DFOB: Detecting and describing features by octagon filter bank for fast image matching

Abstract

Feature correspondence is vital in image processing and computer vision. To find corresponding pairs efficiently, in this paper it is proposed that feature detector and descriptor are constructed from the same octagon filter bank (DFOB). The DFOB method is a novel method for the detection, orientation computation, and description of feature points, and is very efficient as computationally implemented by integral images. The matching capability of DFOB is close to the prevalent methods such as SIFT and SURF, because they all detect blob-like image structures as interest features and describe these features using histogram of oriented gradients. Experimental results on benchmark datasets demonstrate that the matching performance of DFOB is comparable with the SIFT and SURF algorithms, while the computational cost is much lower, especially the proposed descriptor is about 50 times faster than SURF descriptor.