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Article: Maximum a posteriori spatial probability segmen

TitleMaximum a posteriori spatial probability segmen
Authors
KeywordsEntropy
Image segmentation
Spatial information
Thresholding
Issue Date1997
PublisherIEEE.
Citation
I E E Proceedings Vision, Image and Signal Processing, 1997, v. 144 n. 3, p. 161-167 How to Cite?
AbstractAn image segmentation algorithm that performs pixel-by-pixel segmentation on an image with consideration of the spatial information is described. The spatial information is the joint grey level values of the pixel to be segmented and its neighbouring pixels. The conditional probability that a pixel belongs to a particular class under the condition that the spatial information has been observed is defined to be the a posteriori spatial probability. A maximum a posteriori spatial probability (MASP) segmentation algorithm is proposed to segment an image such that each pixel is segmented into a particular class when the a posteriori spatial probability is a maximum. The proposed segmentation algorithm is implemented in an iterative form. During the iteration, a series of intermediate segmented images are produced among which the one that possesses the maximum amount of information in its spatial structure is chosen as the optimum segmented image. Results from segmenting synthetic and practical images demonstrate that the MASP algorithm is capable of achieving better results when compared with other global thresholding methods.
Persistent Identifierhttp://hdl.handle.net/10722/44839
ISSN
2015 Impact Factor: 5.629
2015 SCImago Journal Rankings: 1.586

 

DC FieldValueLanguage
dc.contributor.authorLeung, CKen_HK
dc.contributor.authorLam, FKen_HK
dc.date.accessioned2007-10-30T06:11:20Z-
dc.date.available2007-10-30T06:11:20Z-
dc.date.issued1997en_HK
dc.identifier.citationI E E Proceedings Vision, Image and Signal Processing, 1997, v. 144 n. 3, p. 161-167en_HK
dc.identifier.issn0018-9219en_HK
dc.identifier.urihttp://hdl.handle.net/10722/44839-
dc.description.abstractAn image segmentation algorithm that performs pixel-by-pixel segmentation on an image with consideration of the spatial information is described. The spatial information is the joint grey level values of the pixel to be segmented and its neighbouring pixels. The conditional probability that a pixel belongs to a particular class under the condition that the spatial information has been observed is defined to be the a posteriori spatial probability. A maximum a posteriori spatial probability (MASP) segmentation algorithm is proposed to segment an image such that each pixel is segmented into a particular class when the a posteriori spatial probability is a maximum. The proposed segmentation algorithm is implemented in an iterative form. During the iteration, a series of intermediate segmented images are produced among which the one that possesses the maximum amount of information in its spatial structure is chosen as the optimum segmented image. Results from segmenting synthetic and practical images demonstrate that the MASP algorithm is capable of achieving better results when compared with other global thresholding methods.en_HK
dc.format.extent953156 bytes-
dc.format.extent8841 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectEntropyen_HK
dc.subjectImage segmentationen_HK
dc.subjectSpatial informationen_HK
dc.subjectThresholdingen_HK
dc.titleMaximum a posteriori spatial probability segmenen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0018-9219&volume=144&issue=3&spage=161&epage=167&date=1997&atitle=Maximum+a+posteriori+spatial+probability+segmenen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.hkuros27066-

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