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共 467 条记录，以下是121-130 订阅

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A Camera-Projector System for Robot Positioning by Visual Servoing

A Camera-Projector System for Robot Positioning by Visual Se...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： J. Pages C. Collewet F. Chaumette J. Salvi Institut d'Informàtica i Aplicacions University of Girona Girona Spain IRISA INRIA-Rennes Rennes France IRISA INRIA-Rennes Institut d'Informàtica i Aplicacions

Positioning a robot with respect to objects by using data provided by a camera is a well known technique called visual servoing. In order to perform a task, the object must exhibit visual features which can be extracted from different points of view. Then, visual servoing is object-dependent as it depends on the object appearance. Therefore, performing the positioning task is not possible in presence of nontextured objets or objets for which extracting visual features is too complex or too costly. This paper proposes a solution to tackle this limitation inherent to the current visual servoing techniques. Our proposal is based on the coded structured light approach as a reliable and fast way to solve the correspondence problem. In this case, a coded light pattern is projected providing robust visual features independently of the object appearance.

关键词： Visual servoing Feature extraction Robot sensing systems Robot vision systems Cameras Data mining Robustness computer vision Proposals Robot control

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Multiresolution Approach for Non-Contact Measurements of Arterial Pulse using Thermal Imaging

Multiresolution Approach for Non-Contact Measurements of Art...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： S.Y. Chekmenev A.A. Farag E.A. Essock Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Department of Psychological and Brain University of Louisville Louisville KY USA

This paper presents a novel, multiresolution (MR) approach for non-contact passive measurements of the arterial pulse along the arterial tree using thermal imaging of the most accessible regions of human face and neck. The method automatically detects Region/s Of Measurement (ROM) of the arterial pulse using the periodic nature of arterial thermal patterns. This ROM constitutes a skin area where the heat pulsation produced by an artery is most pronounced. The output of the algorithm is a descriptive arterial pulse waveform. To the best of our knowledge, our results are novel and have not been reported before. Some potential applications of this work are in the field of non-contact health state/ physiology monitoring, magnetic resonance imaging, smart rooms, polygraph testing, and intent identification.

关键词： Pulse measurements Image resolution Read only memory Humans Face detection Neck Skin Arteries Physiology Monitoring

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An FPGA-Based Verification Framework for Real-Time vision Systems

An FPGA-Based Verification Framework for Real-Time Vision Sy...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： G. van der Wal F. Brehm M. Piacentino J. Marakowitz E. Gudis A. Sufi J. Montante Embedded Vision Systems Sarnoff Corporation Princeton NJ USA

Field-Programmable Gate Arrays (FPGAs) have become a mainstay in the digital electronics world both for the ease of implementation as well as their inherent usefulness in incrementally refining hardware designs. When moving to an Application Specific Integrated Circuit (ASIC) or System on a Chip (SoC), verification becomes a very time consuming process, with virtually no room for error. As a result, a variety of methods have been devised to decrease the risk when creating an ASIC or SoC. We describe a hardware and software framework for testing real-time vision algorithms for lowering the uncertainty in FPGA and SoC development, while reducing the SoC verification time. The framework benefits from hardware and software verification, ease of reconfiguration for testing multiple vision algorithms, and an iterative hardware/ software co-design.

关键词： Real time systems Machine vision Hardware System-on-a-chip Field programmable gate arrays Application specific integrated circuits Software testing Software algorithms Iterative algorithms Circuit testing

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Segmentation of fMRI Data by Maximization of Region Contrast

Segmentation of fMRI Data by Maximization of Region Contrast

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： P.S. Mitra V. Gopalakrishnan R.L. McNamee Center for Biomedical Informatics University of Pittsburgh USA Departments of Pharmaceutical Sciences & Bioengineering University of Pittsburgh USA

Functional segmentation of an fMRI image is the partitioning of the image voxels into clumps that are comodulated by task-related neural activation or other processes. This grouping of voxels can be useful for exploratory analysis and for detection of signal artifacts that can confound regression-based analysis. We present a new method for functional segmentation that is based upon maximization of the contrast of an image-region with respect to its spatial neighborhood. Contrast maximization is an appealing approach since it does not require imagespecific tuning of parameters, as with many clustering approaches. With synthetic benchmark datasets, the accuracy of this Auto-threshold Contrast Enhancing Iterative Clustering (ACEIC) method compares favorably with that of Probabilistic ICA, which is a popular method used for detection of functional groupings of voxels. We also show that functional segmentation of in vivo datasets with ACEIC can provide insights into the results from conventional analysis.

关键词： Independent component analysis Image segmentation Clustering methods Size control Biomedical informatics Pharmaceuticals Biomedical engineering computer vision pattern recognition conferences

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Particle DynamicsWarping Approach for Offline Signature recognition

Particle DynamicsWarping Approach for Offline Signature Reco...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： G. Agam S. Suresh Department of Computer Science Illinois Institute of Technology Chicago IL USA

Offline signature recognition is an important form of biometric identification that can be used for various purposes. Similar to other biometric measures, signatures have inherent variability and so pose a difficult recognition problem. In this paper we explore a novel approach for reducing the variability associated with matching signatures based on curve warping. Existing techniques, such as the dynamic time warping approach, address this problem by minimizing a cost function through dynamic programming. This is by nature a one dimensional optimization process that is possible when a one dimensional parametrization of the curves is known. In this paper we propose a novel approach for solving the curve correspondence problem that is not limited by the requirement of one dimensional parametrization. The proposed approach utilizes particle dynamics and minimizes a cost function through an iterative solution of a system of first order ordinary differential equations. The proposed approach is therefore capable of handling complex curves for which a simple parametrization is not available. The proposed approach is evaluated by measuring the precision and recall rates of documents based on signature similarity. To facilitate a realistic evaluation, the signature data we use was collected from real world documents spanning a period of several decades.

关键词： Biometrics Dynamic programming Cost function Indexing Training data pattern recognition computer science Iterative methods Differential equations Credit cards

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Keystroke Biometric recognition Studies on Long-Text Input under Ideal and Application-Oriented Conditions

Keystroke Biometric Recognition Studies on Long-Text Input u...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： M. Villani C. Tappert Giang Ngo J. Simone H.St. Fort Sung-Hyuk Cha School of CSIS Pace University Pleasantville NY USA

A long-text-input keystroke biometric system was developed for applications such as identifying perpetrators of inappropriate e-mail or fraudulent Internet activity. A Java applet collected raw keystroke data over the Internet, appropriate long-text-input features were extracted, and a pattern classifier made identification decisions. Experiments were conducted on a total of 118 subjects using two input modes - copy and free-text input - and two keyboard types - desktop and laptop keyboards. Results indicate that the keystroke biometric can accurately identify an individual who sends inappropriate email (free text) if sufficient enrollment samples are available and if the same type of keyboard is used to produce the enrollment and questioned samples. For laptop keyboards we obtained 99.5% accuracy on 36 users, which decreased to 97.9% on a larger population of 47 users. For desktop keyboards we obtained 98.3% accuracy on 36 users, which decreased to 93.3% on a larger population of 93 users. Accuracy decreases significantly when subjects used different keyboard types or different input modes for enrollment and testing.

关键词： Biometrics pattern recognition computer vision Keyboards conferences System testing Feature extraction Java Text recognition Rhythm

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Multi-view Appearance-based 3D Hand Pose Estimation

Multi-view Appearance-based 3D Hand Pose Estimation

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Haiying Guan Jae Sik Chang Longbin Chen R.S. Feris M. Turk Computer Science Department University of California Santa Barbara CA USA

We describe a novel approach to appearance-based hand pose estimation which relies on multiple cameras to improve accuracy and resolve ambiguities caused by selfocclusions. Rather than estimating 3D geometry as most previous multi-view imaging systems, our approach uses multiple views to extend current exemplar-based methods that estimate hand pose by matching a probe image with a large discrete set of labeled hand pose images. We formulate the problem in a MAP (maximum a posteriori) framework, where the information from multiple cameras is fused to provide reliable hand pose estimation. Our quantitative experimental results show that correct estimation rate is much higher using our multi-view approach than using a single-view approach.

关键词： Cameras Probes Three dimensional displays computer displays Feature extraction computer science Geometry Large-scale systems Personal digital assistants Cellular phones

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Reinforcement Matching Using Region Context

Reinforcement Matching Using Region Context

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hongli Deng E.N. Mortensen L. Shapiro T.G. Dietterich Electrical Engineering and Computer Science Oregon State University USA Computer Science and Engineering University of Washington USA

Local feature-based matching is robust to both clutter and occlusion. However, a primary shortcoming of local features is a deficiency of global information that can cause ambiguities in matching. Local features combined with global relationships convey much more information, but global spatial information is often not robust to occlusion and/or non-rigid transformations. This paper proposes a new framework for including global context information into local feature matching, while still maintaining robustness to occlusion, clutter, and nonrigid transformations. To generate global context information, we extend previous fixed-scale, circular-bin methods by using affine-invariant log-polar elliptical bins. Further, we employ a reinforcement matching scheme that provides greater robustness to occlusion and clutter than previous methods that non-discriminately compare accumulated bins values over the entire context. We also present a more robust method of calculating a feature’s dominant orientation. We compare reinforcement matching to nearest neighbor matching without region context and to robust matching methods (RANSAC and PROSAC).

关键词： computer vision computer science Solid modeling Euclidean distance Shape Histograms pattern recognition conferences Robustness

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A Min-Cover Approach for Finding Salient Curves

A Min-Cover Approach for Finding Salient Curves

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： P. Felzenszwalb D. McAllester University of Chicago USA TTI-Chicago

We consider the problem of deriving a global interpretation of an image in terms of a small set of smooth curves. The problem is posed using a statistical model for images with multiple curves. Besides having important applications to edge detection and grouping the curve finding task is a special case of a more general problem, where we want to explain the whole image in terms of a small set of objects. We describe a novel approach for estimating the content of scenes with multiple objects using a min-cover framework that is simple and powerful. The min-cover problem is NP-hard but there is a good approximation algorithm that sequentially selects objects minimizing a "cost per pixel" measure. In the case of curve detection we use a type of best-first search to quickly find good curves for the covering algorithm. The method integrates image data over long curves without relying on binary feature detection. We have applied the curve detection method for finding object boundaries in natural scenes and measured its performance using the Berkeley segmentation dataset.

关键词： Layout Object detection Costs Approximation algorithms Image segmentation Image edge detection computer vision Gray-scale Color Object recognition

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Feature-level Fusion for Object Segmentation using Mutual Information

Feature-level Fusion for Object Segmentation using Mutual In...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： V. Sharma J.W. Davis Department of Computer Science and Engineering Ohio State Uinversity Columbus OH USA

We present a new feature-level image fusion technique for object segmentation based on mutual information. Using object regions roughly detected from one sensor as input, the proposed technique extracts relevant information from another to complete the segmentation. First, a contourbased feature representation is presented that implicitly captures object shape. The notion of relevance across sensor modalities is then defined using mutual information computed based on the affinity between contour features. Finally a heuristic selection scheme is proposed to identify the set of contour features having the highest mutual information with the input object regions. The approach works directly from the input image pair without relying on a training phase. Results are presented for segmenting people from background, and quantitatively evaluated.

关键词： Object segmentation Mutual information Sensor phenomena and characterization Image fusion Image segmentation Sensor fusion Object detection Image sensors Data mining Shape

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