Improvement of grayscale image 2D maximum entropy threshold segmentation method

Zheng, Liping; Li, Guangyao; Bao, Yun

doi:10.1109/iclsim.2010.5461410

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…Kapur's entropy and Otsu-based between-class variance as fitness functions in PSO were used to find multiple thresholds for segmentation [19]. An information entropy technique based on a two-dimensional histogram (image graylevels and means) was also used to find good thresholds by PSO [20]. Also, an unfixed number of thresholds are optimised by PSO, cooperating with Fuzzy means [21].…”

Section: B Image Segmentation Using Psomentioning

confidence: 99%

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Gaussian mixture models and information entropy for image segmentation using particle swarm optimisation

Johnston

Zhang

2013

2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013)

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Image segmentation is a key step in image analysis. The Gaussian Mixture Model (GMM) method based on image histograms is popular in image segmentation, but it is difficult to find good parameters of Gaussian models. A hybrid Particle Swarm Optimisation (PSO) algorithm has been used to effectively search the parameters of the models for image segmentation. However, the segmentation results from the fitted models are not stable. In this study, the parameters are optimised by developing a new fitness function in PSO based on information entropy. A combination of the entropy and goodness of the approximation on the image histogram is proposed as a new fitness function further. The results show that the fitness function combining the entropy and goodness of the approximation can be effectively used to obtain better segmentation results than only considering the goodness of the approximation on the image histogram, in terms of intensity errors and the information entropy.

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Section: B Image Segmentation Using Psomentioning

confidence: 99%

“…Information entropy is not considered in GMM. However, entropy-based techniques have been widely applied to image segmentation [14] [20]. In [11], an optimal threshold was obtained based on entropy information from GMM.…”

Section: B Fitness Function Using Information Gainmentioning

confidence: 99%

Gaussian mixture models and information entropy for image segmentation using particle swarm optimisation

Johnston

Zhang

2013

2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013)

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“…Qingyong Li et al [13] proposed a double-scale non-linear thresholding method based on vessel support regions. In clustering methods, Zheng et al [14] proposed the improved gray image maximum entropy segmentation method to replace the gray probability by the spatial information value and the two-dimensional difference attribute information entropy is generated. The algorithm has anti-noise ability.…”

Section: Introductionmentioning

confidence: 99%

3D Flow Entropy Contour Fitting Segmentation Algorithm Based on Multi-Scale Transform Contour Constraint

Liu

Lan

2019

Symmetry

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Image segmentation is a crucial topic in image analysis and understanding, and the foundation of target detection and recognition. Image segmentation, essentially, can be considered as classifying the image according to the consistency of the region and the inconsistency between regions, it is widely used in medical and criminal investigation, cultural relic identification, monitoring and so forth. There are two outstanding common problems in the existing segmentation algorithm, one is the lack of accuracy, and the other is that it is not widely applicable. The main contribution of this paper is to present a novel segmentation method based on the information entropy theory and multi-scale transform contour constraint. Firstly, the target contour is initially obtained by means of a multi-scale sample top-hat and bottom-hat transform and an improved watershed method. Subsequently, in terms of this initial contour, the interesting areas can be finely segmented out with an innovative 3D flow entropy method. Finally, the sufficient synthetic and real experiments proved that the proposed algorithm can greatly improve the segmentation effect. In addition, it is widely applicable.

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“…To solve this problem, many scholars attempt to achieve fast optimization for the 2D threshold by combining 2D threshold segmentation method and optimization algorithm. Shen et al [15], Zheng et al [16], and Alim et al [17] sought to figure out the optimal threshold of 2D maximum entropy, respectively, through genetic algorithm, ant colony algorithm, PSO (Particle Swarm Optimization) algorithm, and ABCO (Artificial Bee Colony Optimization). Qian used PSO algorithm to find the optimal threshold of 2D-Otsu [18].…”

Section: Introductionmentioning

confidence: 99%

Automatic Change Detection Method of Multitemporal Remote Sensing Images Based on 2D-Otsu Algorithm Improved by Firefly Algorithm

et al. 2015

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This paper presents a new automatic change detection method of multitemporal remote sensing images based on 2D-Otsu algorithm improved by Firefly algorithm. The proposed method is designed to automatically extract the changing area between two temporal remote sensing images. First, two different temporal remote sensing images were acquired through difference value method of remote sensing images; then, the 2D-Otsu threshold segmentation principles are analyzed and the optimal threshold of 2D-Otsu threshold segmentation method is figured out by using the Firefly algorithm, where the difference images are conducted with binary classification to obtain the changing category and the nonchanging category; finally, the proposed method is used to carry out change detection experiments on the two selected areas, where a variety of methods are compared. Experimental results show that the proposed method can effectively and quickly extract the changing area between the two temporal remote sensing images; thus, it is an effective method of change detection for remote sensing images.

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Improvement of grayscale image 2D maximum entropy threshold segmentation method

Cited by 19 publications

References 10 publications

Gaussian mixture models and information entropy for image segmentation using particle swarm optimisation

Gaussian mixture models and information entropy for image segmentation using particle swarm optimisation

3D Flow Entropy Contour Fitting Segmentation Algorithm Based on Multi-Scale Transform Contour Constraint

Automatic Change Detection Method of Multitemporal Remote Sensing Images Based on 2D-Otsu Algorithm Improved by Firefly Algorithm

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