Scale and rotation invariant texture classification
Michael Leung, A. M. Peterson · 2003
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%.>