Rotation and gray-scale transform invariant texture recognition using hidden Markov model

In the first stage of the proposed scheme the quadrature mirror filter (QMF) bank is used as the wavelet transform to decompose the texture image into subbands. Gray scale transform invariant features are then extracted from each subband image. In the second stage, the sequence of subbands is modeled as a hidden Markov model (HMM), and one HMM is designed for each class of textures. During recognition, the unknown texture is matched against all models. The best matched model identifies the texture class. The angles of rotation in the experiments are selected randomly in between -90° and 90°. Up to 95% classification accuracy is reported