{"title":"多分辨率立体-贝叶斯方法","authors":"C. Chang, S. Chatterjee","doi":"10.1109/ICPR.1990.118239","DOIUrl":null,"url":null,"abstract":"A Bayesian approach is proposed for stereo matching to derive the maximum a posteriori estimation of depth. How a pyramid data structure can be combined with simulated annealing to speed up convergence in stereo matching is described. Using the invariant property of image intensity and modeling the disparity as a Markov random field (MRF), the pyramid structure is followed from high (coarse) level to low (fine) level to derive the maximum a posteriori estimates. Simulation results on both random dot diagrams and synthesized images show the promise of this multiresolution stereo approach.<<ETX>>","PeriodicalId":135937,"journal":{"name":"[1990] Proceedings. 10th International Conference on Pattern Recognition","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1990-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Multiresolution stereo-a Bayesian approach\",\"authors\":\"C. Chang, S. Chatterjee\",\"doi\":\"10.1109/ICPR.1990.118239\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A Bayesian approach is proposed for stereo matching to derive the maximum a posteriori estimation of depth. How a pyramid data structure can be combined with simulated annealing to speed up convergence in stereo matching is described. Using the invariant property of image intensity and modeling the disparity as a Markov random field (MRF), the pyramid structure is followed from high (coarse) level to low (fine) level to derive the maximum a posteriori estimates. Simulation results on both random dot diagrams and synthesized images show the promise of this multiresolution stereo approach.<<ETX>>\",\"PeriodicalId\":135937,\"journal\":{\"name\":\"[1990] Proceedings. 10th International Conference on Pattern Recognition\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1990-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1990] Proceedings. 10th International Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.1990.118239\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1990] Proceedings. 10th International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.1990.118239","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Bayesian approach is proposed for stereo matching to derive the maximum a posteriori estimation of depth. How a pyramid data structure can be combined with simulated annealing to speed up convergence in stereo matching is described. Using the invariant property of image intensity and modeling the disparity as a Markov random field (MRF), the pyramid structure is followed from high (coarse) level to low (fine) level to derive the maximum a posteriori estimates. Simulation results on both random dot diagrams and synthesized images show the promise of this multiresolution stereo approach.<>
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