A framework for object recognition in images using the short time Fourier transform

The problem of object recognition in images regardless of their scale and orientation is considered in this paper. A framework is used to train and to recognize or classify a transformed object. A set of features obtained from the short-time Fourier transform of the object is used for scale and rotation invariant recognition. An analysis window is used to compute the short-time Fourier transform. The Fourier magnitudes in the polar domain constitute the scale invariant and rotation invariant features. Since, short time sections are used in this method, features are more separable because of the localization of the window which is useful for discriminating variants of very similar objects. The recognition system is tested for different sets of scales and rotations of several objects. This framework performed well for the range of scales and orientations of the objects considered. The framework is computationally efficient and showed robustness in the presence of noise. The tasks involved are simple and the framework can be used for real-time applications.