Maximum Fuzzy Entropy and Immune Clone Selection Algorithm for Image Segmentation

Tian, Wenjie; Geng, Yu; Liu, Jicheng; Ai, Lan

doi:10.1109/apcip.2009.18

Cited by 10 publications

(2 citation statements)

References 5 publications

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“…Mutation method is given as follows: ， Aff means the current degree of affinity values [7,8] , β is a variation which controls the exponential decay, usually equals 0.15. ( , )…”

Section: Mutation Operatormentioning

confidence: 99%

Finger Vein Feature Extraction Based on Linear Weighting Function Immune Clone Algorithm

Zhou

et al. 2010

2010 International Conference on Computational Intelligence and Software Engineering

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To solve the misjudgment of noise and vein information in features extraction from low quality images, a novel method based on LWF (Linear weighting function) immune-clone algorithm is proposed in this paper. The method can produce initial antibody by using adaptive threshold method, obtain weighting function by curve fitting, and denoise and enhance border by linear weighting of the vein area. The function of affinity and concentration of antibodies helps to boost the growth of the vein information and suppress the interference of noise. Simulation results show that compared to other algorithms, finger vein pattern extracted by the algorithm proposed in this paper is more distinct and accurate. In addition, this algorithm, which can effectively retain the details of information, is especially suitable for features extraction from low quality finger vein images.

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“…Mutation method is given as follows: ， Aff means the current degree of affinity values [7,8] , β is a variation which controls the exponential decay, usually equals 0.15. ( , )…”

Section: Mutation Operatormentioning

confidence: 99%

Finger Vein Feature Extraction Based on Linear Weighting Function Immune Clone Algorithm

Zhou

et al. 2010

2010 International Conference on Computational Intelligence and Software Engineering

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show abstract

“…A nonparametric multilevel image segmentation algorithm has been developed by Otsu [6]. Many entropy based algorithm has also been proposed in the recent years including the Shannon entropy [7], Tsalli's entropy [8], Renyi's entropy [9] and a fuzzy entropy based algorithm [10] which effectively contributed to the advancement in the field of Image Segmentation. But the demerits, like longer computational time and complexity, could not be avoided.…”

Section: Introductionmentioning

confidence: 99%

A Type–II Fuzzy Entropy Based Multi-Level Image Thresholding Using Adaptive Plant Propagation Algorithm

Nag¹

2017

Preprint

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One of the most straightforward, direct and efficient approaches to Image Segmentation is Image Thresholding. Multi-level Image Thresholding is an essential viewpoint in many image processing and Pattern Recognition based real-time applications which can effectively and efficiently classify the pixels into various groups denoting multiple regions in an Image. Thresholding based Image Segmentation using fuzzy entropy combined with intelligent optimization approaches are commonly used direct methods to properly identify the thresholds so that they can be used to segment an Image accurately. In this paper a novel approach for multi-level image thresholding is proposed using Type II Fuzzy sets combined with Adaptive Plant Propagation Algorithm (APPA). Obtaining the optimal thresholds for an image by maximizing the entropy is extremely tedious and time consuming with increase in the number of thresholds. Hence, Adaptive Plant Propagation Algorithm (APPA), a memetic algorithm based on plant intelligence, is used for fast and efficient selection of optimal thresholds. This fact is reasonably justified by comparing the accuracy of the outcomes and computational time consumed by other modern state-of-the-art algorithms such as Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA) and Genetic Algorithm (GA).

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Fuzzy Entropy Approaches for Image Segmentation

Oliva

Elaziz

Hinojosa

2019

Studies in Computational Intelligence

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Maximum Fuzzy Entropy and Immune Clone Selection Algorithm for Image Segmentation

Cited by 10 publications

References 5 publications

Finger Vein Feature Extraction Based on Linear Weighting Function Immune Clone Algorithm

Finger Vein Feature Extraction Based on Linear Weighting Function Immune Clone Algorithm

A Type–II Fuzzy Entropy Based Multi-Level Image Thresholding Using Adaptive Plant Propagation Algorithm

Fuzzy Entropy Approaches for Image Segmentation

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