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Proceedings Paper

Improved thresholding method based on Tsallis-Havrda-Charvat entropy
Author(s): Jingjing Chen; Jin Wu
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Paper Abstract

This paper presents a thresholding method for image segmentation by using an improved thresholding output function on a two-dimensional (2-D) histogram based on Tsallis-Havrda-Charvat entropy principle. The Tsallis-Havrda-Charvat entropy is obtained from two-dimensional histogram which has determined by using the gray value of the pixels and the local average gray value of the pixels. Based on Tsallis-Havrda-Charvat entropy, we obtain the optimal threshold pair by maximizing the criterion function. The threshold pair groups the projection drawing of the 2-D histogram into four quadrants. Then we draw a line passing the optimal point. According to the line, we use the improved thresholding output function to separate the four quadrants into two parts, above the line and below the line. Therefore, the pixels are also grouped into two groups, targets and background. Experiment results show that the proposed method is robust to noise.

Paper Details

Date Published: 15 November 2007
PDF: 8 pages
Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 678650 (15 November 2007); doi: 10.1117/12.751262
Show Author Affiliations
Jingjing Chen, Wuhan Univ. of Science and Technology (China)
Jin Wu, Wuhan Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 6786:
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition
Tianxu Zhang; Tianxu Zhang; Carl Anthony Nardell; Carl Anthony Nardell; Hanqing Lu; Duane D. Smith; Hangqing Lu, Editor(s)

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