Comprehensive Feature and Texture Fusion-based Image Registration Approach

Many computer vision applications benefit from image registration where the mutual geometric information between images is estimated. With this estimation, the perspective of one image can be altered such that mutual information can easily be determined. This task is an essential step in object recognition. Existing methods seek to minimize some dissimilarity measure through optimization approaches such as the gradient descent method or particle swarm theory. The challenge associated with the optimization methods lies in the unintended convergence of local minima or maxima. Feature-based approaches attempt to identify keypoints of an image that are suitable for the homography estimation; however, these methods produce a large set of candidate points. We propose a comprehensive image registration method that takes advantage of feature point detection but imposes a strict method for identifying optimal interest points for the estimation of the homography matrix. The proposed method combines feature-based results with texture-based optimizations for the selection of control points. The preliminary experimental results show that our methodology can greatly reduce the computational time while improving registration accuracy.