Fast Threshold Selection Algorithm of Infrared Human Images Based on Two-Dimensional Fuzzy Tsallis Entropy

Dong-xue Xia, Chun-gui Li and Shu-hong Yang

Mathematical Problems in Engineering, 2014, vol. 2014, 1-10

Abstract:

Infrared images are fuzzy and noisy by nature; thus the segmentation of human targets in infrared images is a challenging task. In this paper, a fast thresholding method of infrared human images based on two-dimensional fuzzy Tsallis entropy is introduced. First, to address the fuzziness of infrared image, the fuzzy Tsallis entropy of objects and that of background are defined, respectively, according to probability partition principle. Next, this newly defined entropy is extended to two dimensions to make good use of spatial information to deal with the noise in infrared images, and correspondingly a fast computation method of two-dimensional fuzzy Tsallis entropy is put forward to reduce its computation complexity from to . Finally, the optimal parameters of fuzzy membership function are searched by shuffled frog-leaping algorithm following maximum entropy principle, and then the best threshold of an infrared human image is computed from the optimal parameters. Compared with typical entropy-based thresholding methods by experiments, the method presented in this paper is proved to be more efficient and robust.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2014/308164.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2014/308164.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:308164

DOI: 10.1155/2014/308164

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().