Fast pattern recognition using normalized grey-scale correlation in a pyramid image representation

The ability to quickly locate one or more instances of a model in a grey scale image is of importance to industry. The recognition/localization must be fast and accurate. In this paper we present an algorithm which incorporates normalized correlation into a pyramid image representation structure to perform fast recognition and localization. The algorithm employs an estimate of the gradient of the correlation surface to perform a steepest descent search. Test results are given detailing search time by target size, effect of rotation and scale changes on performance, and accuracy of the subpixel localization algorithm used in the algorithm. Finally, results are given for searches on real images with perspective distortion and the addition of Gaussian noise.