Advances in Rotation-Invariant Texture Analysis

Authors:
Alfonso Estudillo-Romero;Boris Escalante-Ramirez
Affiliations:
Fac. de Ingenieria, Edif. de Posgrado e Investigacion, Ciudad Universitaria, Universidad Nacional Autonoma de Mexico, Mexico, Mexico C.P. 04510;Fac. de Ingenieria, Edif. de Posgrado e Investigacion, Ciudad Universitaria, Universidad Nacional Autonoma de Mexico, Mexico, Mexico C.P. 04510
Venue:
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2009

Citing 10
Cited 1

A model-based method for rotation invariant texture classification

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Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models

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The Design and Use of Steerable Filters

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Image representation and compression with steered Hermite transforms

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Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification

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Rotation-invariant and scale-invariant Gabor features for texture image retrieval

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The hermite transform: a survey

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Fast communication: Rotation-invariant texture retrieval using wavelet-based hidden Markov trees

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Rotation-invariant multiresolution texture analysis using Radon and wavelet transforms

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The multiscale Hermite transform for local orientation analysis

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Rotation-invariant texture features from the steered Hermite transform

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Abstract

Robust rotation invariance has been a matter of great interest in many applications which use low-level features such as textures. In this paper, we propose a method to analyze and capture visual patterns from textures regardless their orientation. In order to achieve rotation invariance, visual texture patterns are locally described as one-dimensional patterns by appropriately steering the Cartesian Hermite coefficients. Experiments with two datasets from the Brodatz album were performed to evaluate orientation invariance. High average precision and recall rates were achieved by the proposed method.