Two-dimensional minimum local cross-entropy thresholding based on co-occurrence matrix

Nie, Fangyan; Gao, Chao; Guo, Yongcai; Gan, Min

doi:10.1016/j.compeleceng.2011.06.006

Cited by 33 publications

(10 citation statements)

References 24 publications

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“…Excellent reviews on early thresholding methods can be found in [6]. Among all the thresholding methods, entropybased method is widely studied and is considered effectively [3]. Recently, Tsallis entropy was applied to the field of image thresholding for its excellent performance in the description of nonadditive information existing in images [7].…”

Section: Related Workmentioning

confidence: 99%

“…It intends to extract objects from background based on some pertinent characteristics in an image such as gray level, color, texture, and location [1]. Thresholding is one of the most popular segmentation approaches because of its simplicity [2,3]. It serves a variety of applications such as biomedical image analysis, character identification, and change detection [3].…”

Section: Introductionmentioning

confidence: 99%

“…Thresholding is one of the most popular segmentation approaches because of its simplicity [2,3]. It serves a variety of applications such as biomedical image analysis, character identification, and change detection [3]. Compared with visible images, the intensity of human targets in infrared images is obviously different from that of background.…”

Section: Introductionmentioning

confidence: 99%

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

Xia

Yang

2014

Mathematical Problems in Engineering

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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 fromO(L2)toO(L). 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.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Xia

Yang

2014

Mathematical Problems in Engineering

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“…UM and SM are the best-known objective evaluation criteria for evaluating the performance of image segmentation, and many literatures use the two criteria to judge the merits of the image segmentation methods [5,6,24].…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Among these methods, the thresholding approach is one the most popular technology be used because of its simplicity and effectiveness [5]. The entropy-based method is the famous technology for image segmentation in histogram thresholding methods [5][6][7]. The first entropy-based method is proposed by Pun [8], and then another entropy-based approach is proposed by Kapur et al through correcting the insufficiency in the Pun's method [9].…”

Section: Introductionmentioning

confidence: 99%