{"title":"一种视频纹理配准的多分辨率方法","authors":"R. Bonneau, M. Novak, J. Perretta, S. Ertan","doi":"10.1109/AIPR.2001.991214","DOIUrl":null,"url":null,"abstract":"Electro-optical imagery can have uniform characteristics that prevent it from being registered by conventional edge-based methods. Such uniform characteristics, if they have periodicity, can be exploited using multi-resolution texture extraction techniques. We first use a multi-resolution Markov model to represent electro-optical textures and apply an autoregressive statistical approach to find correspondence between two images. We then demonstrate how this approach reduces the computational complexity of registering of two successive frames of video.","PeriodicalId":277181,"journal":{"name":"Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A multiresolution approach for video texture registration\",\"authors\":\"R. Bonneau, M. Novak, J. Perretta, S. Ertan\",\"doi\":\"10.1109/AIPR.2001.991214\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Electro-optical imagery can have uniform characteristics that prevent it from being registered by conventional edge-based methods. Such uniform characteristics, if they have periodicity, can be exploited using multi-resolution texture extraction techniques. We first use a multi-resolution Markov model to represent electro-optical textures and apply an autoregressive statistical approach to find correspondence between two images. We then demonstrate how this approach reduces the computational complexity of registering of two successive frames of video.\",\"PeriodicalId\":277181,\"journal\":{\"name\":\"Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIPR.2001.991214\",\"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 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIPR.2001.991214","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A multiresolution approach for video texture registration
Electro-optical imagery can have uniform characteristics that prevent it from being registered by conventional edge-based methods. Such uniform characteristics, if they have periodicity, can be exploited using multi-resolution texture extraction techniques. We first use a multi-resolution Markov model to represent electro-optical textures and apply an autoregressive statistical approach to find correspondence between two images. We then demonstrate how this approach reduces the computational complexity of registering of two successive frames of video.