Mohamed A. Naiel
Concordia University, Canada
Title: Approximation of feature pyramids in the transform domain for object detection
Biography
Biography: Mohamed A. Naiel
Abstract
Feature extraction from each scale of an image pyramid to construct a feature pyramid is considered as a computational bottleneck for many object detectors. In this paper, we present a novel technique for the approximation of feature pyramids in the transform domain, namely, the 2D discrete Fourier transform (2DDFT) or the 2D discrete cosine transform (2DDCT) domain. The proposed method is based on a feature resampling technique in the 2DDFT or the 2DDCT domain, exploiting the effect of resampling an image on the feature responses. Experimental results show that the proposed scheme provides feature approximation accuracy which is higher than that of the spatial domain counterpart when gradient magnitude or gradient histogram features are used. Further, when the proposed method is employed for object detection, it provides a detection accuracy superior to that provided by the spatial domain counterpart and compares favorably with that of the state-of-the-art techniques, while performing in real-time.