Abstract
Feature points are effective for wide range of computer vision applications. In the last two decades, a large number of feature point detection and description algorithms have been proposed. All these algorithms are implemented differently but have a common objective to detect and describe feature points invariant to scale, rotation, intensity, and affine variations. Several comparative studies of feature points have been reported in literature. These studies are either application specific or deal with common type of transformations and deformations. Additionally, they primarily focus on evaluation of feature points on gray scale or RGB images. In contrast, this paper presents a comparison of feature points on multisensor images, which possess non linear intensity changes. The objective is to identify robust feature points for image to image matching tasks on multisensor images. Six well known feature point detector and seventeen popular descriptor algorithms are compared. Experimental results obtained on four different image datasets show that the combination of Harris detector with BRIEF descriptor outperforms all other detector-descriptor combinations on multisensor images. (C) 2017 Elsevier Ltd. All rights reserved.