DocumentCode :
468439
Title :
A Methodology for Automatically Detecting Texture Paths and Patterns in Images
Author :
Bourbakis, N. ; Patil, Raj
Author_Institution :
Wright State Univ., Dayton
Volume :
1
fYear :
2007
fDate :
29-31 Oct. 2007
Firstpage :
504
Lastpage :
512
Abstract :
This paper presents a methodology that uses formal languages representation for image processing for automatically detecting texture patterns and determining texture paths in images. The texture paths are detected and extracted by using image processing techniques, such as image segmentation to isolate regions of interest, and then the extraction of repeating textures. The detection of the texture blocks is obtained by recursively using 27 times 27, 9 times 9, and 3 times 3 windows. Predefined repeating texture patterns are also searched for in each of the sets. For each set of texture blocks with similar or same characteristics, curve fitting techniques are used for association of patterns with the texture. The selected curve is split into straight line segments, and the pattern is finally represented using the defined context-free formal language methodology. The methodology has capabilities to learn texture paths associated with certain "objects", which generate them, and use this knowledge for a variety of applications. Results are shown for various color images.
Keywords :
context-free languages; curve fitting; feature extraction; image colour analysis; image segmentation; image texture; color images; context-free formal language; curve fitting techniques; formal languages representation; image pattern detection; image processing; image segmentation; repeating texture extraction; texture path detection; Application software; Computer vision; Curve fitting; Data mining; Formal languages; Frequency; Humans; Image processing; Image segmentation; Smoothing methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on
Conference_Location :
Patras
ISSN :
1082-3409
Print_ISBN :
978-0-7695-3015-4
Type :
conf
DOI :
10.1109/ICTAI.2007.176
Filename :
4410327
Link To Document :
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