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Article: Maximum a posteriori spatial probability segmentation

TitleMaximum a posteriori spatial probability segmentation
Authors
KeywordsEntropy
Image segmentation
Spatial information
Thresholding
Issue Date1997
PublisherThe Institution of Engineering and Technology.
Citation
IEE 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
2006 Impact Factor: 0.461
2009 SCImago Journal Rankings: 0.450
ISI Accession Number ID

 

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.citationIEE Proceedings: Vision, Image and Signal Processing, 1997, v. 144 n. 3, p. 161-167en_HK
dc.identifier.issn1350-245Xen_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.extent8841 bytes-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherThe Institution of Engineering and Technology.en_HK
dc.relation.ispartofIEE Proceedings: Vision, Image and Signal Processing-
dc.subjectEntropyen_HK
dc.subjectImage segmentationen_HK
dc.subjectSpatial informationen_HK
dc.subjectThresholdingen_HK
dc.titleMaximum a posteriori spatial probability segmentationen_HK
dc.typeArticleen_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1049/ip-vis:19971181-
dc.identifier.scopuseid_2-s2.0-0031153644-
dc.identifier.hkuros27066-
dc.identifier.isiWOS:A1997XJ77000006-

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