文件名称:automatic_image_segement
- 所属分类:
- 图形图像处理(光照,映射..)
- 资源属性:
- 上传时间:
- 2019-05-11
- 文件大小:
- 1kb
- 下载次数:
- 0次
- 提 供 者:
- xiaoxi******
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- 无
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本文以k-means算法为背景,引入信息熵相关知识,从而实现全自动分割图像。然而在利用混合高斯模型对图像进行数据分析时,会发生一定的过拟合现象,导致我们得不到预期的聚类数目。本文设计合理的合并准则,令模型简化,有效地消除过拟合现象,使得最后得到的聚类数目与预期符合。,设计合理的准则改进了图像的全自动分割方法,使得分割结果更加优化(In this paper, k-means algorithm is used as the background, and information entropy related knowledge is introduced to realize full-automatic image segmentation. However, when the Gaussian mixture model is used to analyze the image data, there will be some over-fitting phenomenon, resulting in that we cannot get the expected number of clusters. In this paper, a reasonable merging criterion is designed to simplify the model and effectively eliminate the over-fitting phenomenon, so that the final clustering number is in line with the expectation. A reasonable criterion is designed to improve the automatic image segmentation method and make the segmentation result more optimized.)
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下载文件列表
文件名 | 大小 | 更新时间 |
---|---|---|
gray_segement.m | 1389 | 2019-04-24 |
h_segement.m | 1135 | 2019-04-24 |