This paper presents an algorithm for the segmentation of cow image to deal with the cow identification in the dairy factory. The cow image is firstly transformed from RGB space into Luv space, then the image is partitioned into sub-images which size is 4×4, and the color clustering has been done on each sub-image and the features of color and texture have heen extracted. Colors in each suh-images have been reduced. Two main colors will be used to descrihe the main color feature of each sub-images. The texture feature which describes the distrihution of the colors in each sub-images are counted by scanning the sub-images horizontally and vertically. The region growing algorithm has been done hased on sub-images according to the cow features of color and texture. 50 pictures had been processed by the algorithm which presented by the paper, the accuracy rate of segmentation is 80％ or higher. The experimental results show that the proposed algorithm can separate the cow from the background efficiently.%为解决在实际生产环境中使用计算杌自动识别奶牛的问题,提出了针对奶牛图像的分割算法,以提高识别率.该算法首先将奶牛图像从RGB空间转化为Luv空间,然后将图像作4×4大小的子块划分,对各子块进行块内颜色聚类并提取颜色和纹理特征,通过颜色内聚将子块中的颜色范围缩小到2～4种,并将其中所占教量最多的两种颜色作为子块的颜色特征:纹理特征则是基于对子块分别横向和纵向扫描并统计出来的,反映子块的颜色在横向和纵向的分布特征.在此基础上按奶牛的颜色和纹理特征进行基于子块的区域生长,最终达到清晰分割的目的.在对50个实地采集样本中按本文所述的方法进行分割,分割准确率达到80%以上.实验证明,本文提出算法能有效地将奶牛从背景中分离出来.
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