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Prior parameter estimation for Ising-MRF-based sonar image segmentation by local center-encoding
Song SM(宋三明); Si BL(斯白露); Feng XS(封锡盛); Herrmann, J. Michael
Department水下机器人研究室
Conference NameOCEANS 2015 MTS/IEEE Genova
Conference DateMay 18-21, 2015
Conference PlaceGenova, Italy
Source PublicationOCEANS 2015 MTS/IEEE Genova
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2015
Pages1-5
Indexed ByEI ; CPCI(ISTP)
EI Accession number20160701916016
WOS IDWOS:000380485500112
Contribution Rank1
ISBN978-1-4799-8736-8
AbstractA prior parameter estimation method based on local center-encoding (LCE) is proposed for a Markov random field (MRF) model, i.e. the Ising case, in the task of image segmentation. The LCE algorithm makes efficient use of the local information in the image, avoiding the exclusion of certain blocks as in the least square (LSQR) algorithm. In addition, LCE doesn't require complex matrix computations, therefore reduces the computational cost. As a general algorithm, LCE can be used to estimate the prior parameters in anisotropic label fields. Experimental results on label fields and image segmentations demonstrate the efficiency and generality of the LCE algorithm.
Language英语
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Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/17504
Collection水下机器人研究室
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.Institute of Perception, Action and Behavior, University of Edinburgh, Edinburgh, United Kingdom
Recommended Citation
GB/T 7714
Song SM,Si BL,Feng XS,et al. Prior parameter estimation for Ising-MRF-based sonar image segmentation by local center-encoding[C]. Piscataway, NJ, USA:IEEE,2015:1-5.
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