Feature points detection using combined character along principal orientation

Authors:
Sicong Yue;Qing Wang;Rongchun Zhao
Affiliations:
School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, P.R. China;School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, P.R. China;School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, P.R. China
Venue:
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Year:
2007

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Abstract

Most existing methods for determining localization of the image feature point are still inefficient in terms of the precision. In the paper, we propose a new algorithm for feature point detection based on the combined intensity variation status along the adaptive principal direction of the corner. Firstly, we detect principal orientation of each pixel, instead of calculating the gradients along the horizontal and vertical axes. And then we observe the intensity variations of the pixel along the adaptive principal axes and its tangent one respectively. When the combined variation status is classified into several specific types, it can be used to determine whether a pixel is a corner point or not. In addition to corner detection, it is also possible to use our proposed algorithm to detect the edges, isolated point and plain regions of a natural image. Experimental results on synthetic and natural scene images have shown that the proposed algorithm can successfully detect any kind of the feature points with good accuracy of localization.