{"title":"基于ICA的自适应区域多模态图像融合","authors":"N. Cvejic, J. Lewis, D. Bull, C. N. Canagarajah","doi":"10.1109/ICIF.2006.301600","DOIUrl":null,"url":null,"abstract":"In this paper, we present a novel multimodal image fusion algorithm in ICA domain. It uses segmentation to determine the most important regions in the input images and consequently fuses the ICA coefficients from given regions using the Piella fusion metric to maximise the quality of the fused image. The proposed method exhibits significantly higher performance than the basic ICA algorithm and improvement over other state-of-the-art algorithms","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Adaptive Region-Based Multimodal Image Fusion Using ICA Bases\",\"authors\":\"N. Cvejic, J. Lewis, D. Bull, C. N. Canagarajah\",\"doi\":\"10.1109/ICIF.2006.301600\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a novel multimodal image fusion algorithm in ICA domain. It uses segmentation to determine the most important regions in the input images and consequently fuses the ICA coefficients from given regions using the Piella fusion metric to maximise the quality of the fused image. The proposed method exhibits significantly higher performance than the basic ICA algorithm and improvement over other state-of-the-art algorithms\",\"PeriodicalId\":248061,\"journal\":{\"name\":\"2006 9th International Conference on Information Fusion\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 9th International Conference on Information Fusion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIF.2006.301600\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 9th International Conference on Information Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIF.2006.301600","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive Region-Based Multimodal Image Fusion Using ICA Bases
In this paper, we present a novel multimodal image fusion algorithm in ICA domain. It uses segmentation to determine the most important regions in the input images and consequently fuses the ICA coefficients from given regions using the Piella fusion metric to maximise the quality of the fused image. The proposed method exhibits significantly higher performance than the basic ICA algorithm and improvement over other state-of-the-art algorithms
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