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Pixon-based image segmentation with Markov random fields
Yang, FG; Jiang, TZ
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2003-12-01
Volume12Issue:12Pages:1552-1559
SubtypeArticle
AbstractImage segmentation is an essential processing step for many image analysis applications. In this paper, we propose a novel pixon-based adaptive scale method for image segmentation. The key idea of our approach is that a pixon-based image model is combined with a Markov random field (MRF) model under a Bayesian framework. In our method, we introduce a new pixon scheme that is more suitable for image segmentation than the "fuzzy" pixon scheme. The anisotropic diffusion equation is successfully used to form the pixons in our new pixon scheme. Experimental results demonstrate that our algorithm performs fairly well and computational costs decrease dramatically compared with the pixel-based MRF algorithm.
KeywordImage Segmentation Markov Random Fields Pixon
WOS HeadingsScience & Technology ; Technology
WOS KeywordGIBBS RANDOM-FIELDS ; UNSUPERVISED SEGMENTATION ; ACTIVE CONTOURS ; MODELS ; RECONSTRUCTION ; CLASSIFICATION ; RELAXATION
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000188197500010
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9888
Collection09年以前成果
Affiliation1.Acad Sinica, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
2.Univ Houston, Dept Comp Sci, Houston, TX 77204 USA
Recommended Citation
GB/T 7714
Yang, FG,Jiang, TZ. Pixon-based image segmentation with Markov random fields[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2003,12(12):1552-1559.
APA Yang, FG,&Jiang, TZ.(2003).Pixon-based image segmentation with Markov random fields.IEEE TRANSACTIONS ON IMAGE PROCESSING,12(12),1552-1559.
MLA Yang, FG,et al."Pixon-based image segmentation with Markov random fields".IEEE TRANSACTIONS ON IMAGE PROCESSING 12.12(2003):1552-1559.
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