Article ID Journal Published Year Pages File Type
527918 Computer Vision and Image Understanding 2009 16 Pages PDF
Abstract

This paper presents an algorithm that extracts robust feature descriptors from 2.5D range images, in order to provide accurate point-based correspondences between compared range surfaces. The algorithm is inspired by the two-dimensional (2D) Scale Invariant Feature Transform (SIFT) in which descriptors comprising the local distribution function of the image gradient orientations, are extracted at each sampling keypoint location over a local measurement aperture. We adapt this concept into the 2.5D domain by concatenating the histogram of the range surface topology types, derived using the bounded [−1, 1] shape index, and the histogram of the range gradient orientations to form a feature descriptor. These histograms are sampled within a measurement window centred over each mathematically derived keypoint location. Furthermore, the local slant and tilt at each keypoint location are estimated by extracting range surface normals, allowing the three-dimensional (3D) pose of each keypoint to be recovered and used to adapt the descriptor sampling window to provide a more reliable match under out-of-plane viewpoint rotation.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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