融合形态学连通域和CV模型的民族服饰图案纹样元素分割方法

对民族服饰图案进行自动分割以提取图案纹样元素，是民族服饰图案素材库构建急需解决的难题。通过融合形态学连通域标记和CV模型（MCC-CV），提出了一种民族服饰图案自动分割方法，首先对民族服饰图案进行预处理，然后采用形态学连通域标记算法获得待分割目标的位置和大致轮廓信息，对CV模型进行初始化，最后通过CV模型对不同分割目标进行边缘追踪，以实现民族服饰图案纹样元素的自动分割。实验表明，融合形态学连通域和CV模型的民族服饰图案纹样元素自动分割方法在边界召回率（BR）为0.5时，分割准确率为60%，与其他自动分割算法相比，该算法更为有效，满足了民族服饰图案素材库建设对图案纹样元素分割的基本要求。

The national costume pattern elements segmentation by incorporating morphology connected component and CV model

A automatic segmentation of national costume patterns to extract the pattern elements is an urgent problem to be solved in the construction of the national costume pattern material library. In this paper, we propose an automatic segmentation method by incorporating morphology connected component labeling and CV model (MCC-CV). Firstly, we carry out the image preprocessing on national dress patterns. Then，the location of the target pattern is obtained by using the morphology connected component labeling ,which serves as the initial contour of the CV model. Finally, the automatic segmentation of national costume pattern elements is realized by detecting the edge of the pattern gene using the CV model. Experimental results show that the accuracy of MCC-CV model is 60% under the premise boundary recall 0.5, which satisfies the basic requirements for the national costume pattern material library construction.