{"title":"AutoKey:人工辅助密钥提取","authors":"T. Mitsunaga, Taku Yokoyama, T. Totsuka","doi":"10.1145/218380.218450","DOIUrl":null,"url":null,"abstract":"Key extraction is an inverse problem of finding the foreground, the background, and the alpha from an image and some hints. Although the chromakey solves this for a limited case (single background color), this is often too restrictive in practical situations. When the extraction from arbitrary background is necessary, this is currently done by a time consuming manual task. In order to reduce the operator load, attempts have been made to assist operators using either color space or image space information. However, existing approaches have their limitations. Especially, they leave too much work to operators. In this paper, we present a key extraction algorithm which for the first time, addresses the problem quantitatively. We first derive a partial differential equation that relates the gradient of an image to the alpha values. We then describe an efficient algorithm that provides the alpha values as the solution of the equation. Along with our accurate motion estimation technique, it produces correct alpha values almost everywhere, leaving little work to operators. We also show that a careful design of the algorithm and the data representation greatly improves human interaction. At every step of the algorithm, human interaction is possible and it is intuitive. CR Categories: I.3.3 [Computer Graphics]: Picture / Image Generation; I.4.6 [Image Processing]: Segmentation Edge and feature detection; I.4.7 [Image Processing]: Feature Measurement; I.5.2 [Pattern Recognition]: Design Methodology Feature evaluation and selection. Additional","PeriodicalId":447770,"journal":{"name":"Proceedings of the 22nd annual conference on Computer graphics and interactive techniques","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"56","resultStr":"{\"title\":\"AutoKey: human assisted key extraction\",\"authors\":\"T. Mitsunaga, Taku Yokoyama, T. Totsuka\",\"doi\":\"10.1145/218380.218450\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Key extraction is an inverse problem of finding the foreground, the background, and the alpha from an image and some hints. Although the chromakey solves this for a limited case (single background color), this is often too restrictive in practical situations. When the extraction from arbitrary background is necessary, this is currently done by a time consuming manual task. In order to reduce the operator load, attempts have been made to assist operators using either color space or image space information. However, existing approaches have their limitations. Especially, they leave too much work to operators. In this paper, we present a key extraction algorithm which for the first time, addresses the problem quantitatively. We first derive a partial differential equation that relates the gradient of an image to the alpha values. We then describe an efficient algorithm that provides the alpha values as the solution of the equation. Along with our accurate motion estimation technique, it produces correct alpha values almost everywhere, leaving little work to operators. We also show that a careful design of the algorithm and the data representation greatly improves human interaction. At every step of the algorithm, human interaction is possible and it is intuitive. CR Categories: I.3.3 [Computer Graphics]: Picture / Image Generation; I.4.6 [Image Processing]: Segmentation Edge and feature detection; I.4.7 [Image Processing]: Feature Measurement; I.5.2 [Pattern Recognition]: Design Methodology Feature evaluation and selection. Additional\",\"PeriodicalId\":447770,\"journal\":{\"name\":\"Proceedings of the 22nd annual conference on Computer graphics and interactive techniques\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"56\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 22nd annual conference on Computer graphics and interactive techniques\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/218380.218450\",\"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 22nd annual conference on Computer graphics and interactive techniques","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/218380.218450","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Key extraction is an inverse problem of finding the foreground, the background, and the alpha from an image and some hints. Although the chromakey solves this for a limited case (single background color), this is often too restrictive in practical situations. When the extraction from arbitrary background is necessary, this is currently done by a time consuming manual task. In order to reduce the operator load, attempts have been made to assist operators using either color space or image space information. However, existing approaches have their limitations. Especially, they leave too much work to operators. In this paper, we present a key extraction algorithm which for the first time, addresses the problem quantitatively. We first derive a partial differential equation that relates the gradient of an image to the alpha values. We then describe an efficient algorithm that provides the alpha values as the solution of the equation. Along with our accurate motion estimation technique, it produces correct alpha values almost everywhere, leaving little work to operators. We also show that a careful design of the algorithm and the data representation greatly improves human interaction. At every step of the algorithm, human interaction is possible and it is intuitive. CR Categories: I.3.3 [Computer Graphics]: Picture / Image Generation; I.4.6 [Image Processing]: Segmentation Edge and feature detection; I.4.7 [Image Processing]: Feature Measurement; I.5.2 [Pattern Recognition]: Design Methodology Feature evaluation and selection. Additional