The existing level set segmentation methods have drawbacks such as poor convergence,poor noise resistance,and long iteration times.In this paper,a fractional order distance regularized level set segmentation method wi...The existing level set segmentation methods have drawbacks such as poor convergence,poor noise resistance,and long iteration times.In this paper,a fractional order distance regularized level set segmentation method with bias correction is proposed.This method firstly introduces fractional order distance regularized term to punish the deviation between the level set function(LSF)and the signed distance function.Secondly a series of covering template is constructed to calculate fractional derivative and its conjugate of image pixel.Thirdly introducing the offset correction term and fully using the local clustering property of image intensity,the local clustering criterion of image intensity is defined and integrated with the neighborhood center to obtain the global criterion of image segmentation.Finally,the fractional distance regularization,offset correction,and external energy constraints are combined,and the energy optimization segmentation method for noisy image is established by level set.Experimental results show that the proposed method can accurately segment the image,and effectively improve the efficiency and robustness of exiting state of the art level set related algorithms.展开更多
Image fusion is widely used in computer vision and image analysis.Considering that the traditional image fusion algorithm has a certain limitation in multi-channel image fusion,a memristor-based multi-channel pulse co...Image fusion is widely used in computer vision and image analysis.Considering that the traditional image fusion algorithm has a certain limitation in multi-channel image fusion,a memristor-based multi-channel pulse coupled neural network(M-MPCNN)for image fusion is proposed.Based on a dual-channel pulse coupled neural network(D-PCNN),a novel multi-channel pulse coupled neural network(M-PCNN)is firstly constructed in this paper.Then the exponential growth dynamic threshold model is used to improve the pulse generation of pulse coupled neural network,which can not only avoid multiple ignitions effectively,but can also improve operational efficiency and reduce complexity.At the same time,synchronous capture can also enhance image edge,which is more conducive to image fusion.Finally,the threshold and synaptic characteristics of pulse coupled neural networks(PCNNs)can be well realized by using a memristor-based pulse generator.Experimental results show that the proposed algorithm can fuse multi-source images more effectively than existing state-of-the-art fusion algorithms.展开更多
To improve the anti-noise ability of fuzzy local information C-means clustering, a robust entropy-like distance driven fuzzy clustering with local information is proposed. This paper firstly uses Jensen-Shannon diverg...To improve the anti-noise ability of fuzzy local information C-means clustering, a robust entropy-like distance driven fuzzy clustering with local information is proposed. This paper firstly uses Jensen-Shannon divergence to induce a symmetric entropy-like divergence. Then the root of entropy-like divergence is proved to be a distance measure, and it is applied to existing fuzzy C-means(FCM) clustering to obtain a new entropy-like divergence driven fuzzy clustering, meanwhile its convergence is strictly proved by Zangwill theorem. In the end, a robust fuzzy clustering by combing local information with entropy-like distance is constructed to segment image with noise. Experimental results show that the proposed algorithm has better segmentation accuracy and robustness against noise than existing state-of-the-art fuzzy clustering-related segmentation algorithm in the presence of noise.展开更多
To enhance the segmentation performance and robustness of kernel weighted fuzzy local information C-means(KWFLICM) clustering for image segmentation in the presence of high noise, an improved KWFLICM algorithm aggrega...To enhance the segmentation performance and robustness of kernel weighted fuzzy local information C-means(KWFLICM) clustering for image segmentation in the presence of high noise, an improved KWFLICM algorithm aggregating neighborhood membership information is proposed. This algorithm firstly constructs a linear weighted membership function by combining the membership degrees of current pixel and its neighborhood pixels. Then it is normalized to meet the constraint that the sum of membership degree of pixel belonging to different classes is 1. In the end, normalized membership is used to update the clustering centers of KWFLICM algorithm. Experimental results show that the proposed adaptive KWFLICM(AKWFLICM) algorithm outperforms existing state of the art fuzzy clustering-related segmentation algorithms for image with high noise.展开更多
基金This work was supported by the National Natural Science Foundation of China(62071378).
文摘The existing level set segmentation methods have drawbacks such as poor convergence,poor noise resistance,and long iteration times.In this paper,a fractional order distance regularized level set segmentation method with bias correction is proposed.This method firstly introduces fractional order distance regularized term to punish the deviation between the level set function(LSF)and the signed distance function.Secondly a series of covering template is constructed to calculate fractional derivative and its conjugate of image pixel.Thirdly introducing the offset correction term and fully using the local clustering property of image intensity,the local clustering criterion of image intensity is defined and integrated with the neighborhood center to obtain the global criterion of image segmentation.Finally,the fractional distance regularization,offset correction,and external energy constraints are combined,and the energy optimization segmentation method for noisy image is established by level set.Experimental results show that the proposed method can accurately segment the image,and effectively improve the efficiency and robustness of exiting state of the art level set related algorithms.
基金This work was supported by the National Natural Science Foundation of China(61671377,51709228)the Shaanxi Natural Science Foundation of China(2016JM8034,2017JM6107).
文摘Image fusion is widely used in computer vision and image analysis.Considering that the traditional image fusion algorithm has a certain limitation in multi-channel image fusion,a memristor-based multi-channel pulse coupled neural network(M-MPCNN)for image fusion is proposed.Based on a dual-channel pulse coupled neural network(D-PCNN),a novel multi-channel pulse coupled neural network(M-PCNN)is firstly constructed in this paper.Then the exponential growth dynamic threshold model is used to improve the pulse generation of pulse coupled neural network,which can not only avoid multiple ignitions effectively,but can also improve operational efficiency and reduce complexity.At the same time,synchronous capture can also enhance image edge,which is more conducive to image fusion.Finally,the threshold and synaptic characteristics of pulse coupled neural networks(PCNNs)can be well realized by using a memristor-based pulse generator.Experimental results show that the proposed algorithm can fuse multi-source images more effectively than existing state-of-the-art fusion algorithms.
基金supported by the National Natural Science Foundation of China (61671377, 51709228)the Natural Science Foundation of Shaanxi Province (2016JM8034,2017JM6107)。
文摘To improve the anti-noise ability of fuzzy local information C-means clustering, a robust entropy-like distance driven fuzzy clustering with local information is proposed. This paper firstly uses Jensen-Shannon divergence to induce a symmetric entropy-like divergence. Then the root of entropy-like divergence is proved to be a distance measure, and it is applied to existing fuzzy C-means(FCM) clustering to obtain a new entropy-like divergence driven fuzzy clustering, meanwhile its convergence is strictly proved by Zangwill theorem. In the end, a robust fuzzy clustering by combing local information with entropy-like distance is constructed to segment image with noise. Experimental results show that the proposed algorithm has better segmentation accuracy and robustness against noise than existing state-of-the-art fuzzy clustering-related segmentation algorithm in the presence of noise.
文摘To enhance the segmentation performance and robustness of kernel weighted fuzzy local information C-means(KWFLICM) clustering for image segmentation in the presence of high noise, an improved KWFLICM algorithm aggregating neighborhood membership information is proposed. This algorithm firstly constructs a linear weighted membership function by combining the membership degrees of current pixel and its neighborhood pixels. Then it is normalized to meet the constraint that the sum of membership degree of pixel belonging to different classes is 1. In the end, normalized membership is used to update the clustering centers of KWFLICM algorithm. Experimental results show that the proposed adaptive KWFLICM(AKWFLICM) algorithm outperforms existing state of the art fuzzy clustering-related segmentation algorithms for image with high noise.
