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Machine Vision and Applications

, Volume 29, Issue 3, pp 455–466 | Cite as

A new approach for rotation-invariant and noise-resistant texture analysis and classification

  • Mohammad Mahdi Feraidooni
  • Davood Gharavian
Original Paper
  • 456 Downloads

Abstract

The analysis and classification of images, such as texture images, is one of the substantial and important fields in image processing. Due to destructive effects of image rotation and noise, the stability and efficiency of texture analysis and classification methods are an important research area. In this paper, a new method for texture analysis and classification has been proposed which is based on a particular combination of wavelet, ridgelet and Fourier transforms as well as support vector machine. The proposed method has been evaluated for 13 texture datasets produced by three original datasets containing 25 and 111 original textures from Brodatz database and 24 original textures from OUTEX database. These datasets comprise 415584 and 93600 rotated noise-free and noisy texture images for Brodatz database and also 49920 noisy and 4320 noise-free texture images for OUTEX database, respectively. Simulation results demonstrate the capability, efficiency and also stability of the proposed method especially for real-time rotation-invariant and noise-resistant texture analysis and classification.

Keywords

Texture analysis Rotation and noise invariant Wavelet transform Ridgelet transform Support vector machine Computational time 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Telecommunications, Faculty of Electrical EngineeringShahid Beheshti University, G. C.TehranIran

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