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A nonparametric statistical method for image segmentation using information theory and curve evolution

Junmo Kim ; Fisher, J.W. ; Yezzi, A. ; Cetin, M. ; Willsky, A.S.

IEEE transactions on image processing, 2005-10, Vol.14 (10), p.1486-1502 [Periódico revisado por pares]

New York, NY: IEEE

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Título:
A nonparametric statistical method for image segmentation using information theory and curve evolution
Autor: Junmo Kim ; Fisher, J.W. ; Yezzi, A. ; Cetin, M. ; Willsky, A.S.
Assuntos: Algorithms ; Applied sciences ; Artificial Intelligence ; Computer Simulation ; Curve evolution ; Evolution ; Exact sciences and technology ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Image processing ; Image segmentation ; Imaging, Three-Dimensional - methods ; Information Storage and Retrieval - methods ; Information Theory ; Information, signal and communications theory ; Laboratories ; level-set methods ; Models, Statistical ; Mutual information ; nonparametric density estimation ; Parametric statistics ; Pattern recognition ; Pattern Recognition, Automated - methods ; Pixel ; Probability distribution ; Signal and communications theory ; Signal processing ; Signal Processing, Computer-Assisted ; Signal representation. Spectral analysis ; Signal, noise ; Statistical analysis ; Statistical distributions ; Studies ; Telecommunications and information theory
É parte de: IEEE transactions on image processing, 2005-10, Vol.14 (10), p.1486-1502
Notas: ObjectType-Article-2
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Descrição: In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the image pixel intensities within each region are completely unknown a priori, and we formulate the problem based on nonparametric density estimates. Due to the nonparametric structure, our method does not require the image regions to have a particular type of probability distribution and does not require the extraction and use of a particular statistic. We solve the information-theoretic optimization problem by deriving the associated gradient flows and applying curve evolution techniques. We use level-set methods to implement the resulting evolution. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems. Furthermore, our method, which does not require any training, performs as good as methods based on training.
Editor: New York, NY: IEEE
Idioma: Inglês

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A nonparametric statistical method for image segmentation using information theory and curve evolution

Junmo Kim ; Fisher, J.W. ; Yezzi, A. ; Cetin, M. ; Willsky, A.S.

New York, NY: IEEE

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