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17 April 2006 Efficient image preprocessing for topological pattern recognition
Chia-Lun John Hu, Anyarat Boonnithivorakul
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Proceedings Volume 6245, Optical Pattern Recognition XVII; 62450M (2006) https://doi.org/10.1117/12.664205
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States
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.
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Chia-Lun John Hu and Anyarat Boonnithivorakul "Efficient image preprocessing for topological pattern recognition", Proc. SPIE 6245, Optical Pattern Recognition XVII, 62450M (17 April 2006); https://doi.org/10.1117/12.664205
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KEYWORDS
Pattern recognition
Binary data
Analog electronics
Target recognition
Image analysis
Automatic tracking
Feature extraction
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