Paper Info
Title
Unsupervised segmentation based on Von Mises circular distributions for orientation estimation in textured images
Abstract
In the case of textured images and more particularly of directional textures, a new parametric technique is proposed to estimate the orientation field of textures. It consists of segmenting the image into regions with homogeneous orientations, and estimating the orientation inside each of these regions. This allows us to maximize the size of the samples used to estimate the orientation without being corrupted by the presence of boundaries between regions. For that purpose, the local-hence noisy-orientations of the texture are first estimated using small filters (3 x 3 pixels). The segmentation of the obtained orientation field image then relies on a generalization of a minimum description length based segmentation technique, to the case of pi-periodic circular data modeled with Von Mises probability density functions. This leads to a fast segmentation algorithm without tuning parameters in the optimized criterion. The accuracy of the orientations estimated with the proposed method is then compared with other approaches on synthetic images and an application to the processing of real images is finally addressed. (C) 2012 SPIE and IS&T. [DOI: 10.1117/1.JEI.21.2.021102]
Year
DOI
Venue
2012
10.1117/1.JEI.21.2.021102
JOURNAL OF ELECTRONIC IMAGING
Keywords
Field
DocType
texture,orientation,von mises,segmentation
Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Minimum description length,Image processing,Parametric statistics,Smoothing,Artificial intelligence,Real image,von Mises yield criterion
Journal
Volume
Issue
ISSN
21
2
1017-9909
Citations 
PageRank 
References 
1
0.35
14
Authors
4
Name
Order
Citations
PageRank
Jean Pierre Da Costa1667.09
Frédéric Galland2281.79
Antoine Roueff3244.61
Christian Germain411318.95