DocumentCode :
2305394
Title :
Segmentation of noisy images using information theory based approaches
Author :
Galland, Frédéric ; Réfrégier, Philippe
Author_Institution :
Phys. & Image Process. group, Aix-Marseille Univ., Marseille
fYear :
2008
fDate :
23-26 Nov. 2008
Firstpage :
1
Lastpage :
4
Abstract :
In this presentation, we propose to discuss some interesting properties of segmentation techniques based on the minimization of the stochastic complexity. We emphasize the general framework provided by the minimization of the stochastic complexity for segmentation purpose, some of its main advantages and also some of the motivating perspectives that are open by such approaches. We illustrate this presentation with different results obtained in our research group with polygonal parametric shape descriptions, level set models of contours and polygonal grids to partition images into an arbitrary number of homogeneous regions.
Keywords :
computational complexity; image denoising; image segmentation; information theory; information theory; level set model; noisy image segmentation; polygonal grid; polygonal parametric shape description; stochastic complexity; Active contours; Image analysis; Image processing; Image segmentation; Information theory; Markov random fields; Physics; Statistics; Stochastic processes; Stochastic resonance; Minimum Description Length; Noise in imaging systems; Segmentation; Stochastic complexity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing Theory, Tools and Applications, 2008. IPTA 2008. First Workshops on
Conference_Location :
Sousse
Print_ISBN :
978-1-4244-3321-6
Electronic_ISBN :
978-1-4244-3322-3
Type :
conf
DOI :
10.1109/IPTA.2008.4743794
Filename :
4743794
Link To Document :
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