DUGiDocsAn information theoretic framework for image segmentation
dc.contributor.author
dc.date.accessioned
2010-10-01T12:29:58Z
dc.date.available
2010-08-10T09:05:34Z
2010-10-01T12:29:58Z
dc.date.issued
2004
dc.identifier.citation
Rigau, J., Feixas, M., i Sbert, S. (2004). An information theoretic framework for image segmentation. International Conference on Image Processing : 2004 : ICIP '04, 2, 1193 - 1196. Recuperat 1 octubre 2010, a http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1419518
dc.identifier.isbn
0-7803-8554-3
dc.identifier.issn
1522-4880
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dc.description.abstract
In this paper, an information theoretic framework for image segmentation is presented. This approach is based on the information channel that goes from the image intensity histogram to the regions of the partitioned image. It allows us to define a new family of segmentation methods which maximize the mutual information of the channel. Firstly, a greedy top-down algorithm which partitions an image into homogeneous regions is introduced. Secondly, a histogram quantization algorithm which clusters color bins in a greedy bottom-up way is defined. Finally, the resulting regions in the partitioning algorithm can optionally be merged using the quantized histogram
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
IEEE
dc.relation.isformatof
Reproducció digital del document publicat a: http://dx.doi.org/10.1109/ICIP.2004.1419518
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© International Conference on Image Processing, 2004, vol. 2, p. 1193-1196
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Articles publicats (D-IMA)
dc.rights
Tots els drets reservats
dc.subject
dc.title
An information theoretic framework for image segmentation
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.identifier.doi