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Proceedings Paper

Extraction of a compact analytical data file from a complex binary image for use in real-time learning
Author(s): Chia-Lun John Hu
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Paper Abstract

For a binary image containing only curves (and lines) in a background infested by binary noises, (e.g., salt-and-pepper noise,) a very efficient way to extract the image data, and to save them in a very compact file for an accurate and complete image recall later, is very attractive to many image processing and pattern recognition systems. This paper reports the data-extraction method we developed recently for inputting a binary image to a special neural-network pattern recognition system, the noniterative, real-time learning system. We use an adaptive/tracking window to track the direction of a continuous curve in the binary image, and record the xy-coordinates of all points on this curve until the window hits an end point, or a branch point, or the original starting point. By scanning this tracking window across the whole image frame, we can then segment the original binary image into many single curves. The xy’s of points on each curve can then be analyzed by a curve fitting process, and the analytic data can be stored very compactly in an analog data file. This data file can be recalled very efficiently to reconstruct the original binary image, or can be used directly for inputting to a special neural network and for carrying out an extremely fast pattern learning process. This paper reports the image-processing steps, the programming algorithm, and the experimental results on this novel image extraction technique. It will be verified in each experiment by reconstructing the original image from the compactly extracted analog data file.

Paper Details

Date Published: 17 January 2005
PDF: 6 pages
Proc. SPIE 5675, Vision Geometry XIII, (17 January 2005); doi: 10.1117/12.586922
Show Author Affiliations
Chia-Lun John Hu, Southern Illinois Univ. (United States)

Published in SPIE Proceedings Vol. 5675:
Vision Geometry XIII
Longin Jan Latecki; David M. Mount; Angela Y. Wu, Editor(s)

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