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Brightness perception, dynamic range and noise: a unifying model for adaptive image sensors

Brightness perception, dynamic range and noise: a unifying m...

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conference on computer Vision and pattern recognition (CVPR)

作者： V. Brajovic Carnegie Mellon University USA

Many computer vision applications have to cope with large dynamic range and changing illumination conditions in the environment. Any attempt to deal with these conditions at the algorithmic level alone are inherently difficult because of the following: (1) conventional image sensors cannot completely capture wide dynamic range radiances without saturation or underexposure; (2) the quantization process destroys small signal variations especially in shadows; and (3) all possible illumination conditions cannot be completely accounted for. The paper proposes a computational model for brightness perception that deals with issues of dynamic range and noise. The model can be implemented on-chip in analog domain before the signal is saturated or destroyed through quantization. The model is "unified" because a single mathematical formulation addresses the problem of shot and thermal noise, and normalizes the signal range to simultaneously compress the dynamic range, minimize appearance variations due to changing illumination, and minimize quantization noise. The model strongly mimics brightness perception processes in early biological vision.

关键词： Brightness Dynamic range image sensors Lighting Quantization Biological system modeling Working environment noise computer vision Application software Signal processing

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An affine invariant tensor dissimilarity measure and its applications to tensor-valued image segmentation

An affine invariant tensor dissimilarity measure and its app...

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conference on computer Vision and pattern recognition (CVPR)

作者： Zhizhou Wang B.C. Vemuri Department of CISE University of Florida Gainesville FL USA

Tensor fields specifically, matrix valued data sets, have recently attracted increased attention in the fields of image processing. computer vision, visualization and medical imaging. In this paper, we present a novel definition of tensor "distance" grounded in concepts from information theory and incorporate it in the segmentation of tensor-valued images. In some applications, a symmetric positive definite (SPD) tensor at each point of a tensor valued image can be interpreted as the covariance matrix of a local Gaussian distribution. Thus, a natural measure of dissimilarity between SPD tensors would be the KL divergence or its relative. We propose the square root of the J-divergence (symmetrized KL) between two Gaussian distributions corresponding to the tensors being compared that leads to a novel closed form expression. Unlike the traditional Frobenius norm-based tensor distance, our "distance" is affine invariant, a desirable property in many applications. We then incorporate this new tensor "distance" in a region based active contour model for bimodal tensor field segmentation and show its application to the segmentation of diffusion tensor magnetic resonance images (DT-MRI) as well as for the texture segmentation problem in computer vision. Synthetic and real data experiments are shown to depict the performance of the proposed model.

关键词： Tensile stress image segmentation Application software computer vision Gaussian distribution Magnetic field measurement image processing Data visualization Biomedical imaging Information theory

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Camera calibration from a single night sky image

Camera calibration from a single night sky image

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conference on computer Vision and pattern recognition (CVPR)

作者： A. Klaus J. Bauer K. Karner P. Elbischger R. Perko H. Bischof VRVis Research Center Graz Austria Institute of Computer Graphics and Vision Graz Austria

We present a simple and universal camera calibration method. Instead of extensive setups we are exploiting the accurate angular positions of fixed stars. High precision is achieved by compensating the interfering error sources. Our approach uses a star catalog and requires a single input image only. No additional user input information such as focal length, exposure date or position is required. Fully automatic processing.and fast convergence is achieved by performing three consecutive steps. First, a star segmentation and centroid finding algorithm extracts the sub-pixel positions of the luminaries. Second, an initial solution for the most essential parameters is determined by combinatorial analysis. Finally, the Levenberg-Marquardt algorithm is applied to solve the resulting non-linear system. Experimental results with several digital consumer cameras demonstrate high robustness and accuracy. The introduced method is advisable for applications where large calibration targets are required.

关键词： Calibration Nonlinear distortion computer vision Lenses Optical distortion Optical noise Data mining Digital cameras image resolution Adaptive optics

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Accurate face models from uncalibrated and ill-lit video sequences

Accurate face models from uncalibrated and ill-lit video seq...

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conference on computer Vision and pattern recognition (CVPR)

作者： M. Dimitrijevic S. Ilic P. Fua Computer Vision Laboratory Swiss Federal Institute of Technology Lausanne Switzerland

We propose a face reconstruction technique that produces models that not only look good when texture mapped, but are also metrically accurate. Our method is designed to work with short uncalibrated video or movie sequences, even when the lighting is poor resulting in specularities and shadows that complicate the algorithm's task. Our approach relies on optimizing the shape parameters of a sophisticated PCA based model given pairwise image correspondences as input. All that is required is enough relative motion between camera and subject so that we can derive structure from motion. By matching the results against laser scanning data, we will show that its precision is excellent and can be predicted as a junction of the number and quality of the correspondences. This is important if one wishes to obtain the appropriate compromise between processing.speed and quality of the results. Furthermore, our method is in fact not specific to faces and could equally be applied to any shape for which a shape model controlled with relatively small number of parameters exists.

关键词： Video sequences Lighting Motion pictures image reconstruction Cameras Shape control Face detection computer vision Laser modes Head

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Fast, integrated person tracking and activity recognition with plan-view templates from a single stereo camera

Fast, integrated person tracking and activity recognition wi...

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conference on computer Vision and pattern recognition (CVPR)

作者： M. Harville Dalong Li Hewlett Packard Laboratories Palo Alto CA USA Center for Signal and Image Processing Georgia Institute of Technology Atlanta GA USA

Plan-view projection of real-time depth imagery can improve the statistics of its intrinsic 3D data, and allows for cleaner separation of occluding and closely-interacting people. We build a probabilistic, real-time multi-person tracking system upon a plan-view image substrate that well preserves both shape and size information of foreground objects. The tracking's robustness derives in part from its "plan-view template" person models, which capture detailed properties of people's body configurations. We demonstrate that these same person models, obtained with a single compact stereo camera unit, may also be used for fast recognition of body pose and activity. Principal components analysis is used to extract plan-view "eigenposes", onto which person models, extracted during tracking, are projected to produce a compact representation of human body configuration. We then formulate pose recognition as a classification problem, and use support vector machines (SVMs) to quickly distinguish between, for example, different directions people are facing, and different body poses such as standing, sitting, bending over, crouching, and reaching. The SVM outputs are transformed to probabilities and integrated across time in a probabilistic framework for real-time activity recognition.

关键词： Cameras Biological system modeling Support vector machines Support vector machine classification Statistics Real time systems Shape Robustness Principal component analysis Humans

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On region merging: The statistical soundness of fast sorting, with applications

On region merging: The statistical soundness of fast sorting...

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conference on computer Vision and pattern recognition

作者： Nielsen, F Nock, R FRL Sony CS Labs Shinagawa Ku Tokyo 1410022 Japan

This work explores a statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions. We exhibit a particular blend of algorithmics and statistics whose error is, as we formally show, close to the best possible. This approach can be approximated in a very fast segmentation algorithm for processing.images described using most common numerical feature spaces. Simple modifications of the algorithm allow to cope with occlusions and/or hard noise levels. Experiments on grey-level and color images, obtained with a short C-code, display the quality of the segmentations obtained.

关键词： computer vision

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Vector-valued image regularization with PDE's:: A common framework for different applications

Vector-valued image regularization with PDE's:: A common fra...

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conference on computer Vision and pattern recognition

作者： Tschumperlé, D Deriche, R INRIA Odyssee Lab F-06902 Sophia Antipolis France

ISBN: (纸本)0769519008

We address the problem of vector-valued image regularization with variational methods and PDE's. From the study of existing formalisms, we propose a unifying framework based on a very local interpretation of the regularization processes. The resulting equations are then specialized into new regularization PDE's and corresponding numerical schemes that respect the local geometry of vector-valued images. They are finally applied on a wide variety of image processing.problems, including color image restoration, inpainting, magnification and flow visualization.

关键词： image analysis

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Background subtraction based on cooccurrence of image variations

Background subtraction based on cooccurrence of image variat...

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conference on computer Vision and pattern recognition

作者： Seki, M Wada, T Fujiwara, H Sumi, K Mitsubishi Electr Corp Adv Technol R&D Ctr Hyogo 6618661 Japan

This paper presents a novel background subtraction method for detecting foreground objects in dynamic scenes involving swaying trees and fluttering flags. Most methods proposed so far adjust the permissible range of the background image variations according to the training samples of background images. Thus, the detection sensitivity decreases at those pixels having wide permissible ranges. If we can narrow the ranges by analyzing input images, the detection sensitivity can be improved. For this narrowing, we employ the property that image variations at neighboring image blocks have strong correlation, also known as "cooccurrence". This approach is essentially different from chronological background image updating or morphological postprocessing. Experimental results for real images demonstrate the effectiveness of our method.

关键词： Video signal processing

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conference on computer Vision and pattern recognition

作者： Keller, Y Averbuch, A Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

This paper presents an implicit similarity-based approach to registration of significantly dissimilar images, acquired by sensors of different modalities. The proposed algorithm introduces a robust matching criterion by aligning the locations of gradient maxima. The alignment is formulated as a parametric variational optimization problem which is solved iteratively by considering the intensities of a single image. The locations of the maxima of the second image's gradient are used as initialization., We were able to robustly estimate affine and projective global motions using 'coarse to fine' processing. even when the images are characterized by complex space varying intensity transformations. Finally, we present the registration of real images, which were taken by multi-sensor and multi-modality using affine and projective motion models.

关键词： image analysis

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Evaluation of local models of dynamic backgrounds

Evaluation of local models of dynamic backgrounds

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conference on computer Vision and pattern recognition

作者： Pless, R Larson, J Siebers, S Westover, B Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA

Background subtraction is the first step of many video surveillance applications. What is considered background varies by application, and may include regular, systematic, or complex motions. This paper explores the use of several different local spatio-temporal models of a background, defined at each pixel in the image. We present experiments with real image data and conclude that appropriate local representations are sufficient to make background models of complicated real world motions. Empirical studies illustrate, for example, that an optical flow-based model is able to detect emergency vehicles whose motion is different from those typically observed in traffic scenes. We conclude that "different models are appropriate for different scenes", but give criteria by which one can choose which model will be best.

关键词： Video signal processing

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