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Proceedings Paper

Efficient image preprocessing for topological pattern recognition
Author(s): Chia-Lun John Hu; Anyarat Boonnithivorakul
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Paper Abstract

As we published last year, we have developed a very efficient image pre-processing scheme for using in any image analyzing system or any pattern recognition system. This scheme will analyze an edge-detected binary image and break it down to many simple branches by the automatic line tracking method we published earlier. Each branch can then be curve-fitted with the standard Window operations and it will result in an analog output which contains the starting point xy-coordinates, the ending point xy-coordinates, the polynomial degree, the coefficients in the best-fit algebra expression, and the angle of rotation to make the polynomial fitting work. The original binary image then can be closely reconstructed using this compact analog data. The reconstructed image is seen to be highly accurate compared to the original image in all our experiments. This paper reports the description of the topological structure of the original binary image detected by this novel image pre-processing method. That is, it will tell us how many branching points, how many single-ended points will be detected, and what algebraic curves are connected among them. This "topological" description of the image is not only very specific, but also very robust because when the image is viewed in different elevations and different directions, even though the geometrical shape changes, the topological or syntactical description will NOT change. Therefore it can be used in very fast learning, and very robust, yet very accurate, real-time recognition.

Paper Details

Date Published: 17 April 2006
PDF: 9 pages
Proc. SPIE 6245, Optical Pattern Recognition XVII, 62450M (17 April 2006); doi: 10.1117/12.664205
Show Author Affiliations
Chia-Lun John Hu, Southern Illinois Univ./Carbondale (United States)
Anyarat Boonnithivorakul, Southern Illinois Univ./Carbondale (United States)

Published in SPIE Proceedings Vol. 6245:
Optical Pattern Recognition XVII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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