Classifying scaled and rotated textures using a region-matched algorithm

Chih-Chia Yao; Yu-Tin Chen

doi:10.1117/1.OE.51.7.077005

10 July 2012 Classifying scaled and rotated textures using a region-matched algorithm

Chih-Chia Yao, Yu-Tin Chen

Author Affiliations +

Funded by: National Science Council of the Republic of China

Optical Engineering, Vol. 51, Issue 7, 077005 (July 2012). https://doi.org/10.1117/1.OE.51.7.077005

Abstract

A novel method to correct texture variations resulting from scale magnification, narrowing caused by cropping into the original size, or spatial rotation is discussed. The variations usually occur in images captured by a camera using different focal lengths. A representative region-matched algorithm is developed to improve texture classification after magnification, narrowing, and spatial rotation. By using a minimum ellipse, a representative region-matched algorithm encloses a specific region extracted by the J-image segmentation algorithm. After translating the coordinates, the equation of an ellipse in the rotated texture can be formulated as that of an ellipse in the original texture. The rotated invariant property of ellipse provides an efficient method to identify the rotated texture. Additionally, the scale-variant representative region can be classified by adopting scale-invariant parameters. Moreover, a hybrid texture filter is developed. In the hybrid texture filter, the scheme of texture feature extraction includes the Gabor wavelet and the representative region-matched algorithm. Support vector machines are introduced as the classifier. The proposed hybrid texture filter performs excellently with respect to classifying both the stochastic and structural textures. Furthermore, experimental results demonstrate that the proposed algorithm outperforms conventional design algorithms.

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Chih-Chia Yao and Yu-Tin Chen "Classifying scaled and rotated textures using a region-matched algorithm," Optical Engineering 51(7), 077005 (10 July 2012). https://doi.org/10.1117/1.OE.51.7.077005

Published: 10 July 2012

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KEYWORDS

Image classification

Image segmentation

Wavelets

Optical engineering

Image processing algorithms and systems

Databases

Algorithm development

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