A Gabor wavelet pyramid-based object detection algorithm

Abstract

We introduce visual object detection architecture, making full use of technical merits of so-called multi-scale feature correspondence in the neurally inspired Gabor pyramid. The remarkable property of the multi-scale Gabor feature correspondence is found with scale-space approaches, which an original image Gabor-filtered with the individual frequency levels is approximated to the correspondingly sub-sampled image smoothed with the low-pass filter. The multi-scale feature correspondence is used for effectively reducing computational costs in filtering. In particular, we show that the multi-scale Gabor feature correspondence play an effective role in matching between an input image and the model representation for object detection. © 2011 Springer-Verlag.