{"title":"Shape Recognition Using a New Spatial Representation and a D.P. Matching Algorithm","authors":"Shiyuan Gu, S. Kundu","doi":"10.1109/ICAPR.2009.107","DOIUrl":null,"url":null,"abstract":"We propose a new method for the recognition and retrieval of shapes whose contours are simple closed curves. First, we give a new shape representation by a sequence of 2D-vectors of angles, which is independent of rotation and scaling. Each 2D-vector captures the local shape information around a point in the contour. Next, we apply a dynamic programming method to match the points of two contours and identify the outlier (unmatched) points in each contour with respect to the other. Then, we define the similarity of a pair of contours by the number of unmatched points and the matching cost for the matched points. Finally, the recognition and retrieval are done using the nearest-neighbor method. Our experiments on MPEG-7 database show that the performance of the new algorithm is very similar to that of the best-known algorithms in the literature in spite of its significantly less computational complexity and simplicity.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Seventh International Conference on Advances in Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAPR.2009.107","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
We propose a new method for the recognition and retrieval of shapes whose contours are simple closed curves. First, we give a new shape representation by a sequence of 2D-vectors of angles, which is independent of rotation and scaling. Each 2D-vector captures the local shape information around a point in the contour. Next, we apply a dynamic programming method to match the points of two contours and identify the outlier (unmatched) points in each contour with respect to the other. Then, we define the similarity of a pair of contours by the number of unmatched points and the matching cost for the matched points. Finally, the recognition and retrieval are done using the nearest-neighbor method. Our experiments on MPEG-7 database show that the performance of the new algorithm is very similar to that of the best-known algorithms in the literature in spite of its significantly less computational complexity and simplicity.