王亮,田萱.单幅散焦图像的局部特征模糊分割算法[J].计算机科学,2018,45(2):318-321
单幅散焦图像的局部特征模糊分割算法
Local Feature Fuzzy Segmentation Algorithm for Single Defocused Image
投稿时间:2017-10-09  修订日期:2017-11-09
DOI:10.11896/j.issn.1002-137X.2018.02.055
中文关键词:  单幅散焦图像,局部特征,模糊分割,免疫谱聚类算法
英文关键词:Single defocused image,Local feature,Fuzzy segmentation,Immune spectrum clustering algorithm
基金项目:本文受北京林业大学中央高校基本科研业务费专项基金资助
作者单位
王亮 北京林业大学信息学院 北京100083 
田萱 北京林业大学信息学院 北京100083 
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中文摘要:
      当前局部特征模糊分割算法没有对单幅散焦图像进行预处理,导致单幅散焦图像的清晰度较低,从而影响分割效果。原有的模糊分割算法在像素分割的过程中,像素标签量巨大,从而导致分割过程复杂。为此,提出利用免疫谱聚类算法实现对单幅散焦图像的局部特征模糊分割。首先,通过分块的方法对局部模糊图像进行再次模糊;然后,比较模糊前后散焦图像的奇异值变化,并以该变化为依据对散焦图像进行标识 ;最后,提取出单幅散焦图像的奇异值特征,进而实现单幅散焦图像的局部特征模糊分割的目标。利用谱聚类的方法对散焦图像中的像素点样本进行聚类,采用Nystrm逼近方法对像素点相似性矩阵的特征向量进行计算,降低了计算的复杂度;同时利用免疫算法提高聚类结果的准确性,保证了散焦图像的局部特征模糊分割结果。实验结果表明,所提算法能够有效地对单幅散焦图像进行分割,分割的效果较好,计算过程较为简单。
英文摘要:
      At present,the fuzzy segmentation algorithm of local features does not preprocess a single defocused image,resulting in low definition of the single defocused image and affecting the segmentation effect.The original fuzzy segmentation algorithm requires a large number of pixel labels in the process of pixel segmentation,and its segmentation process is complicated.Therefore,this paper proposed a method of using immune spectral clustering algorithm to excute fuzzy segmentation of the local features for a single defocused image .Firstly,the local fuzzy image is blurred again by using the method of block.Then,the variation of the singular value for the defocused image is compared,and the defocused image is identified based on this variation.Finally,the singular value features of a single defocused image are extracted,and the local features of a single defocused image are blurred.The spectral clustering method is used to cluster the pixels in the defocused image and the Nystrm approximation method is used to calculate the eigenvectors of the pixel similarity matrix,which reduces the computational complexity.The immune algorithm improves the accuracy of the clustering results and ensures the fuzzy segmentation results of the local features for defocused images.The experimental results show that the proposed algorithm can effectively segment the defocused image,the segmentation result is better and the calculation process is simpler.
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