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• W1736593278 abstract "In this paper, we propose a method for extracting image features which utilizes 2nd order statistics, i.e., spatial and orientational auto-correlations of local gradients. It enables us to extract richer information from images and to obtain more discriminative power than standard histogram based methods. The image gradients are sparsely described in terms of magnitude and orientation. In addition, normal vectors on the image surface are derived from the gradients and these could also be utilized instead of the gradients. From a geometrical viewpoint, the method extracts information about not only the gradients but also the curvatures of the image surface. Experimental results for pedestrian detection and image patch matching demonstrate the effectiveness of the proposed method compared with other methods, such as HOG and SIFT." @default.
• W1736593278 created "2016-06-24" @default.
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• W1736593278 date "2008-01-01" @default.
• W1736593278 modified "2023-09-25" @default.
• W1736593278 title "Image Feature Extraction Using Gradient Local Auto-Correlations" @default.
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• W1736593278 doi "https://doi.org/10.1007/978-3-540-88682-2_27" @default.
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