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Author Bennamoun, Men_US
Author Boashash, Boualemen_US
Available date 2011-07-24T06:41:26Zen_US
Available date 2015-02-01T08:24:37Z
Publication Date 1997-12en_US
Publication Name IEEE Transactions On Systems Man and Cybernetics Part B-Cybernetics�
Citation IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,Volume : 27 , Issue:6 On page(s): 893en_US
ISSN 1083-4419en_US
URI http://hdl.handle.net/10576/10717en_US
URI http://dx.doi.org/10.1109/3477.650052
Abstract This paper presents the results of the integration of a proposed part-segmentation-based vision system. The first stage of this system extracts the contour of the object using a hybrid first- and second-order differential edge detector. The object defined by its contour is then decomposed into its constituent parts using the part segmentation algorithm given by Bennamoun (1994). These parts are then isolated and modeled with 2D superquadrics. The parameters of the models are obtained by the minimization of a best-fit cost function. The object is then represented by its structural description which is a set of data structures whose predicates represent the constituent parts of the object and whose arguments represent the spatial relationship between these parts. This representation allows the recognition of objects independently of their positions, orientations, or sizes. It is also insensitive to objects with partially missing parts. In this paper, examples illustrating the acquired images of objects, the extraction of their contours, the isolation of the parts, and their fitting with 2D superquadrics are reported. The reconstruction of objects from their structural description is illustrated and improvements are suggested.en_US
Language enen_US
Publisher IEEEen_US
Subject Cost functionen_US
Subject Detectorsen_US
Subject Filtersen_US
Subject Image edge detectionen_US
Subject Machine visionen_US
Subject Object detectionen_US
Subject Shapeen_US
Subject Signal processing algorithmsen_US
Subject Two dimensional displayen_US
Subject 2D super-quadricsen_US
Subject Gaussian filteren_US
Subject automatic object recognitionen_US
Subject best-fit cost functionen_US
Subject computer visionen_US
Subject contour extractionen_US
Subject convex pointen_US
Subject differential edge detectoren_US
Subject dominant pointen_US
Subject object recognitionen_US
Subject parameter selectionen_US
Subject part segmentationen_US
Subject structural-descriptionen_US
Subject invarianceen_US
Subject modelingen_US
Subject part isolationen_US
Subject Pattern recognitionen_US
Subject superquadricsen_US
Subject vision systemsen_US
Title A structural-description-based vision system for automatic object recognitionen_US
Type Articleen_US


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