二维多边形物体剖分研究

Study on Decomposition of Two-dimensional Polygonal Objects

文中对二维多边形物体有意义剖分进行了研究。心理学家通过研究后发现对物体进行有意义的剖分是人类识别物体的一个重要过程。研究对二维多边形物体的剖分,对于图像识别具有重要意义。在进行图像识别时可首先提取图像中物体的边缘,并用封闭多边形表示。研究如何对该多边形进行有意义的剖分,是正确识别该图像中的物体很关键的一步。文中首先用谱分析结合K-均值的方法,对要剖分的多边形的顶点进行聚类,然后用计算多边形剖分线段适合度的方法,递归地在顶点类内部和顶点类之间选择最佳剖分线段,实验结果表明了该方法的有效性。该算法剖分结果和知名的人工剖分结果的定量分析比较表明,算法剖分结果符合人类思维,取得了较好的剖分结果。

This paper studies how to decompose the two-dimensional polygonal objects into meaningful parts.Psychologists have found that meaningful decomposition of objects is an important process for human beings to recognize objects.Especially,in image recognition,after the edge of the object in the image has been detected,the edge can be expressed as a closed polygon.So how to decompose the polygon is a very important step to recognize the object in image.In this paper,we first separate the vertices of polygon into several clusters by spectral analysis combined with K-means,and then by computing cut line fitness proposed in the paper,the algorithm choose the best cut line recursively on between-cluster and within-cluster.Experimental results show the effectiveness of this method.The quantitative analysis and comparison between the algorithm and the well-known artificial decomposition data set show that the algorithm decomposition results are in line with human thinking and have achieved good decomposition results.