Scale and rotation invariant texture classification

Abstract

The problem of classifying scaled and rotated texture images is addressed using a number of different approaches. The first approach extracts invariant features from texture images; moment invariant features and log-polar filter features are employed. The second approach follows a mental transformation procedure similar to the process of scaled and rotated shape recognition carried out by human beings. Texture images are rotated and scaled to a specific size and orientation which allows the application of a more general rotation-scale sensitive classification scheme. A two-stage estimation procedure is introduced to determine the required scaling and rotation factors. Simulations show that the mental transformation approaches outperformed the other approaches, giving a good averaged error rate of 10%.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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