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检索条件"任意字段=MIPPR 2007: Pattern Recognition and Computer Vision"

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Pedestrian Detection in Infrared Images based on Local Shape Features

Pedestrian Detection in Infrared Images based on Local Shape...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Li Zhang Bo Wu Ram Nevatia Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

Use of IR images is advantageous for many surveillance applications where the systems must operate around the clock and external illumination is not always available. We investigate the methods derived from visible spectrum analysis for the task of human detection. Two feature classes (edgelets and HOG features) and two classification models(AdaBoost and SVM cascade) are extended to IR images. We find out that it is possible to get detection performance in IR images that is comparable to state-of-the-art results for visible spectrum images. It is also shown that the two domains share many features, likely originating from the silhouettes, in spite of the starkly different appearances of the two modalities.

关键词： Infrared detectors Infrared imaging Shape Surveillance Clocks Lighting Humans computer vision Image edge detection Support vector machines

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Learning and Matching Line Aspects for Articulated Objects

Learning and Matching Line Aspects for Articulated Objects

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Xiaofeng Ren Toyota Technical Institute Chicago IL USA

Traditional aspect graphs are topology-based and are impractical for articulated objects. In this work we learn a small number of aspects, or prototypical views, from video data. Groundtruth segmentations in video sequences are utilized for both training and testing aspect models that operate on static images. We represent aspects of an articulated object as collections of line segments. In learning aspects, where object centers are known, a linear matching based on line location and orientation is used to measure similarity between views. We use K-medoid to find cluster centers. When using line aspects in recognition, matching is based on pairwise cues of relative location, relative orientation as well adjacency and parallelism. Matching with pairwise cues leads to a quadratic optimization that we solve with a spectral approximation. We show that our line aspect matching is capable of locating people in a variety of poses. Line aspect matching performs significantly better than an alternative approach using Hausdorff distance, showing merits of the line representation.

关键词： Image segmentation Shape Video sequences computer vision Prototypes Testing Parallel processing Psychology Humans Machine vision

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Robust Real-Time Visual SLAM Using Scale Prediction and Exemplar Based Feature Description

Robust Real-Time Visual SLAM Using Scale Prediction and Exem...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Denis Chekhlov Mark Pupilli Walterio Mayol Andrew Calway Department of Computer Science University of Bristol UK

Two major limitations of real-time visual SLAM algorithms are the restricted range of views over which they can operate and their lack of robustness when faced with erratic camera motion or severe visual occlusion. In this paper we describe a visual SLAM algorithm which addresses both of these problems. The key component is a novel feature description method which is both fast and capable of repeat-able correspondence matching over a wide range of viewing angles and scales. This is achieved in real-time by using a SIFT-like spatial gradient descriptor in conjunction with efficient scale prediction and exemplar based feature representation. Results are presented illustrating robust realtime SLAM operation within an office environment.

关键词： Robustness Simultaneous localization and mapping Cameras Matched filters Robot vision systems Filtering computer science Layout Wearable computers Stochastic processes

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Learning Conditional Random Fields for Stereo

Learning Conditional Random Fields for Stereo

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Daniel Scharstein Chris Pal Middlebury College Middlebury VT USA University of Massachusetts Amherst Amherst MA USA

State-of-the-art stereo vision algorithms utilize color changes as important cues for object boundaries. Most methods impose heuristic restrictions or priors on disparities, for example by modulating local smoothness costs with intensity gradients. In this paper we seek to replace such heuristics with explicit probabilistic models of disparities and intensities learned from real images. We have constructed a large number of stereo datasets with ground-truth disparities, and we use a subset of these datasets to learn the parameters of conditional random fields (CRFs). We present experimental results illustrating the potential of our approach for automatically learning the parameters of models with richer structure than standard hand-tuned MRF models.

关键词： Stereo vision Costs Learning systems Training data Minimization methods Markov random fields Optimization methods Belief propagation Educational institutions Intensity modulation

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On Stabilisation of Parametric Active Contours

On Stabilisation of Parametric Active Contours

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Conference on computer vision and pattern recognition (CVPR)

作者： V. Srikrishnan Subhasis Chaudhuri Sumantra Dutta Roy Daniel Sevcovic Vision and Image Processing Lab EE Department Indian Institute of Technology Bombay Mumbai India Department of Applied Math and Stats. Comenius University Bratislava Slovakia

Parametric active contours have been used extensively in computer vision for different tasks like segmentation and tracking. However, all parametric contours are known to suffer from the problem of frequent bunching and spacing out of curve points locally during the curve evolution. In a spline based implementation of active contours, this leads to occasional formation of loops locally, and subsequently the curve blows up due to instabilities. It has been shown earlier that in addition to usual evolution along the normal direction, the curve should also be evolved in the tangential direction for stability purposes. In this paper, we provide a mathematical basis for selecting such a suitable tangential component for stabilisation. We prove the boundedness of the evolved curve in this paper, and provide the physical significance. We demonstrate the usefulness of the proposed method with a number of experiments.

关键词： Active contours Spline Image segmentation computer vision Image processing Stability Testing Equations Finite element methods Lagrangian functions

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A Quasi-Minimal Model for Paper-Like Surfaces

A Quasi-Minimal Model for Paper-Like Surfaces

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Conference on computer vision and pattern recognition (CVPR)

作者： Mathieu Perriollat Adrien Bartoli LASMEA-CNRS UBP Clermont Ferrand France

Smoothly bent paper-like surfaces are developable. They are however difficult to minimally parameterize since the number of meaningful parameters is intrinsically dependent on the actual deformation. Previous generative models are either incomplete, i.e. limited to subsets of developable surfaces, or depend on huge parameter sets. We propose a generative model governed by a quasi-minimal set of intuitive parameters, namely rules and angles. More precisely, a flat mesh is bent along guiding rules, while a number of extra rules controls the level of smoothness. The generated surface is guaranteed to be developable. A fully automatic multi-camera three dimensional reconstruction algorithm, including model-based bundle-adjustment, demonstrates our model on real images.

关键词： Surface fitting Surface reconstruction Face detection Automatic control Reconstruction algorithms Principal component analysis Cost function Linear approximation computer vision Cameras

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Improving Variance Estimation in Biometric Systems

Improving Variance Estimation in Biometric Systems

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Ross J. Micheals Terrance E. Boult Image Group Information Access Division Information Technology Laboratory National Institute for Standards and Technology Gaithersburg MD USA Vision and Software Technology Laboratory University of Colorado Colorado Springs Colorado Springs CO USA

Measuring system performance seems conceptually straightforward. However, the interpretation of the results and predicting future performance remain as exceptional challenges in system evaluation. Robust experimental design is critical in evaluation, but there have been very few techniques to check designs for either overlooked associations or weak assumptions. For biometric & vision system evaluation, the complexity of the systems make a thorough exploration of the problem space impossible - this lack of verifiability in experimental design is a serious issue. In this paper, we present a new evaluation methodology that improves the accuracy of variance estimator via the discovery of false assumptions about the homogeneity of cofactors - i.e., when the data is not ''well mixed". The new methodology is then applied in the context of a biometric system evaluation with highly influential cofactors.

关键词： Biometrics Design for experiments System performance Machine vision Sampling methods Springs Measurement computer vision Parameter estimation Iterative methods

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Towards Fog-Free In-Vehicle vision Systems through Contrast Restoration

Towards Fog-Free In-Vehicle Vision Systems through Contrast ...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Nicolas Hautiere Jean-Philippe Tarel Didier Aubert LCPC Paris France LIVIC-LCPC/INRETS Versailles France

In foggy weather, the contrast of images grabbed by in-vehicle cameras in the visible light range is drastically degraded, which makes the current applications very sensitive to weather conditions. An onboard vision system should take fog effects into account. The effects of fog varies across the scene and are exponential with respect to the depth of scene points. Because it is not possible in this context to compute the road scene structure beforehand contrary to fixed camera surveillance, a new scheme is proposed. Weather conditions are first estimated and then used to restore the contrast according to a scene structure which is inferred a priori and refined during the restoration process. Based on the aimed application, different algorithms with increasing complexities are proposed. Results are presented using sample road scenes under foggy weather and assessed by computing the contrast before and after restoration.

关键词： Machine vision Layout Image restoration Cameras Roads Degradation Vehicles Extinction coefficients Surveillance Radar measurements

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A Bayesian algorithm for tracking multiple moving objects in outdoor surveillance video

A Bayesian algorithm for tracking multiple moving objects in...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Manjunath Narayana Donna Haverkamp University of Kansas Lawrence KS USA

Reliable tracking of multiple moving objects in video is an interesting challenge, made difficult in real-world video by various sources of noise and uncertainty. We propose a Bayesian approach to find correspondences between moving objects over frames. By using color values and position information of the moving objects as observations, we probabilistically assign tracks to those objects. We allow for tracks to be lost and then recovered when they resurface. The probabilistic assignment method, along with the ability to recover lost tracks, adds robustness to the tracking system. We present results that show that the Bayesian method performs well in difficult tracking cases and compare the probabilistic results to a Euclidean distance based method.

关键词： Bayesian methods Surveillance Probability Target tracking Object detection Motion segmentation computer vision Predictive models Matched filters Uncertainty

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Autocalibration and Uncalibrated Reconstruction of Shape from Defocus

Autocalibration and Uncalibrated Reconstruction of Shape fro...

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Conference on computer vision and pattern recognition (CVPR)

作者： Yifei Lou Paolo Favaro Andrea L. Bertozzi Stefano Soatto UCLA Los Angeles CA USA Heriot-Watt University Edinburgh UK UCLA Los Angeles USA

Most algorithms for reconstructing shape from defocus assume that the images are obtained with a camera that has been previously calibrated so that the aperture, focal plane, and focal length are known. In this manuscript we characterize the set of scenes that can be reconstructed from defocused images regardless of calibration parameters. In lack of knowledge about the camera or about the scene, reconstruction is possible only up to an equivalence class that is described analytically. When weak knowledge about the scene is available, however, we show how it can be exploited in order to auto-calibrate the imaging device. This includes imaging a slanted plane or generic assumptions on the restoration of the deblurred images.

关键词： Shape Layout Image reconstruction Cameras Calibration Apertures Image restoration Frequency computer vision Deconvolution

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