into adult tissues has led to widespread use for studying the mechanisms and dynamics of stem cell differentiation and exploring strategies for tissue repair. Imaging live ESCs during development is now feasible due to advances in optical imaging and engineering of genetically encoded fluorescent reporters; however, a major limitation is the low spatio-temporal resolution of long-term 3-D imaging required for generational and neighboring reconstructions. Here, we present the ESC-Track (ESC-T) workflow, which includes an automated cell and nuclear segmentation and tracking tool for 4-D (3-D + time) confocal image data sets as well as a manual editing tool for visual inspection and error correction. ESC-T automatically identifies cell divisions and membrane contacts for lineage tree and neighborhood reconstruction and computes quantitative features from individual cell entities, enabling analysis of fluorescence signal dynamics and tracking of cell morphology and motion. We use ESC-T to examine Myc intensity fluctuations in the context of mouse ESC (mESC) lineage and neighborhood relationships. ESC-T is a powerful tool for evaluation of the genealogical and microenvironmental cues that maintain ESC fitness.

49 CFR 213.307 - Class of track: operating speed limits.

Code of Federal Regulations, 2010 CFR

2010-10-01

... conditions are met: (1) The vehicles utilized to carry such freight are of equal dynamic performance and have... distribution and securement in the freight vehicle will not adversely affect the dynamic performance of the... addressing other safety issues presented by the system. (b) If a segment of track does not meet all of the...

Do Chinese Readers Follow the National Standard Rules for Word Segmentation during Reading?

PubMed Central

Liu, Ping-Ping; Li, Wei-Jun; Lin, Nan; Li, Xing-Shan

2013-01-01

We conducted a preliminary study to examine whether Chinese readers’ spontaneous word segmentation processing is consistent with the national standard rules of word segmentation based on the Contemporary Chinese language word segmentation specification for information processing (CCLWSSIP). Participants were asked to segment Chinese sentences into individual words according to their prior knowledge of words. The results showed that Chinese readers did not follow the segmentation rules of the CCLWSSIP, and their word segmentation processing was influenced by the syntactic categories of consecutive words. In many cases, the participants did not consider the auxiliary words, adverbs, adjectives, nouns, verbs, numerals and quantifiers as single word units. Generally, Chinese readers tended to combine function words with content words to form single word units, indicating they were inclined to chunk single words into large information units during word segmentation. Additionally, the “overextension of monosyllable words” hypothesis was tested and it might need to be corrected to some degree, implying that word length have an implicit influence on Chinese readers’ segmentation processing. Implications of these results for models of word recognition and eye movement control are discussed. PMID:23408981

Investigating an Application of Speech-to-Text Recognition: A Study on Visual Attention and Learning Behaviour

ERIC Educational Resources Information Center

Huang, Y-M.; Liu, C-J.; Shadiev, Rustam; Shen, M-H.; Hwang, W-Y.

2015-01-01

One major drawback of previous research on speech-to-text recognition (STR) is that most findings showing the effectiveness of STR for learning were based upon subjective evidence. Very few studies have used eye-tracking techniques to investigate visual attention of students on STR-generated text. Furthermore, not much attention was paid to…

English Listeners Use Suprasegmental Cues to Lexical Stress Early during Spoken-Word Recognition

ERIC Educational Resources Information Center

Jesse, Alexandra; Poellmann, Katja; Kong, Ying-Yee

2017-01-01

Purpose: We used an eye-tracking technique to investigate whether English listeners use suprasegmental information about lexical stress to speed up the recognition of spoken words in English. Method: In a visual world paradigm, 24 young English listeners followed spoken instructions to choose 1 of 4 printed referents on a computer screen (e.g.,…

Bistatic and Multistatic Radar: Surveillance, Countermeasures, and Radar Cross Sections. (Latest citations from the Aerospace Database)

NASA Technical Reports Server (NTRS)

1998-01-01

The bibliography contains citations concerning the design, development, testing, and evaluation of bistatic and multistatic radar used in surveillance and countermeasure technology. Citations discuss radar cross sections, target recognition and characteristics, ghost recognition, motion image compensation, and wavelet analysis. Stealth aircraft design, stealth target tracking, synthetic aperture radar, and space applications are examined.

Bistatic and Multistatic Radar: Surveillance, Countermeasures, and Radar Cross Sections. (Latest Citations from the Aerospace Database)

NASA Technical Reports Server (NTRS)

1997-01-01

The bibliography contains citations concerning the design, development, testing, and evaluation of bistatic and multistatic radar used in surveillance and countermeasure technology. Citations discuss radar cross sections, target recognition and characteristics, ghost recognition, motion image compensation, and wavelet analysis. Stealth aircraft design, stealth target tracking, synthetic aperture radar, and space applications are examined.

Influences of High and Low Variability on Infant Word Recognition

ERIC Educational Resources Information Center

Singh, Leher

2008-01-01

Although infants begin to encode and track novel words in fluent speech by 7.5 months, their ability to recognize words is somewhat limited at this stage. In particular, when the surface form of a word is altered, by changing the gender or affective prosody of the speaker, infants begin to falter at spoken word recognition. Given that natural…

3D automatic anatomy segmentation based on iterative graph-cut-ASM.

PubMed

Chen, Xinjian; Bagci, Ulas

2011-08-01

This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images. The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al. [Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine. The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 degrees and 0.03, and over all foot bones are about 3.5709 mm, 0.35 degrees and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and all foot bones for all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s. The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min.