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《Forest Engineering》 2014-06
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Tree Image Segmentation Based on K-mean Clustering and Gray-gradient Maximum Entropy

Bai Xuebing;Chen Kai;Guo Jingqiu;Zhu He;Zhang Tingliang;College of Mechanical and Electrical Engineering,Northeast Forestry University;  
This paper proposed a k-mean clustering and gray-gradient maximum entropy image segmentation algorithm for trees.The color tree image was processed for base-k-mean clustering in the RGB color space,to achieve early segmentation parameters by selecting the appropriate class. Since gray-gradient space can clearly delineates between the distribution of edge cases each image pixel gray,gradient and image of the target and the background,the gray-gradient maximum entropy algorithm can be used to perform fine segmentation in combination with morphological processing of extracted image edge and the separated target image can eventually be obtained. Compared with the two-dimensional maximum entropy segmentation method,the results showed that gray-gradient maximum entropy algorithm can improve the accuracy of image segmentation of trees.
【Fund】: 黑龙江省自然基金项目(C201208)
【CateGory Index】: S758;TP391.41
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