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Many computer vision algorithms for pattern matching, object tracking, and 3-D reconstruction, etc., begin with feature detection and matching. Common feature detectors such as Harris, Sobel, Canny, and Difference of Gaussians perform basic linear algebra operations on an image in order to identify "corners" or "edges" for matching. These detectors however, require single-channel...
Most of the quasi-dense matching algorithms are designed for general scene structures. In reality, there are many man-made objects in the scene, such as for example buildings and furniture which include many planar regions. For this case, plane induced homography is a useful tool for matching. In this paper, we present a novel quasi-dense matching method for fisheye images which is based on plane...
A method of object detecting based on local contour learning and matching is proposed. Firstly, the representative images are obtained through unsupervised clustering to be as templates. The local contour information of template is extracted and generalized as the template feature, at the same time, codebook dictionary of local contour is built up. Secondly, based on codebook dictionary, using simple...
No feature-based vision system can work unless good features can be identified and tracked from frame to frame. This paper addresses robust feature tracking. We extend the well-known Shi-Tomasi-Kanade tracker by introducing a simple scheme for rejecting spurious features. Interest points extracted with the Harris-SIFT detector can be adapted to affine transformations and give repeatable results. In...
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