2D-feature descriptor without orientation compensation
Abdelhak Bennia, Manel Benaissa · International Journal of Computational Vision and Robotics · 2019
Several feature descriptors have been proposed in the literature with a variety of definitions and a common goal, describe and get the best possible match between potentially interesting points in two images. In this paper, we proposed a new orientation invariant feature descriptor without an additional step dedicated to this task. We exploited the information provided by two representations of the image (intensity and gradient) for a better understanding and representation of the feature point and its surroundings. The information provided is summarised in two cumulative histograms and used in the description and matching process of the feature points. The experimental results show its robustness in the face of multiple image changes.