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

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 ass...

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Veröffentlicht in:	IEEE transactions on image processing 2005-10, Vol.14 (10), p.1486-1502
Hauptverfasser:	Junmo Kim, Fisher, J.W., Yezzi, A., Cetin, M., Willsky, A.S.
Format:	Artikel
Sprache:	eng
Schlagworte:	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
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