Image segmentation by unifying region and boundary information

A two-stage method of image segmentation based on gray level cooccurrence matrices is described. An analysis of the distributions within a cooccurrence matrix defines an initial pixel classification into both region and interior or boundary designations. Local consistency of pixel classification is...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 1990-10, Vol.12 (10), p.929-948
Main Authors: Haddon, J.F., Boyce, J.F.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Entropy
Exact sciences and technology
Image analysis
Image processing
Image segmentation
Image sequences
Infrared imaging
Interference
Labeling
Pattern recognition. Digital image processing. Computational geometry
Robustness
Statistics
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Summary:A two-stage method of image segmentation based on gray level cooccurrence matrices is described. An analysis of the distributions within a cooccurrence matrix defines an initial pixel classification into both region and interior or boundary designations. Local consistency of pixel classification is then implemented by minimizing the entropy of local information, where region information is expressed via conditional probabilities estimated from the cooccurrence matrices, and boundary information via conditional probabilities which are determined a priori. The method robustly segments an image into homogeneous areas and generates an edge map. The technique extends easily to general edge operators. An example is given for the Canny operator. Applications to synthetic and forward-looking infrared (FLIR) images are given.< >
ISSN:0162-8828
1939-3539
DOI:10.1109/34.58867