{"title":"地籍地图组装:解谜游戏","authors":"Jean-Marc Viglino, L. Guigues","doi":"10.1109/ICDAR.2001.953979","DOIUrl":null,"url":null,"abstract":"The French cadastral map consists of over 500,000 map sheets that cover the whole territory. The raster digitisation of these paper maps is in progress. In order to exploit them, we have to assemble and geo-reference the set of maps to make them superimposable on other geographic information in a GIS. The problem can be seen as a complex jigsaw puzzle where the pieces are the cadastre sections extracted from the maps. In this paper, we present an automatic solution to this geographic jigsaw puzzle, based on a non-combinatorial optimisation method that maximises the \"sticking\" between every piece and its neighbours. The first step is to extract image features from the documents. Then we compute the sticking relationships between each pair of pieces. The puzzle resolution itself is based on an L1 norm optimisation. A method to detect process faults is discussed. The final goal of the process is to integrate every piece of the puzzle (i.e. the cadastre maps) into a national geographic reference frame and database.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"273 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Cadastre map assembling: a puzzle game resolution\",\"authors\":\"Jean-Marc Viglino, L. Guigues\",\"doi\":\"10.1109/ICDAR.2001.953979\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The French cadastral map consists of over 500,000 map sheets that cover the whole territory. The raster digitisation of these paper maps is in progress. In order to exploit them, we have to assemble and geo-reference the set of maps to make them superimposable on other geographic information in a GIS. The problem can be seen as a complex jigsaw puzzle where the pieces are the cadastre sections extracted from the maps. In this paper, we present an automatic solution to this geographic jigsaw puzzle, based on a non-combinatorial optimisation method that maximises the \\\"sticking\\\" between every piece and its neighbours. The first step is to extract image features from the documents. Then we compute the sticking relationships between each pair of pieces. The puzzle resolution itself is based on an L1 norm optimisation. A method to detect process faults is discussed. The final goal of the process is to integrate every piece of the puzzle (i.e. the cadastre maps) into a national geographic reference frame and database.\",\"PeriodicalId\":277816,\"journal\":{\"name\":\"Proceedings of Sixth International Conference on Document Analysis and Recognition\",\"volume\":\"273 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of Sixth International Conference on Document Analysis and Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDAR.2001.953979\",\"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 Sixth International Conference on Document Analysis and Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2001.953979","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The French cadastral map consists of over 500,000 map sheets that cover the whole territory. The raster digitisation of these paper maps is in progress. In order to exploit them, we have to assemble and geo-reference the set of maps to make them superimposable on other geographic information in a GIS. The problem can be seen as a complex jigsaw puzzle where the pieces are the cadastre sections extracted from the maps. In this paper, we present an automatic solution to this geographic jigsaw puzzle, based on a non-combinatorial optimisation method that maximises the "sticking" between every piece and its neighbours. The first step is to extract image features from the documents. Then we compute the sticking relationships between each pair of pieces. The puzzle resolution itself is based on an L1 norm optimisation. A method to detect process faults is discussed. The final goal of the process is to integrate every piece of the puzzle (i.e. the cadastre maps) into a national geographic reference frame and database.