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We propose a novel shape descriptor for matching and recognizing 2D object silhouettes. The contour of each object is represented by a fixed number of sample points. For each sample point, a height function is defined based on the distances of the other sample points to its tangent line. One compact and robust shape descriptor is obtained by smoothing the height functions. The proposed descriptor...
Shape matching is a very critical problem in computer vision, and many smart features have been designed in recent literature for improving the similarity measure between pairs of shapes, and most of them consider either distribution of the sample contour points, or convexity/concavity property of the contour. In this paper, we design a novel shape feature to capture the Co-Occurrence Pattern (COP)...
Shape analysis has been a long standing problem in the literature. In this paper, we address the shape classification problem and make the following contributions: (1) We combine both contour and skeleton (also local and global) information for shape analysis, and we derive an effective classifier. (2) We collect a challenging shape database in which there are 20 categories of animals, with each having...
In this paper, we focus on the problem of detecting/matching a query object in a given image. We propose a new algorithm, shape band, which models an object within a bandwidth of its sketch/contour. The features associated with each point on the sketch are the gradients within the bandwidth. In the detection stage, the algorithm simply scans an input image at various locations and scales for good...
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