DocumentCode :
3076830
Title :
An efficient fractal measure for image texture recognition
Author :
Abiyev, Rahib ; Kilic, Kemal Ihsan
Author_Institution :
Dept. of Comput. Eng., Near East Univ., Nicosia, Cyprus
fYear :
2009
fDate :
2-4 Sept. 2009
Firstpage :
1
Lastpage :
4
Abstract :
Fractal measures like fractal dimension (FD), lacunarity, succolarity measure the geometrical complexity of objects and could be used to describe texture information of the images. For this purpose different box counting algorithms were developed to estimate FD. However the existing box-counting methods usually suffer from under counting or over counting, introducing difficulties in obtaining the exact value of the FD. This paper focuses on the box-counting´s power in uniquely identifying patterns and presents a new approach which considers the aggregate effects of all the gray levels in the boxes, rather than considering only two gray levels, (min and max) as in the case of traditional differential box-counting method. The proposed method uses new counting measure based on volume percentage of the gray levels inside the boxes. Results from experiments tabulated to depict the improved effect of the proposed method in recognition of the noisy test images from Brodatz Texture and normal test images from CASIA-V3 Iris Databases.
Keywords :
computational complexity; computational geometry; fractals; image recognition; image texture; box counting method; fractal dimension; fractal measure; geometrical complexity; image texture recognition; lacunarity measure; patterns identification; succolarity measure; Aggregates; Fractals; Image analysis; Image recognition; Image texture; Noise level; Noise measurement; Noise robustness; Testing; Volume measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009. ICSCCW 2009. Fifth International Conference on
Conference_Location :
Famagusta
Print_ISBN :
978-1-4244-3429-9
Electronic_ISBN :
978-1-4244-3428-2
Type :
conf
DOI :
10.1109/ICSCCW.2009.5379454
Filename :
5379454
Link To Document :
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