Image segmentation based on fuzzy clustering with cellular automata and features weighting

Li, Chengfan; Liu, Lan; Sun, Xiang; Zhao, Junjuan; Yin, Jing

doi:10.1186/s13640-019-0436-5

Cited by 32 publications

(17 citation statements)

References 21 publications

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“…Each cell in the CA continuously iterates and updates in terms of the established rules [29], [30]. A complete CA model includes cell, cell space, neighbor and evolution rule, and can be represented by a quadruple, hence we have:…”

Section: B Ca Basic Theorymentioning

confidence: 99%

Volcanic Ash Cloud Diffusion From Remote Sensing Image Using LSTM-CA Method

Liu¹,

Sun²

2020

IEEE Access

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Monitoring of volcanic ash cloud is conducive to the disaster prevention and mitigation and public safety. To tackle of large amount and various types of data and continuous changes of volcanic ash cloud monitoring, in this paper, a new long short term memory (LSTM) and cellular automaton (CA) (i.e., LSTM-CA) collaborative computing method for volcanic ash cloud diffusion is proposed via neural networks. Based on diffusion characteristics of volcanic ash cloud, a CA model of volcanic ash cloud in the three-dimensional spaces was first constructed. And then the constantly changing sequential characteristics of volcanic ash cloud was learned by LSTM neural network and further treated as the evolution rule of the CA diffusion model of volcanic ash cloud in three-dimensional space. Next, simulation experiments and analysis were conducted in terms of wind direction, wind speed, step size and the number of cell. Finally, the proposed LSTM-CA collaborative computing method was tested and verified in the actual Etna ash cloud diffusion case. The experimental results show that: (1) in the two-dimensional space, the proposed LSTM-CA method can obtain a good initial simulation effect of volcanic ash cloud diffusion, and the total accuracy of volcanic ash cloud identification reached 96.1%; (2) in the three-dimensional space, the proposed LSTM-CA method can exact simulate the horizontal and vertical diffusion trends of volcanic ash cloud; (3) the proposed LSTM-CA method can significantly reduce the modeling complexity of volcanic ash cloud and improve the calculation efficiency of spatiotemporal data. It seems to provide a new idea to identify and simulate the volcanic ash cloud in complex environments. INDEX TERMS Neural networks, collaborative computing, volcanic ash cloud diffusion, remote sensing data, simulation.

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Section: B Ca Basic Theorymentioning

confidence: 99%

Volcanic Ash Cloud Diffusion From Remote Sensing Image Using LSTM-CA Method

Liu¹,

Sun²

2020

IEEE Access

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“…CA have successfully been used in image processing, such as edge detection [41]- [44], noise filtering [40], [45]- [47]. saliency detection [48]- [50], image segmentation [51]- [53], and 3D image reconstruction [54]. For example, S. Wongthanavasu et al [41] proposed an edge detection method based on a cellular automata model.…”

Section: B Learning-based Dehazingmentioning

confidence: 99%

“…The CCA integrates multiple saliency maps generated by SCA at different scales in a Bayesian framework to increase the performance of the model. C. Li et al [53] proposed image segmentation method based on fuzzy clustering with cellular automata (CA) and features weighting. The method combined image color spatial feature weighting and the CA's self-iteration to speeds up the convergence of image segmentation.…”

Section: B Learning-based Dehazingmentioning

confidence: 99%

Single Image Haze Removal Using Deep Cellular Automata Learning

Tangsakul

Wongthanavasu

2020

IEEE Access

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Deep learning is one of the most popular approaches to machine learning, which has been widely used for classification. In this paper, we propose a novel learning method based on a combination of an idea of the deep learning approach and the cellular automata model, called DeepCA for single image haze removal. DeepCA's learning is divided into two main parts. The first part is a cellular automata-based deep feature extraction: multi-layer cellular automata with the rules are used to extract the data feature matrices of the image, in which the matrices can be divided into several layers. Then, the score matrices were generated as the model in which was trained by the cellular automata rules. The second part is a decision stage: we used the score matrices to the mapping between the proper data. For demonstration, we take the single image haze removal task as an example to confirm the capability of the proposed method. In this regard, the dichromatic model is chosen as the major model to remove the haze of the image. The multilayer cellular automata with the rules work as a mechanical extractor of the light source feature of the hazy image. The decision stage of DeepCA performs as the recognizer for properly predicting the global light source for dehazing. This aims to improve the light source and the transmission map that they are important compositions for haze-free image restoration. For performance evaluation, we perform quantitative and qualitative measures. For the qualitative performance of the haze removal, DeepCA did not even cause the halo artifact effect that occurred in other haze removal algorithms. The empirical results in quantitative measures show that DeepCA improved intensity, color saturation quality, and halo artifact when compared with the state-of-the-art methods.

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“…Initially, all agents are randomized and considered as a candidate solution. The force and gravitational constant are computed using equation (1) and (2).With every iteration the best solution is updated to date. Agents in the vicinity of optimal solutions try to grab the other agents moving around them.…”

Section: E Particle Swarm Optimization With Gravitational Search Algmentioning

confidence: 99%

“…All the existing methods are having inaccurate in segmenting the white blood cells.Cellular automata model have been widely used in the past two years by researchers in the field of image segmentation. In the Cellular Automata model, each pixel can be treated as a cell and the image as a cellular space [1]. The CA model contains cell space, cell status, neighborhood and a set of rules.…”

Section: Introductionmentioning

confidence: 99%

Optimal Cellular Automata Technique for Image Segmentation

A. A.*,

Sathiaseelan

2020

IJITEE

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Leukemia death secured 10 thplace among the most dangerous death in the world. The main reason is due to the delay in diagnosis which in turn delayed the treatment process. Hence it becomes an exigent requirement to diagnose leukemia in its early stage. Segmentation of WBC is the initial phase of leukemia detection using image processing.This paper aims to extract WBC from the image background. There exists various techniques for WBC segmentation in the literature. Yet, they provides inaccurate results.Cellular Automata can be effectively implemented in image processing. In this paper, we have proposed an Optimal Cellular Automata approach for image segmentation.In this approach, the optimal value for alive cells is obtained through particle swarm Optimization with Gravitational Search Algorithm (PSOGSA). The optimal value have fed in to the cellular automata model and get the segmented image. The results are validated based on the parameters likeRand Index (RI), Global Consistency Error (GCE), and Variation of Information (VOI). The Experimental results of proposed technique shows better results when compared to the previously proposed techniques namely, Hybrid K-Means with Cluster Center Estimation, Region Splitting and Clustering Technique and Cellular Automata. The proposed technique outperformed all other techniques.

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Image segmentation based on fuzzy clustering with cellular automata and features weighting

Cited by 32 publications

References 21 publications

Volcanic Ash Cloud Diffusion From Remote Sensing Image Using LSTM-CA Method

Volcanic Ash Cloud Diffusion From Remote Sensing Image Using LSTM-CA Method

Single Image Haze Removal Using Deep Cellular Automata Learning

Optimal Cellular Automata Technique for Image Segmentation

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