Translation, rotation and scaling invariant object and texture classification using polyspectra

The problem addressed in this paper is the detection and classification of deterministic objects and random textures in a noisy scene. An energy detector is developed in the cumulant domain, by exploiting the noise insensitivity of higher-order statistics. An efficient implementation of this detector is described, using matched filtering. Its performance is analyzed using asymptotic distributions in a binary hypothesis testing framework. Object and texture classifiers are derived using higher-order statistics. They are minimum distance classifiers in the cumulant domain, and can be efficiently implemented using a bank of matched filters. Further, they are robust to additive Gaussian noise and insensitive to object shifts. Extensions, which can handle object rotation and scaling are also discussed. An alternate texture classifier is derived from a ML viewpoint, that is more efficient at the expense of complexity. The application of these algorithms to texture modeling is shown and consistent parameter estimators are obtained. Simulations are shown for both the object and the texture classification problems.