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Proceedings Paper

Use of syntactic recognition with sampled boundary distances
Author(s): David T. Wang; Ming-Chien Peng; Jueen Lee; Jyh-Woei Chen; Peter A. Ng
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Paper Abstract

An approach of syntactic recognition (SR) using sampled boundary distances (SBD) is studied. The SBD is an ordered collection of samples of distance defined from a major axis to points located on the boundary of the object image. With the SRSBD approach the object undergoes many affine and non-affine transformations can be recognized. The affine transformations include translation rotation scaling and stretching (along and/or perpendicular to the major axis). The non-affine transformations include i) additive transformation applied to the distance of all the boundary points (perpendicularly) from the major axis ii) additive transformation applied to the SBD only and iii) random transformation of all the boundary points except the points used to measure the sampled boundary distances provided that the major axis is unchanged. Therefore the SRSBD can be used to recognize an object at various locations orientations and distances from the camera and various objects of the same family. The conversion of the SBD into an invariant string representation is developed. The use of this Earley''s parsing algorithm for recognition of the string representation of SBD is presented. The use of SRSBD to recognize partially obscured object or object family and to detect circularity of a partially obscured circle is presented. The experimental results are presented. With the SRSBD the following problems can be avoided the primitive selection problem the starting point selection problem and the problem of the noise-sensitive

Paper Details

Date Published: 1 March 1991
PDF: 14 pages
Proc. SPIE 1386, Machine Vision Systems Integration in Industry, (1 March 1991); doi: 10.1117/12.25395
Show Author Affiliations
David T. Wang, New Jersey Institute of Technology (United States)
Ming-Chien Peng, New Jersey Institute of Technology (United States)
Jueen Lee, New Jersey Institute of Technology (United States)
Jyh-Woei Chen, New Jersey Institute of Technology (United States)
Peter A. Ng, New Jersey Institute of Technology (United States)


Published in SPIE Proceedings Vol. 1386:
Machine Vision Systems Integration in Industry
Bruce G. Batchelor; Frederick M. Waltz, Editor(s)

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