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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

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Autonomous fixation

Autonomous fixation

引用

conference on computer vision and pattern recognition (cvpr)

作者： M.A. Taalebinezhaad MIT Artificial Intelligence Laboratory Cambridge MA USA

The author previously introduced a direct method, called fixation, for the recovery of shape and motion in the general case that uses neither feature correspondence nor optical flow. Instead, it directly uses the spatio-temporal gradients of image brightness. The experimental results of applying some of the author's fixation algorithms to a sequence of real images, where the motion is a combination of translation and rotation, are reported. Techniques for autonomous choice of parameters that result in good estimates for important motion parameters are described.< >

关键词： Shape Brightness Equations Vectors Artificial intelligence Laboratories Motion estimation Image motion analysis computer vision Optical computing

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The distinctiveness, detectability, and robustness of local image features

The distinctiveness, detectability, and robustness of local ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： G. Carneiro A.D. Jepson Department of Computer Science University of British Columbia Vancouver BC Canada Department of Computer Science University of Toronto Toronto ONT Canada

We introduce a new method that characterizes typical local image features (e.g., SIFT, phase feature) in terms of their distinctiveness, detectability, and robustness to image deformations. This is useful for the task of classifying local image features in terms of those three properties. The importance of this classification process for a recognition system using local features is as follows: a) reduce the recognition time due to a smaller number of features present in the test image and in the database of model features; b) improve the recognition accuracy since only the most useful features for the recognition task are kept in the model database; and c) increase the scalability of the recognition system given the smaller number of features per model. A discriminant classifier is trained to select well behaved feature points. A regression network is then trained to provide quantitative models of the detection distributions for each selected feature point. It is important to note that both the classifier and the regression network use image data alone as their input. Experimental results show that the use of these trained networks not only improves the performance of our recognition system, but it also significantly reduces the computation time for the recognition process.

关键词： Robustness Image recognition System testing Scalability computer science Image databases Spatial databases Character recognition Phase detection computer networks

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Articulated object recognition, or: how to generalize the generalized Hough transform

Articulated object recognition, or: how to generalize the ge...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Beinglass H.J. Wolfson Computer Science Department Sackler Faculty of Exact Sciences Tel-Aviv University Tel-Aviv Israel

A method for model-based recognition of articulated objects in cluttered scenes is presented. The objects consist of rigid parts connected by rotary or prismatic joints. The method is based on an extension of the generalized Hough transform approach. It is applicable to various viewing transformations. Unlike previous methods there is no significant degradation in performance for recognition of articulated objects compared with the recognition of rigid objects containing similar amounts of information. The method was implemented and successfully tested for recognition of partially overlapping 2-D objects with rotary joints which have undergone rotation, translation, and scaling.< >

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Driver state monitor from DELPHI

Driver state monitor from DELPHI

引用

conference on computer vision and pattern recognition (cvpr)

作者： N. Edenborough R. Hammoud A. Harbach A. Ingold B. Kisacanin P. Malawey T. Newman G. Scharenbroch S. Skiver M. Smith A. Wilhelm G. Witt E. Yoder H. Zhang Delphi Electronics & Safety

We present an automotive-grade, real-time, vision-based driver state monitor. Upon detecting and tracking the driver's facial features, the system analyzes eye-closures and head pose to infer his/her fatigue or distraction. This information is used to warn the driver and to modulate the actions of other safety systems. The purpose of this monitor is to increase road safety by preventing drivers from falling asleep or from being overly distracted, and to improve the effectiveness of other safety systems.

关键词： Monitoring Road safety Automotive engineering Fatigue Accidents computer vision Human factors Air safety Belts Driver circuits

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A Smart Sensor with Hyper spectral/Range Fovea and Panoramic Peripheral View

A Smart Sensor with Hyper spectral/Range Fovea and Panoramic...

引用

ieee computer society conference on computer vision and pattern recognition Workshops

作者： Tao Wang Zhigang Zhu Harvey Rhody Department of Computer Science Department of Computer Science The CUNY Graduate Center Center for Imaging Science Rochester Institute of Technology

ISBN: (纸本)9781424439942

We propose an adaptive and effective multimodal peripheral-fovea sensor design for real-time targets tracking. This design is inspired by the biological vision systems for achieving real-time target detection and recognition with a hyperspectral/range fovea and panoramic peripheral view. A realistic scene simulation approach is used to evaluate our sensor design and the related data exploitation algorithms before a real sensor is made. The goal is to reduce development time and system cost while achieving optimal results through an iterative process that incorporates simulation, sensing, processing and evaluation. Important issues such as multimodal sensory component integration, region of interest extraction, target tracking, hyperspectral image analysis and target signature identification are discussed.

关键词： Hyperspectral imaging Panoramic vision bio-inspired sensing Target tracking

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Online learning of probabilistic appearance manifolds for video-based recognition and tracking

Online learning of probabilistic appearance manifolds for vi...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Kuang-Chih Lee D. Kriegman Urbana-Champaign University of Illinois Urbana-Champaign IL USA Computer Science & Engineering University of California San Diego La Jolla CA USA

This paper presents an online learning algorithm to construct from video sequences an image-based representation that is useful for recognition and tracking. For a class of objects (e.g., human faces), a generic representation of the appearances of the class is learned off-line. From video of an instance of this class (e.g., a particular person), an appearance model is incrementally learned on-line using the prior generic model and successive frames from the video. More specifically, both the generic and individual appearances are represented as an appearance manifold that is approximated by a collection of sub-manifolds (named pose manifolds) and the connectivity between them. In turn, each sub-manifold is approximated by a low-dimensional linear sub-space while the connectivity is modeled by transition probabilities between pairs of sub-manifolds. We demonstrate that our online learning algorithm constructs an effective representation for face tracking, and its use in video-based face recognition compares favorably to the representation constructed with a batch technique.

关键词： Video sequences Principal component analysis Image recognition computer science Machine learning Machine learning algorithms Face recognition Streaming media Manifolds Humans

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SUSurE: Speeded Up Surround Extrema Feature Detector and Descriptor for Real-time Applications

SUSurE: Speeded Up Surround Extrema Feature Detector and Des...

引用

ieee computer society conference on computer vision and pattern recognition Workshops

作者： Mosalam Ebrahimi Walterio W. Mayol-Cuevas Department of Computer Science University of Bristol Bristol United Kingdom

ISBN: (纸本)9781424439942

There has been significant research into the development of visual feature detectors and descriptors that are robust to a number of image deformations. Some of these methods have emphasized the need to improve on computational speed and compact representations so that they can enable a range of real-time applications with reduced computational requirements. In this paper we present modified detectors and descriptors based on the recently introduced CenSurE [l], and show experimental results that aim to highlight the computational savings that can be made with limited reduction in performance. The developed methods are based on exploiting the concept of sparse sampling which may be of interest to a range of other existing approaches.

关键词： computer vision Detectors Filters Robustness Information services Web sites Internet Application software Kernel Image databases

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Gibbs likelihoods for Bayesian tracking

Gibbs likelihoods for Bayesian tracking

引用

conference on computer vision and pattern recognition (cvpr)

作者： S. Roth L. Sigal M.J. Black Department of Computer Science Brown University Providence RI USA

Bayesian methods for visual tracking model the likelihood of image measurements conditioned on a tracking hypothesis. Image measurements may, for example, correspond to various filter responses at multiple scales and orientations. Most tracking approaches exploit ad hoc likelihood models while those that exploit more rigorous, learned, models often make unrealistic assumptions about the underlying probabilistic model. Such assumptions cause problems for Bayesian inference when an unsound likelihood is combined with an a priori probability distribution. Errors in modeling the likelihood can lead to brittle tracking results, particularly when using non-parametric inference techniques such as particle filtering. We show how assumptions of conditional independence of filter responses are violated in common tracking scenarios, lead to incorrect likelihood models, and cause problems for Bayesian inference. We address the problem of modeling more principled likelihoods using Gibbs learning. The learned models are compared with naive Bayes methods which assume conditional independence of the filter responses. We show how these Gibbs models can be used as an effective image likelihood, and demonstrate them in the context of particle filter-based human tracking.

关键词： Bayesian methods computer society computer vision pattern recognition

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Robust L/sub 1/ norm factorization in the presence of outliers and missing data by alternative convex programming

Robust L/sub 1/ norm factorization in the presence of outlie...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Qifa Ke T. Kanade Computer Science Department Carnegie Mellon University Pittsburgh PA USA

Matrix factorization has many applications in computer vision. Singular value decomposition (SVD) is the standard algorithm for factorization. When there are outliers and missing data, which often happen in real measurements, SVD is no longer applicable. For robustness iteratively re-weighted least squares (IRLS) is often used for factorization by assigning a weight to each element in the measurements. Because it uses L/sub 2/ norm, good initialization in IRLS is critical for success, but is nontrivial. In this paper, we formulate matrix factorization as a L/sub 1/ norm minimization problem that is solved efficiently by alternative convex programming. Our formulation 1) is robust without requiring initial weighting, 2) handles missing data straightforwardly, and 3) provides a framework in which constraints and prior knowledge (if available) can be conveniently incorporated. In the experiments we apply our approach to factorization-based structure from motion. It is shown that our approach achieves better results than other approaches (including IRLS) on both synthetic and real data.

关键词： Robustness Cost function Matrix decomposition Application software computer vision Singular value decomposition Noise measurement Maximum likelihood estimation Least squares approximation computer science

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Optimal sub-shape models by minimum description length

Optimal sub-shape models by minimum description length

引用

conference on computer vision and pattern recognition (cvpr)

作者： G. Langs P. Peloschek H. Bischof Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Department of Clinical Radiology Vienna Medical University Vienna Austria

Active shape models are powerful and widely used tool to interpret complex image data. By building models of shape variation they enable search algorithms to use a priori knowledge in an efficient and gainful way. However, due to the linearity of PCA, non-linearities like rotations or independently moving sub-parts in the data can deteriorate the resulting model considerably. Although non-linear extensions of active shape models have been proposed and application specific solutions have been used, they still need a certain amount of user interaction during model building. In this paper the task of building/choosing optimal models is tackled in a more generic information theoretic fashion. In particular, we propose an algorithm based on the minimum description length principle to find an optimal subdivision of the data into sub-parts, each adequate for linear modeling. This results in an overall more compact model configuration. Which in turn leads to a better model in terms of modes of variations. The proposed method is evaluated on synthetic data, medical images and hand contours.

关键词： Active shape model Biomedical imaging Principal component analysis computer vision Linearity Polynomials pattern recognition computer graphics Radiology Image processing

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