Effect of Illumination and Blur Change on Markerless Image Registration Methods

Abstract

Image registration forms a basis for a wide variety of applications in Computer Vision. The methods used for image registration are generally divided into two categories: 1) Extrinsic: based on some external object placed in an image. 2) Intrinsic: based on image information. Intrinsic methods work upon image features, pixel intensity levels etc. to determine measurements with respect to the requirements of a particular application. This paper presents a comparative analysis of three widely used image registration methods i.e. SIFT, ASIFT and SURF, for intrinsic image registration process. Quality of images describing medical, natural and structured scenes with different illumination and blur conditions is correlated with the performance analysis of the three image registration methods. Results show that the total count of extracted features in an image and correspondences found between two images (for determining the correct number of matches between an image pair, RANSAC algorithm is used for eliminating outliers) decreases with decreasing quality in terms of both blur and illumination conditions. Also, ASIFT outperforms SIFT and SURF in these changing imaging conditions.