A threshold segmentation method for sparse histogram image

  • Authors:

  • Hong Zhang;Jiulun Fan

  • Affiliations:

  • School of Electronic Engineering of Xidian University, Xi'an, Shaanxi, China and Department of Information and Control,Xi'an Institute of Post and Telecommunications, Xi'an, Shaanxi, China;Department of Information and Control,Xi'an Institute of Post and Telecommunications, Xi'an, Shaanxi, China

  • Venue:
  • FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
  • Year:
  • 2009

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Abstract

Maximum entropy thresholding method is a common image segmentation technology, several optimization algorithms are proposed based on maximum entropy objective function, these algorithms use subtraction instead of logarithm and multiplication and which are used in image segmentation. But for sparse histogram images, the segmentation based on the existing optimize methods is ineffective. In this paper, for sparse histogram image segmentation, an improved maximum entropy optimization algorithm is presented. Sparse histogram image segmentation experimental results show that more reasonable segmentation results can be obtained through using the algorithm.