{"title":"基于补丁的纹理合成快速鲁棒参数估计方法","authors":"Jakrapong Narkdej, P. Kanongchaiyos","doi":"10.1145/1477862.1477909","DOIUrl":null,"url":null,"abstract":"Patch-based texture synthesis method uses MRF texture model to synthesize a bigger texture from a smaller patch sample containing two user-defined parameters, patch size and boundary zone. To obtain optimal values for the parameters, the texture has to be analyzed, which costs too expensive for real-time large texture synthesis. This paper introduces a more efficient method for finding the optimal value of the two parameters. Firstly, we use graph-based image segmentation to extract feature segments from the input sample. We then choose a set of major segments preserving the main features to appear in the final result. Finally, we calculate the two parameters based on size and repetition of the segments. The experimental results how that our technique can reduce computational time for determining the parameters compared to previous method and can work with several type of textures.","PeriodicalId":182702,"journal":{"name":"Proceedings of The 7th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Fast and robust parameter estimation method for patch-based texture synthesis\",\"authors\":\"Jakrapong Narkdej, P. Kanongchaiyos\",\"doi\":\"10.1145/1477862.1477909\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Patch-based texture synthesis method uses MRF texture model to synthesize a bigger texture from a smaller patch sample containing two user-defined parameters, patch size and boundary zone. To obtain optimal values for the parameters, the texture has to be analyzed, which costs too expensive for real-time large texture synthesis. This paper introduces a more efficient method for finding the optimal value of the two parameters. Firstly, we use graph-based image segmentation to extract feature segments from the input sample. We then choose a set of major segments preserving the main features to appear in the final result. Finally, we calculate the two parameters based on size and repetition of the segments. The experimental results how that our technique can reduce computational time for determining the parameters compared to previous method and can work with several type of textures.\",\"PeriodicalId\":182702,\"journal\":{\"name\":\"Proceedings of The 7th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of The 7th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1477862.1477909\",\"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 7th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1477862.1477909","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fast and robust parameter estimation method for patch-based texture synthesis
Patch-based texture synthesis method uses MRF texture model to synthesize a bigger texture from a smaller patch sample containing two user-defined parameters, patch size and boundary zone. To obtain optimal values for the parameters, the texture has to be analyzed, which costs too expensive for real-time large texture synthesis. This paper introduces a more efficient method for finding the optimal value of the two parameters. Firstly, we use graph-based image segmentation to extract feature segments from the input sample. We then choose a set of major segments preserving the main features to appear in the final result. Finally, we calculate the two parameters based on size and repetition of the segments. The experimental results how that our technique can reduce computational time for determining the parameters compared to previous method and can work with several type of textures.