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Proceedings Paper

Feature extraction from a noisy background for use in neural network identifications
Author(s): Anyarat Boonnithrovalkul; Sirikanlaya Chanekasit; Chia-Lun John Hu
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Paper Abstract

The curves and lines in an edged-detected binary image can be analyzed using the adaptive-window detection technique. This window at first moves from the top-left corner of the image frame, and then scans horizontally and downward until it hits the starting point S of a “continuous” line or curve. Then it will automatically track the direction of the curve until it hits an end point E or a branch point B. The coordinates of the starting point S, the end point E or the branch point B will be automatically recorded in a data file, and so are the coordinates of all continuous points between S, E or S, B. For the branch point, the adaptive window will detect how many branches are connected to point B, and it will track automatically each branch until another end point or another branch point is hit. The coordinates of all continuous points between any pair of (cusp) points S, B; S, E; B, B; or B, E, will be automatically recorded in a different data file. Each data file then represents a single curve between 2 cusp points. These data file can then be used to find the analytical expression for each curve. We use polynomial, least square curve fitting techniques to get a very compact set of analytical data for representing or reconstructing the original binary image. This paper reports the image-processing steps, the programming algorithm, and the experimental results on this novel feature extraction technique. It will be verified in each experiment by the reconstruction of the original image from the compactly extracted analog data lines. These data lines can then be used very efficiently for inputting to a neural network for an accurate and yet robust pattern recognition job.

Paper Details

Date Published: 23 February 2005
PDF: 11 pages
Proc. SPIE 5673, Applications of Neural Networks and Machine Learning in Image Processing IX, (23 February 2005); doi: 10.1117/12.585061
Show Author Affiliations
Anyarat Boonnithrovalkul, Southern Illinois Univ./Carbondale (United States)
Sirikanlaya Chanekasit, Southern Illinois Univ./Carbondale (United States)
Chia-Lun John Hu, Southern Illinois Univ./Carbondale (United States)


Published in SPIE Proceedings Vol. 5673:
Applications of Neural Networks and Machine Learning in Image Processing IX
Nasser M. Nasrabadi; Syed A. Rizvi, Editor(s)

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