{"title":"实用地图的内存高效骨架化","authors":"A. Vossepoel, K. Schutte, Carl F. P. Delanghe","doi":"10.1109/ICDAR.1997.620620","DOIUrl":null,"url":null,"abstract":"An algorithm is presented that allows one to perform skeletonization of large maps with much lower memory requirements than with the straightforward approach. The maps are divided into overlapping tiles, which are skeletonized separately, using a Euclidean distance transform. The amount of overlap is controlled by the maximum expected width of any map component and the maximum size of what is considered as a small component. Next, the skeleton parts are connected again at the middle of the overlap zones. Some examples are given for efficient memory utilization in tiling an A0 size map into a predefined number of tiles or into tiles of a predefined (square) size. The algorithm is also suited for a parallel implementation of skeletonization.","PeriodicalId":435320,"journal":{"name":"Proceedings of the Fourth International Conference on Document Analysis and Recognition","volume":" 29","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Memory efficient skeletonization of utility maps\",\"authors\":\"A. Vossepoel, K. Schutte, Carl F. P. Delanghe\",\"doi\":\"10.1109/ICDAR.1997.620620\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An algorithm is presented that allows one to perform skeletonization of large maps with much lower memory requirements than with the straightforward approach. The maps are divided into overlapping tiles, which are skeletonized separately, using a Euclidean distance transform. The amount of overlap is controlled by the maximum expected width of any map component and the maximum size of what is considered as a small component. Next, the skeleton parts are connected again at the middle of the overlap zones. Some examples are given for efficient memory utilization in tiling an A0 size map into a predefined number of tiles or into tiles of a predefined (square) size. The algorithm is also suited for a parallel implementation of skeletonization.\",\"PeriodicalId\":435320,\"journal\":{\"name\":\"Proceedings of the Fourth International Conference on Document Analysis and Recognition\",\"volume\":\" 29\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-08-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Fourth International Conference on Document Analysis and Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDAR.1997.620620\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Fourth International Conference on Document Analysis and Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.1997.620620","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An algorithm is presented that allows one to perform skeletonization of large maps with much lower memory requirements than with the straightforward approach. The maps are divided into overlapping tiles, which are skeletonized separately, using a Euclidean distance transform. The amount of overlap is controlled by the maximum expected width of any map component and the maximum size of what is considered as a small component. Next, the skeleton parts are connected again at the middle of the overlap zones. Some examples are given for efficient memory utilization in tiling an A0 size map into a predefined number of tiles or into tiles of a predefined (square) size. The algorithm is also suited for a parallel implementation of skeletonization.