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Particle filtering for geometric active contours with application to tracking moving and deforming objects

Particle filtering for geometric active contours with applic...

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conference on computer vision and pattern recognition (cvpr)

作者： Yogesh Rathi N. Vaswani A. Tannenbaum A. Yezzi School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Geometric active contours are formulated in a manner which is parametrization independent. As such, they are amenable to representation as the zero level set of the graph of a higher dimensional function. This representation is able to deal with singularities and changes in topology of the contour. It has been used very successfully in static images for segmentation and registration problems where the contour (represented as an implicit curve) is evolved until it minimizes an image based energy functional. But tracking involves estimating the global motion of the object and its local deformations as a function of time. Some attempts have been made to use geometric active contours for tracking, but most of these minimize the energy at each frame and do not utilize the temporal coherency of the motion or the deformation. On the other hand, tracking algorithms using Kalman filters or particle filters have been proposed for finite dimensional representations of shape. But these are dependent on the chosen parametrization and cannot handle changes in curve topology. In the present work, we formulate a particle filtering algorithm in the geometric active contour framework that can be used for tracking moving and deforming objects.

关键词： Active filters Filtering Active contours Particle tracking Topology Level set Image segmentation Motion estimation Particle filters Shape

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A dense stereo matching using two-pass dynamic programming with generalized ground control points

A dense stereo matching using two-pass dynamic programming w...

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conference on computer vision and pattern recognition (cvpr)

作者： Jae Chul Kim Kyoung Mu Lee Byoung Tae Choi Sang Uk Lee Electronics and Telecommunications Research Institute Daejeon South Korea School of Electrical Engineering ASRI Seoul National University Seoul South Korea

ISBN: (纸本)0769523722

A method for solving dense stereo matching problem is presented in this paper. First, a new generalized ground control points (GGCPs) scheme is introduced, where one or more disparity candidates for the true disparity of each pixel are assigned by local matching using the oriented spatial filters. By allowing "all" pixels to have multiple candidates for their true disparities, GGCPs not only guarantee to provide a sufficient number of starting pixels needed for guiding the subsequent matching process, but also remarkably reduce the risk of false match, improving the previous GCP-based approaches where the number of the selected control points tends to be inversely proportional to the reliability. Second, by employing a two-pass dynamic programming technique that performs optimization both along and across the scanlines, we solve the typical inter-scanline inconsistency problem. Moreover, combined with the GGCPs, the stability and efficiency of the optimization are improved significantly. Experimental results for the standard data sets show that the proposed algorithm achieves comparable results to the state-of-the-arts with much less computational cost.

关键词： Dynamic programming Stereo vision Telecommunication control Spatial filters computer vision Proportional control Stability Computational efficiency Application software Degradation

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A measure of deformability of shapes, with applications to human motion analysis

A measure of deformability of shapes, with applications to h...

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conference on computer vision and pattern recognition (cvpr)

作者： A.K. Roy-Chowdhury Department of Electrical Engineering University of California Riverside CA USA

In this paper we develop a theory for characterizing how deformable a shape is. We define a term called "deformability index" for shapes. The deformability index is computed from the tracked positions of a sequence of deformable shapes, using a scaled orthographic camera projection model. Our method assumes that a deformable shape sequence can be represented by a linear combination of basis shapes, where the weights assigned to each basis shape changes with time. The tracked points obtained from the shape sequence is transformed to a 3D shape space. Using statistical models to separate out the "true" deformations from those induced by noise in the trajectories, the dimension of this shape space is estimated using spectral analysis methods. The dimension of this shape space determines the number of basis shapes needed to represent the shape sequence, which, in turn, determines the deformability index. Rigid 3D transformations of the shape are taken into account in estimating the deformability index; however, the method does not require estimation of 3D structure or motion. Experimental results are shown using motion capture data as well as real imagery of different human activities. The results show that the deformability index is in accordance with our intuitive judgement and corroborates certain hypotheses in human movement analysis studies.

关键词： Shape measurement Motion measurement Humans Motion analysis Deformable models Spectral analysis Motion estimation computer vision Legged locomotion Cameras

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Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition

Proceedings of the 2004 IEEE Computer Society Conference on ...

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conference on computer vision and pattern recognition (cvpr)

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A Visibility Improvement System for Low vision Drivers by Nonlinear Enhancement of Fused Visible and Infrared Video

A Visibility Improvement System for Low Vision Drivers by No...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Hau Ngo Li Tao Ming Zhang A. Livingston V. Asari Computational Intelligence and Machine Vision Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

Development of a visibility improvement system for helping drivers with poor vision during night and bad weather conditions is proposed in this paper. Video streams captured by a CCD camera and an infrared camera are registered and fused to obtain image details in extremely low lighting conditions. A novel image enhancement technique employing a nonlinear expansion function is developed for enhancing the fused images. The slope characteristics of the expansion function at an individual pixel location is based on the statistical properties of its neighborhood pixels. The expansion function is also capable of reducing the intensity of the overly bright image regions due to the presence of head lights from vehicles in opposite direction. The enhanced video stream is then subjected to a color restoration process to introduce natural color using the color information gathered from the CCD camera image. The entire image processing and analysis system is being installed in an FPGA environment for performing the processing in real-time. The processed video stream is displayed on a Head-Up Display located directly in front of the driver to assist him/her to drive safely in poor visibility environments. Preliminary results obtained in various experiments conducted with the proposed system are encouraging.

关键词： Machine vision Streaming media Charge-coupled image sensors Color Charge coupled devices Infrared imaging Image enhancement Head Vehicles Image restoration

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A Binary Tree for Probability Learning in Eye Detection

A Binary Tree for Probability Learning in Eye Detection

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Junwen Wu M.M. Trivedi Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San Diego CA USA

In this paper we proposed to solve the eye detection and localization problem under a general statistical model based object detection framework. A binary tree representation is used to discover the objects’ underlying statistical structure. Tree structures enable us to describe the object local statistical structure in a coarse-to-fine fashion. Each subtree explains the statistics for certain local substructure. The tree is built in a top-down fashion. Subsets with negligible conditional independency are found by k-means clustering using mutual information. The conditionally independent features are separated into different subtrees, while more dependent features are tended to appear close in the tree. The distribution of the object can be learned accordingly. Gaussian mixture in the independent component analysis (ICA) subspace is used to model the distribution of each high dependent feature subset, where each independent component explains the local substructure. The use of tree structure enables us to learn the distribution recursively by applying Bayesian criterion. Substantial experiments were done to evaluate the performance over the eyes detection accuracy as well as the localization ability. Experimental results show a better detection accuracy than the Viisage system with a reasonable localization ability, which validate the algorithm.

关键词： Binary trees Object detection Bayesian methods computer vision Eyes Tree data structures Probability Statistical distributions Independent component analysis Face detection

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Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition

Proceedings of the 2004 IEEE Computer Society Conference on ...

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conference on computer vision and pattern recognition (cvpr)

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Multi-View Classifier Swarms for Pedestrian Detection and Tracking

Multi-View Classifier Swarms for Pedestrian Detection and Tr...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： P. Saisan S. Medasani Y. Owechko Electrical Engineering UCLA Los Angeles CA USA HRL Laboratories LLC Malibu CA USA

We describe a novel method for recognition and localization of objects in 3D space using multiple views. We pose the task of classifying and locating objects in 3D space as an optimization problem that combines 2D classifier scores from two separate views of the object. Our methods combine feature-based 2D object classification with efficient search mechanisms based on Particle Swarm Optimization (PSO) by implementing each particle as a local window classi- fier guided by PSO dynamics. We first formulate the localization problem in a multi-view framework where the domain of swarm classifier particles is extended from 2D image space to 3D space. In this scheme, swarm particles encapsulate the geometric structure that binds multiple views. Each particle is a self-contained classifier that "flies" through the solution space seeking the most "objectlike" region in space, thus effectively expanding the scope of an image based 2D classifier to 3D space. In a second formulation, we demonstrate how 2D swarm based search results obtained from individual 2D views can be used to perform 3D localization tasks. We outline a method to extend the framework from single object to multiple object localization and tracking scenarios.

关键词： Image recognition computer vision Object recognition Stereo vision Laboratories Particle swarm optimization Decision theory Geometrical optics Optical sensors Image motion analysis

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Fisher+Kernel criterion for discriminant analysis

Fisher+Kernel criterion for discriminant analysis

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conference on computer vision and pattern recognition (cvpr)

作者： Shu Yang Shuicheng Yan Dong Xu Xiaoou Tang Chao Zhang National Laboratory on Machine Perception First Hospital Peking University Beijing China Department of Information Engineering Chinese University of Hong Kong Sha Tin Hong Kong China MOE-Microsoft Key Laboratory of Multimedia Computing and Communication & Department of EEIS University of Science and Technology Hefei Anhui China Microsoft Research Asia Beijing China

ISBN: (纸本)0769523722

We simultaneously approach two tasks of nonlinear discriminant analysis and kernel selection problem by proposing a unified criterion, Fisher+Kernel criterion. In addition, an efficient procedure is derived to optimize this new criterion in an iterative manner. More specifically, original input vector is first transformed into a higher dimensional feature matrix through a battery of nonlinear mappings involved in different kernels. Then, based on the feature matrices, FKC is presented within two coupled projection spaces: one projection space is used to search for the optimal combinations of kernels; while the other encodes the optimal nonlinear discriminating projection directions. Our proposed method is a unified framework for both kernel selection and nonlinear discriminant analysis. Besides, the algorithm potentially alleviates overfitting problem existing in traditional KDA and has no singularity problems in most cases. The effectiveness of our proposed algorithm is validated by extensive face recognition experiments on several datasets.

关键词： Kernel Laboratories Asia Face recognition Linear discriminant analysis Design optimization Chaotic communication Multimedia computing Batteries Couplings

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Illumination normalization for face recognition and uneven background correction using total variation based image models

Illumination normalization for face recognition and uneven b...

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conference on computer vision and pattern recognition (cvpr)

作者： T. Chen W. Yin X.S. Zhou D. Comaniciu T.S. Huang University of Illinois Urbana-Champaign USA Columbia University USA Siemens Corporate Research Inc. USA

We present a new algorithm for illumination normalization and uneven background correction in images, utilizing the recently proposed TV+L/sup 1/ model: minimizing the total variation of the output cartoon while subject to an L/sup 1/-norm fidelity term. We give intuitive proofs of its main advantages, including the well-known edge preserving capability, minimal signal distortion, and scale-dependent but intensity-independent foreground extraction. We then propose a novel TV-based quotient image model (TVQI) for illumination normalization, an important preprocessing for face recognition under different lighting conditions. Using this model, we achieve 100% face recognition rate on Yale face database B if the reference images are under good lighting condition and 99.45% if not. These results, compared to the average 65% recognition rate of the quotient image model and the average 95% recognition rate of the more recent self quotient image model, show a clear improvement. In addition, this model requires no training data, no assumption on the light source, and no alignment between different images for illumination normalization. We also present the results of the related applications - uneven background correction for cDNA mic roar ray films and digital microscope images. We believe the proposed works can serve important roles in the related fields.

关键词： Lighting Face recognition Light sources Distortion Image recognition Microscopy Image databases Training data Surface texture computer vision

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