A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation

Hammouche, Kamal; Diaf, Moussa; Siarry, Patrick

doi:10.1016/j.cviu.2007.09.001

Cited by 219 publications

(80 citation statements)

References 20 publications

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“…Untuk mengatasi masalah ini, Hammouche dkk. [10] menggunakan genetic algorithm (GA) yang mengoptimasi ATC yang diajukan Yen, dkk. Metode GA pada penelitian tersebut memiliki peranan yang sangat besar dalam menentukan titik-titik threshold pada histogram derajat keabuan, sehingga dapat menentukan jumlah threshold yang sesuai dan nilai threshold yang memadai.…”

Section: Pendahuluanunclassified

“…Meski memiliki peranan dalam proses optimasi ATC [10], GA yang digunakan sebagai metode dalam menentukan jumlah, posisi, dan nilai threshold pada segmentasi citra mungkin saja terjebak dalam solusi local optimal. Mengingat beberapa kelemahan GA yaitu dapat konvergen secara dini (premature convergence) dan tanpa positive feedback, sehingga tidak ada jaminan bahwa solusi yang diberikan adalah solusi yang terbaik (global optimal) yang tentu saja berpengaruh langsung terhadap hasil thresholding.…”

Section: Pendahuluanunclassified

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Hybrid Genetic Algorithmdan Ant Colony Optimizationuntuk Optimisasi Metode Multilevel Image Thresholding

Pradnyana¹,

Suputra²

2011

JUTI

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Section: Pendahuluanunclassified

Hybrid Genetic Algorithmdan Ant Colony Optimizationuntuk Optimisasi Metode Multilevel Image Thresholding

Pradnyana¹,

Suputra²

2011

JUTI

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“…Tao et al [8] presented a three-level thresholding method that uses the GA to find the optimal thresholds by maximizing the fuzzy entropy. Hammouche et al [9] proposed a multi-level thresholding method, which allows the determination of the appropriate number of threshols as well as the adequate threshold values. However, GA has some drawbacks such as slow convergence rate, premature convergence to local minima.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method Based on Tissue Membrane Systems and Velocity-Position Model for Image Thresholding

Yi¹,

Zhang²,

Peng³

2014

Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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Abstract-This paper proposes an image thresholding segmentation method, which combines tissue membrane systems and velocity-position model. A tissue membrane system is used as its computing framework and an improved velocity-position model is integrated as evolution rules of objects in cells. Due to parallel computing ability and inherent evolution-communication mechanism of the tissue membrane system, the presented hybrid method can effectively and efficiently find the optimal thresholds for three-level thresholding based on total fuzzy entropy. The performance of the presented hybrid method is studied with several evolutionary algorithms. Simulation results show that the presented hybrid method is superior or comparable to the other evolutionary algorithms and can be efficiently used for image thresholding.

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“…Beside PSO, GAs technique has also been used to solve the multi-level thresholding problems as well as computer vision tasks (Scheunders, 1996;Tao et al, 2003;Yin, 1999;Hammouche et al, 2008;Cao et al, 2008). GAs are probabilistic search algorithms based on the mechanics of natural selection and naturally occurring genetic operations.…”

Section: Introductionmentioning

confidence: 99%

“…However, in these studies, the time-consuming issue had to be dealt with when the threshold number increased. The best proposed method so far in terms of the accuracy of image segmentation and computation time was developed by Hammouche et al (2008), which used a combination of GA and wavelet transform. Recently, GA with a novel learning strategy, the strongest scheme in each iterative computation, was proposed to accelerate the convergence speed for multilevel thresholding by Cao et al (2008).…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Thresholding based on Non-Parametric Approaches for Fast Segmentation

Cho¹,

Duy²,

Han³

et al. 2013

Journal of Biosystems Engineering

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Purpose:In image segmentation via thresholding, Otsu and Kapur methods have been widely used because of their effectiveness and robustness. However, computational complexity of these methods grows exponentially as the number of thresholds increases due to the exhaustive search characteristics. Methods: Particle swarm optimization (PSO) and genetic algorithms (GAs) can accelerate the computation. Both methods, however, also have some drawbacks including slow convergence and ease of being trapped in a local optimum instead of a global optimum. To overcome these difficulties, we proposed two new multi-level thresholding methods based on Bacteria Foraging PSO (BFPSO) and real-coded GA algorithms for fast segmentation. Results: The results from BFPSO and real-coded GA methods were compared with each other and also compared with the results obtained from the Otsu and Kapur methods. Conclusions: The proposed methods were computationally efficient and showed the excellent accuracy and stability. Results of the proposed methods were demonstrated using four real images.

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A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation

Cited by 219 publications

References 20 publications

Hybrid Genetic Algorithmdan Ant Colony Optimizationuntuk Optimisasi Metode Multilevel Image Thresholding

Hybrid Genetic Algorithmdan Ant Colony Optimizationuntuk Optimisasi Metode Multilevel Image Thresholding

A Hybrid Method Based on Tissue Membrane Systems and Velocity-Position Model for Image Thresholding

Multi-Level Thresholding based on Non-Parametric Approaches for Fast Segmentation

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