{"title":"纹理质量标准比较","authors":"M. Haindl, Nahidbanu Shaikh","doi":"10.1109/ICASSPW59220.2023.10193606","DOIUrl":null,"url":null,"abstract":"Visual scene recognition or modeling predominantly uses visual textures representing an object’s material properties. However, the single material texture varies in scale and illumination angles due to mapping an object’s shape. We present a comparative study of thirteen possible texture quality criteria and show the superior performance of two multispectral measures derived from the Markovian descriptive model.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Texture Quality Criteria Comparison\",\"authors\":\"M. Haindl, Nahidbanu Shaikh\",\"doi\":\"10.1109/ICASSPW59220.2023.10193606\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Visual scene recognition or modeling predominantly uses visual textures representing an object’s material properties. However, the single material texture varies in scale and illumination angles due to mapping an object’s shape. We present a comparative study of thirteen possible texture quality criteria and show the superior performance of two multispectral measures derived from the Markovian descriptive model.\",\"PeriodicalId\":158726,\"journal\":{\"name\":\"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)\",\"volume\":\"73 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSPW59220.2023.10193606\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSPW59220.2023.10193606","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Visual scene recognition or modeling predominantly uses visual textures representing an object’s material properties. However, the single material texture varies in scale and illumination angles due to mapping an object’s shape. We present a comparative study of thirteen possible texture quality criteria and show the superior performance of two multispectral measures derived from the Markovian descriptive model.