A new nonlinear quantizer for image processing within nonextensive statistics

Kılıç, İlker; Kayacan, Ozhan

doi:10.1016/j.physa.2007.03.028

Cited by 5 publications

(3 citation statements)

References 60 publications

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“…7.84), facial expression recognition (see Fig. 7.85), among others [755][756][757][758][759][760][761][762][763][764][765][766][767][768][769][770][771][772]. …”

Section: Analysis Of Imagesmentioning

confidence: 99%

Introduction to Nonextensive Statistical Mechanics

Tsallis¹

2009

279

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“…7.84), facial expression recognition (see Fig. 7.85), among others [755][756][757][758][759][760][761][762][763][764][765][766][767][768][769][770][771][772]. …”

Section: Analysis Of Imagesmentioning

confidence: 99%

Introduction to Nonextensive Statistical Mechanics

Tsallis¹

2009

279

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“…In nature, the image is a typical physical system with nonextensive characteristics [3]. However, the traditional thresholding methods often neglect the effec ts of nonextensive information on threshold selection, and thus no good thresholding results can be obtained sometimes.…”

Section: Introductionmentioning

confidence: 99%

A Three-Level Thresholding Technique based on Nonextensive Entropy and Fuzzy Partition with Artificial Bee Colony Algorithm

Nie¹

2015

IJHIT

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In this paper, a new three-level thresholding method for image segmentation is proposed based on nonextensive entropy and fuzzy sets theory. Firstly, the image histogram is transformed from crisp set to fuzzy domain using fuzzy membership function, such as triangular membership function. After that, the nonextensive entropy of each part of fuzzy domain of histogram is computed. The threshold is selected by maximizing the nonextensive fuzzy entropy. However, the search of combination of membership function's parameters is costly. For reduce the computation time, the artificial bee colony algorithm is used to search the optimal combination of the membership function's parameters. The experimental results on tested images demonstrate the success of the proposed approach compared with the competing methods.

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“…Fuzzy theory can be used to deal with these ambiguity and uncertainty in image thresholding, and many thresholding methods have proposed based on fuzzy sets theory in recent years [4][5][6]. In addition, the image is a typical physical system with nonextensive characteristics [7]. However, the ordinary thresholding methods often neglect the effects of nonextensive information on threshold selection, and thus no good results can be obtained sometimes [8].…”

Section: Introductionmentioning

confidence: 99%

Tri-level Thresholding using Invasive Weed Optimization based on Nonextensive Fuzzy Entropy

BinFang¹,

Li²,

Nie³

2014

IJSIP

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This study presents a tri-level thresholding method for image segmentation with invasive weed optimization (IWO) algorithm. The objective of the proposed approach is to handle the nonextensivity and vagueness of image in segmentation, in the meanwhile to reduce the computation time. In this study, the histogram of image is converted to fuzzy domain by membership function firstly. Then the thresholding method is constructed through maximizing the sum of nonextensive entropy of subsets of the each part of fuzzy histogram. The IWO algorithm is used to search the optimal thresholds to reduce the computation time in the new method. Experiments on synthetic and real-world images are given to demonstrate the effectiveness of the proposed approach compared with the other methods.

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A new nonlinear quantizer for image processing within nonextensive statistics

Cited by 5 publications

References 60 publications

Introduction to Nonextensive Statistical Mechanics

Introduction to Nonextensive Statistical Mechanics

A Three-Level Thresholding Technique based on Nonextensive Entropy and Fuzzy Partition with Artificial Bee Colony Algorithm

Tri-level Thresholding using Invasive Weed Optimization based on Nonextensive Fuzzy Entropy

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