{"title":"直方图分析的视错觉效果","authors":"Gwanggil Jeon, Eunju Kim, Nam Kook Kim","doi":"10.1109/ASEA.2014.13","DOIUrl":null,"url":null,"abstract":"In this paper, we presents a fuzzy concept based histogram equalization approach. We use Kaufmann's metric of fuzziness concept for histogram equalization. The principal idea of this article is to utilize and evaluate degree of fuzziness. The presented histogram equalization approach is processed to Y component of YUV color space, which is transformed information from RGB image. The improved Y channel information is merged with color information signals I and Q, and re-transformed to generated RGB image. The subjective performance comparison is provided in simulation results section.","PeriodicalId":320279,"journal":{"name":"2014 7th International Conference on Advanced Software Engineering and Its Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optical Illusion Effect by Histogram Analysis\",\"authors\":\"Gwanggil Jeon, Eunju Kim, Nam Kook Kim\",\"doi\":\"10.1109/ASEA.2014.13\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we presents a fuzzy concept based histogram equalization approach. We use Kaufmann's metric of fuzziness concept for histogram equalization. The principal idea of this article is to utilize and evaluate degree of fuzziness. The presented histogram equalization approach is processed to Y component of YUV color space, which is transformed information from RGB image. The improved Y channel information is merged with color information signals I and Q, and re-transformed to generated RGB image. The subjective performance comparison is provided in simulation results section.\",\"PeriodicalId\":320279,\"journal\":{\"name\":\"2014 7th International Conference on Advanced Software Engineering and Its Applications\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 7th International Conference on Advanced Software Engineering and Its Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASEA.2014.13\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 7th International Conference on Advanced Software Engineering and Its Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASEA.2014.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we presents a fuzzy concept based histogram equalization approach. We use Kaufmann's metric of fuzziness concept for histogram equalization. The principal idea of this article is to utilize and evaluate degree of fuzziness. The presented histogram equalization approach is processed to Y component of YUV color space, which is transformed information from RGB image. The improved Y channel information is merged with color information signals I and Q, and re-transformed to generated RGB image. The subjective performance comparison is provided in simulation results section.