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Multi-modal Scene Reconstruction using Perceptual Grouping Constraints

Multi-modal Scene Reconstruction using Perceptual Grouping C...

引用

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

作者： N. Pugeault F. Worgotter N. Kruger University of Edinburgh UK University of Göttingen Germany Aalborg University Copenhagen Denmark

In this work we propose a scheme integrating perceptual grouping into stereopsis to reduce the ambiguity of those early processes. We propose a simple perceptual grouping algorithm that - in addition to the geometric information makes use of a novel multi-modal affinity measure between local primitives. We then use this group information to 1) disambiguate the stereopsis by enforcing that stereo matches preserve groups; and 2) correct the reconstruction error due to the image pixel sampling using a linear interpolation over the groups. We show quantitative and qualitative demonstrations of those processes on a variety of sequences.

关键词： Layout Image reconstruction Stereo vision Data mining Stereo image processing Interpolation Fluid flow measurement Phase measurement Testing Error correction

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Fast Variational Segmentation using Partial Extremal Initialization

Fast Variational Segmentation using Partial Extremal Initial...

引用

conference on computer vision and pattern recognition (CVPR)

作者： J.E. Solem N.Chr. Overgaard M. Persson A. Heyden Applied Mathematics Group School of Technology and Society Malmö University Sweden

In this paper we consider region-based variational segmentation of two- and three-dimensional images by the minimization of functionals whose fidelity term is the quotient of two integrals. Users often refrain from quotient functionals, even when they seem to be the most natural choice, probably because the corresponding gradient descent PDEs are nonlocal and hence require the computation of global properties. Here it is shown how this problem may be overcome by employing the structure of the Euler-Lagrange equation of the fidelity term to construct a good initialization for the gradient descent PDE, which will then converge rapidly to the desired (local) minimum. The initializer is found by making a one-dimensional search among the level sets of a function related to the fidelity term, picking the level set which minimizes the segmentation functional. This partial extremal initialization is tested on a medical segmentation problem with velocity- and intensity data from MR images. In this particular application, the partial extremal initialization speeds up the segmentation by two orders of magnitude compared to straight forward gradient descent.

关键词： Image segmentation Level set Active contours Mathematics Equations Medical tests Biomedical imaging Statistics Topology Medical services

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An adaptive clustering for multiple object tracking in sequences in and beyond the visible spectrum

An adaptive clustering for multiple object tracking in seque...

引用

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

作者： S. Dubuisson Laboratoire dInformatique de Paris LIP6 UPMC Paris France

In this paper, we propose a method to track multiple deformable objects in sequences (with a static camera) in and beyond the visible spectrum by combining Gabor filtering and clustering. In a first step, a set of Gabor filter banks is used to filter the difference image between two consecutive frames. Then, the moving areas are sampled by randomly positioning particles in high magnitude area of the filtered image. Finally, these points are clustered to obtain one class for each moving object. The novelty in our method is in using cluster information from the previous frame to classify new particles in the current frame. This makes our method robust to occlusions, objects entering and leaving the field of view, objects stopping and starting, and moving objects getting really close to each other.

关键词： Gabor filters Filter bank Target tracking Solid modeling Filtering computer vision Feature extraction Cameras Adaptive filters Robustness

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Differential Tracking based on Spatial-Appearance Model (SAM)

Differential Tracking based on Spatial-Appearance Model (SAM...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Ting Yu Ying Wu Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA

A fundamental issue in differential motion analysis is the compromise between the flexibility of the matching criterion for image regions and the ability of recovering the motion. Localized matching criteria, e.g., pixel-based SSD, may enable the recovery of all motion parameters, but it does not tolerate much appearance changes. On the other hand, global criteria, e.g., matching histograms, can accommodate dramatic appearance changes, but may be blind to some motion parameters, e.g., scaling and rotation. This paper presents a novel differential approach that integrates the advantages of both in a principled way based on a spatial-appearance model (SAM) that combines local appearances variations and global spatial structures. This model can capture a large variety of appearance variations that are attributed to the local non-rigidity. At the same time, this model enables efficient recovery of all motion parameters. A maximum likelihood matching criterion is defined and rigorous analytical results are obtained that lead to a closed form solution to motion tracking. Very encouraging results demonstrate the effectiveness and efficiency of the proposed method for tracking non-rigid objects that exhibit dramatic appearance deformations, large object scale changes and partial occlusions.

关键词： Motion analysis Histograms Motion estimation Closed-form solution Tracking Uncertainty Region 9 Kernel Parameter estimation computer society

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Tunable Kernels for Tracking

Tunable Kernels for Tracking

引用

conference on computer vision and pattern recognition (CVPR)

作者： V. Parameswaran V. Ramesh I. Zoghlami Real-Time Vision and Modeling Department Siemens AG Corporate Research and Development Princeton NJ USA

We present a tunable representation for tracking that simultaneously encodes appearance and geometry in a manner that enables the use of mean-shift iterations for tracking. The classic formulation of the tracking problem using mean-shift iterations encodes spatial information very loosely (i.e. using radially symmetric kernels). A problem with such a formulation is that it becomes easy for the tracker to get confused with other objects having the same feature distribution but different spatial configurations of features. Subsequent approaches have addressed this issue but not to the degree of generality required for tracking specific classes of objects and motions (e.g. humans walking). In this paper, we formulate the tracking problem in a manner that encodes the spatial configuration of features along with their density and yet retains robustness to spatial deformations and feature density variations. The encoding of spatial configuration is done using a set of kernels whose parameters can be optimized for a given class of objects and motions, off-line. The formulation enables the use of meanshift iterations and runs in real-time. We demonstrate better tracking results on synthetic and real image sequences as compared to the original mean-shift tracker.

关键词： Kernel Target tracking Histograms Encoding Robustness Bandwidth Motion analysis Solid modeling Geometry Humans

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Ensemble Method for Robust Motion Estimation

Ensemble Method for Robust Motion Estimation

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

作者： Wei Zhang J. Kosecka George Mason University Fairfax VA USA

The core of the traditional RANSAC algorithm and its more recent efficient counterparts is the hypothesis evaluation stage, with the focus on finding the best, outlier free hypothesis. Motivated by a non-parametric ensemble techniques, we demonstrate that it proves advantageous to use the entire set of hypotheses generated in the sampling stage. We show that by studying the residual distribution of each data point with respect to the entire set of hypotheses, the problem of inlier/ outlier identification can be formulated as a classification problem. We present extensive simulations of the approach, which in the presence of a large percentage (> 50%) of outliers, provides a repeatable and, an order of magnitude more efficient method compared to the currently existing techniques. Results on widebaseline matching and fundamental matrix estimation are presented.

关键词： Robustness Motion estimation Sampling methods computer vision Testing Parameter estimation Context modeling Maximum likelihood estimation Iterative algorithms Iterative methods

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Diffusion Distance for Histogram Comparison

Diffusion Distance for Histogram Comparison

引用

conference on computer vision and pattern recognition (CVPR)

作者： Haibin Ling K. Okada Center for Automation Research Computer Science Department University of Maryland College Park MD USA Imaging and Visualization Department Siemens Corporate Research Inc. Princeton NJ USA

In this paper we propose diffusion distance, a new dissimilarity measure between histogram-based descriptors. We define the difference between two histograms to be a temperature field. We then study the relationship between histogram similarity and a diffusion process, showing how diffusion handles deformation as well as quantization effects. As a result, the diffusion distance is derived as the sum of dissimilarities over scales. Being a cross-bin histogram distance, the diffusion distance is robust to deformation, lighting change and noise in histogram-based local descriptors. In addition, it enjoys linear computational complexity which significantly improves previously proposed cross-bin distances with quadratic complexity or higher. We tested the proposed approach on both shape recognition and interest point matching tasks using several multi-dimensional histogram-based descriptors including shape context, SIFT, and spin images. In all experiments, the diffusion distance performs excellently in both accuracy and efficiency in comparison with other state-of-the-art distance measures. In particular, it performs as accurately as the Earth Mover’s Distance with much greater efficiency.

关键词： Histograms Shape Temperature Diffusion processes Quantization Noise robustness Multi-stage noise shaping Computational complexity Testing Image recognition

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Joint Boosting Feature Selection for Robust Face recognition

Joint Boosting Feature Selection for Robust Face Recognition

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conference on computer vision and pattern recognition (CVPR)

作者： Rong Xiao Wujun Li Yuandong Tian Xiaoou Tang Microsoft Research Asia Beijing China National Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Engineering Shanghai JiaoTong University Shanghai China

A fundamental challenge in face recognition lies in determining what facial features are important for the identification of faces. In this paper, a novel face recognition framework is proposed to address this problem. In our framework, 3D face models are used to synthesize a huge database of realistic face images which covers wide appearance variations of faces due to various pose, illumination, and expression changes. A novel feature selection algorithm which we call Joint Boosting is developed to extract discriminative face features using this massive database. The major contributions of this paper are: (1) With the help of 3D face models, a massive database of realistic virtual face images is generated to achieve robust feature selection; (2)Because the huge database covers a wide range of face variations, our feature selection procedure only needs to be trained once, and the selected feature set can be generalized to other face database without re-training; (3) We propose a new learning algorithm, Joint Boosting Algorithm, which is effective and efficient in learning directly from a massive database without having to convert face images to intra-personal and extra-personal difference images. This property is important for applying our algorithm to other general pattern recognition problems. Experimental results show that our method significantly improves recognition performance.

关键词： Boosting Robustness Face recognition Image databases Spatial databases Facial features Lighting Feature extraction Image generation Image converters

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Empirical Studies of the Existence of the Biometric Menagerie in the FRGC 2.0 Color Image Corpus

Empirical Studies of the Existence of the Biometric Menageri...

引用

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

作者： M. Wittman P. Davis P.J. Flynn Department of Computer Science and Engineering University of Notre Dame Notre Dame IN USA

The issue of recognizability of subjects in biometric identification is of particular interest to the designers of these systems. We have applied the concept of Doddington’s biometric menagerie to the area of facial recognition. We performed a series of tests for the presence of goats, lambs, and wolves on FRGC 2.0 color image data. The data for the subjects that appeared at the extreme end of these tests was then visually examined. Even a cursory comparison of images showed that for this set of data, some images fell into the defined menagerie categories. Our tests show the statistical existence of these animal classifications within the constraints of this set of FRGC 2.0 data using the baseline matching algorithm. Ultimately, these tests were limited by the image data set and matching algorithm used. For further confirmation of the existence of the menagerie, the analysis must be expanded to include different image sets and matching algorithms..

关键词： Biometrics Color Testing Face recognition Probes Principal component analysis Image analysis Statistics computer science USA Councils

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Finding Minimal Parameterizations of Cylindrical Image Manifolds

Finding Minimal Parameterizations of Cylindrical Image Manif...

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

作者： M. Dixon N. Jacobs R. Pless Department of Computer Science and Engineering Washington University Saint Louis MO USA

Manifold learning has become an important tool to characterize high-dimensional data that vary nonlinearly due to a few parameters. Applications to the analysis of medical imagery and human motion patterns have been successful despite the lack of effective tools to parameterize cyclic data sets. This paper offers an initial approach to this problem, and provides for a minimal parameterization of points that are drawn from cylindrical manifolds-data whose (unknown) generative model includes a cyclic and a non-cyclic parameter. Solving for this special case is important for a number of current, practical applications and provides a start toward a general approach to cyclic manifolds. We offer results on synthetic and real data sets and illustrate an application to de-noising cardiac ultrasound images.

关键词： Topology Manifolds Jacobian matrices Noise reduction Ultrasonic imaging computer science Biomedical engineering Data engineering Application software Image analysis

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