Rotation invariant texture recognition using a steerable pyramid
Published 17 December 2002
Hayit Greenspan, Serge Belongie, R.M. Goodman, Pietro Perona
Citations104
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TL;DR
A rotation-invariant texture recognition system using a steerable oriented pyramid to extract representative features for the input textures and a comparison across the performance of the k-NN, backpropagation and rule-based classifiers.
Abstract
A rotation-invariant texture recognition system is presented. A steerable oriented pyramid is used to extract representative features for the input textures. The steerability of the filter set allows a shift to an invariant representation via a DFT-encoding step. Supervised classification follows. State-of-the-art recognition results are presented on a 30 texture database with a comparison across the performance of the k-NN, backpropagation and rule-based classifiers. In addition, high accuracy estimation of the input rotation angle is demonstrated.
Keywords
Computer Science
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