2-D object recognition using invariant contour descriptor and projective refinement

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This paper presents an efficient model-based recognition method to recognize 2-D objects and to obtain correspondences between models and scene boundaries with a subpixel positioning error. As a shape signature for a contour, we propose a descriptor consisting of five-point invariants, which are used to index a hash table. Also, we propose a projective requirement as a verification method to compute exact correspondences between models and scene contour points. This method repeatedly computes projective transformation using a weighted pseudo inverse. We present an error model for five-point invariants, which are used to define a similarity between two descriptors, to determine a searching bound in indexing, and to obtain the weights in the projective refinement. In experiments using seriously distorted images of forty models, this method led to the recognition of planar curved objects. A transformation using the correspondence between the model and scene points on contours was also obtained. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Publisher
ELSEVIER SCI LTD
Issue Date
1998-04
Language
English
Article Type
Article
Keywords

FOURIER DESCRIPTORS; SHAPE

Citation

PATTERN RECOGNITION, v.31, no.4, pp.441 - 455

ISSN
0031-3203
URI
http://hdl.handle.net/10203/20621
Appears in Collection
EE-Journal Papers(저널논문)
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