Wide baseline matching is the state of the art for object recognition and image registration problems in computervision. Though effective, the computational expense of these algorithms limits their application to man...
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Wide baseline matching is the state of the art for object recognition and image registration problems in computervision. Though effective, the computational expense of these algorithms limits their application to many real-world problems. The performance of wide baseline matching algorithms may be improved by using a graphical processing unit as a fast multithreaded co-processor. In this paper, we present an implementation of the difference of Gaussian feature extractor, based on the CUDA system of GPU programming developed by NVIDIA, and implemented on their hardware. For a 2000x2000 pixel image, the GPU-based method executes nearly thirteen times faster than a comparable CPU-based method, with no significant loss of accuracy.
In this paper, we propose a matching method for remote sensing images based on corner structures. Firstly corner angle vector is defined to analysis corner structure, and the process of how to obtain it is discussed i...
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In this paper, we propose a matching method for remote sensing images based on corner structures. Firstly corner angle vector is defined to analysis corner structure, and the process of how to obtain it is discussed in detail. Then some measures are given to eliminate the false corners. Finally a relaxation matching scheme is presented. The experiments show the effectiveness and feasibility of our matching method.
Instead of traditionally using a 3D physical model with many control points on it, a calibration plate with printed chess grid and movable along its normal direction is implemented to provide large area 3D control poi...
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Instead of traditionally using a 3D physical model with many control points on it, a calibration plate with printed chess grid and movable along its normal direction is implemented to provide large area 3D control points with variable Z values. Experiments show that the approach presented is effective for reconstructing 3D color objects in computervision system.
Three different localized representation methods and a manifold learning approach to face recognition are compared in terms of recognition accuracy. The techniques under investigation are (a) local nonnegative matrix ...
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Three different localized representation methods and a manifold learning approach to face recognition are compared in terms of recognition accuracy. The techniques under investigation are (a) local nonnegative matrix factorization (LNMF);(b) independent component analysis (ICA);(c) NMF with sparse constraints (NMFsc);(d) locality-preserving projections (Laplacian faces). A systematic comparative analysis is conducted in terms of distance metric used, number of selected features, and sources of variability on AR and Olivetti face databases. Results indicate that the relative ranking of the methods is highly task-dependent, and the performances vary significantly upon the distance metric used. Copyright (C) 2007 I. B. Ciocoiu and H. N. Costin.
This paper describes a real-time multi-camera surveillance system that can be applied to a range of application domains. This integrated system is designed to observe crowded scenes and has mechanisms to improve track...
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GESTALT is a production system interpreter designed for advanced automatic recognition from difficult pictorial data. Building recognition from leading edge high resolution SAR-data is a good example for such a challe...
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GESTALT is a production system interpreter designed for advanced automatic recognition from difficult pictorial data. Building recognition from leading edge high resolution SAR-data is a good example for such a challenge. This contribution explains the system itself and its application to this particular issue. Perceptual grouping paradigms are coded in the productions in order to discriminate man-made ordered structure from arbitrary clutter. In particular symmetry and repetitive similar structure render promising prospects for this application.
Statistical learning based face detection systems search multiple scale sub-frames of an image or frame of a video stream with a trained classifier to detect face objects. If the frame is large there will be a large n...
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ISBN:
(纸本)9781424431625
Statistical learning based face detection systems search multiple scale sub-frames of an image or frame of a video stream with a trained classifier to detect face objects. If the frame is large there will be a large number of these sub-frames. Sections of the frame with a regional variance below a predefined threshold do not need to be searched as it is not possible for a face to exist in these spaces. A preprocessing system to eliminate these low variance regions of a frame is presented in this paper. A top down quad-tree deconstruction of the frame is used to accomplish this task. Regional variance is computed and tested to determine if a given quadrant should be further broken down. If below a predefined threshold that region will be eliminated in a mask image. This procedure is continued until all sections are eliminated or a defined tree depth is reached. The resulting mask image is then smoothed and subjected to thresholding, merging the remaining valid search areas. The resulting filled mask is then used to determine whether a given sub-frame should be sent to a Viola-Jones face detection cascade. Preliminary results show promise in reducing the number of sub-frames that must be considered for detection, increasing the speed of the detection system.
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