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基于Mean Shift聚类的最大熵图像分割方法 被引量:12

A Maximum Entropy Segmentation Method Based on Mean Shift Clustering
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摘要 为了有效的分割图像,在考虑了图像的噪声消除和边缘保持等因素的基础上,为解决上述问题,提出一种Mean Shift的图像平滑方法和最大熵的图像分割方法相结合的图像分割方法,Mean Shift对图像进行平滑能有效去除图像中的噪声,同时能很好的保持图像的边缘,克服了以往平滑方法的弱点,再通过基于最大熵阈值对平滑后图像进行图像分割,经过实验证明,与小波平滑等目前其他的平滑方法和最大熵分割结合相比,方法有效的改善了分割效果。 In order to segment images effectively, based on consider image noise elimination and edge maintaining and so on, a new method combining the Mean Shift image smoothing method with maximum entropy segmentation method is proposed. Noise can be removed by smoothing image with Mean Shift method, simultaneously, edge of the image can be remained, thus overcoming the weakness of smooth method. This new method can effectively improve the segmentation effect.
出处 《计算机仿真》 CSCD 北大核心 2009年第9期187-189,222,共4页 Computer Simulation
关键词 图像平滑 最大熵 图像分割 Image smoothing Maximum entropy Segmentation
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参考文献4

  • 1Dorin Comaniciu, Peter Meer. Mean Shift:A Robust Approach Toward Feature Space Analysis [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2002,24 (5) : 603 - 619.
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