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- W2524572968 abstract "The recognition of objects in 3-dimensional space is an essential capability of the ideal computer vision system. Range images directly measure 3D coordinates of the visible portion of a scene and are well suited for this task. We report a procedure to identify 3D objects in range images, which makes use of four key processes. The first process segments the range image into surface by a clustering algorithm using points and associated normals. The second process classifies these patches as planar, convex, or concave based on a nonparametric statistical test for trend. The third process derives patch boundary information, and the results of this and the second process are used to merge compatible patches to produce reasonable object faces. The fourth process takes the patch and boundary information provided by the earlier stages and derives a representation of the range image. A list of salient features of the various objects in the database forms the core of an object recognition system, which looks for instances of these features in the representation. Occurrences of these salient features are interpreted as evidence for or against the hypothesis that a given object occurs in the scene. A measure of similarity between the set of observed features and the set of salient features for a given object in the database is used to determine the identity of an object in the scene or reject the object(s) in the scene as unknown. This evidence-based object recognition system correctly identified objects in 30 out of 31 range images. Four range images showing objects not included in the object database were rejected, as desired. Recognition degraded only slightly when evidence weights were perturbed." @default.
- W2524572968 created "2016-10-07" @default.
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- W2524572968 date "1986-01-01" @default.
- W2524572968 modified "2023-09-27" @default.
- W2524572968 title "Object recognition from range images (vision, evidence)" @default.
- W2524572968 hasPublicationYear "1986" @default.
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