利用纹理特征进行智能化分割是提高皮革生产效率和产品质量的有效手段。为此提出了一种基于比例模板和区域边界校正的皮革图像分割方法，该方法首先根据皮革各部位所占面积比例的经验知识构造一个比例模板，并以此模板将图像粗分割成若干个区域，然后利用自相关函数分布统计出每个区域边界点所在窗口内的平均纹理周期，并与区域内窗口平均纹理周期进行相似比较，依据预设的平均纹理周期相似度阈值对区域边界点进行判别校正，使得每一区域内的平均纹理周期误差在允许的范围内保持一致。仿真试验表明，该方法简单易行，能有效地分割皮革图像。

Using leather's texture character to performance intelligent segmentation is an efficient way to improve leather product efficiency and production quality. A new method for leather image segmentation is proposed based on proportional template and region boundary adjustment. First, according to the previous experience of leather segmentation, a proportional template used to performance leather image's coarse segmentation is built, then, the leather image is divided into several coarse regions. Second, pixel on the boundary is selected as the center to design a window, and the mean texture period inside the window is computerized using auto-correlation function distribution. By the same way, the mean texture period of the window belong to the region center is also calculated. Two mean texture period are compared to distinguish whether the location of pixel on the boundary need to adjust. The above processes are repeated until the region's texture consistency is limited to scheduled error scope. Simulations show that the proposed method is simple and efficient to segment leather image.