Image registration using Adaptive Polar Transform

Abstract

Abstract-Image registration is an essential step in many image processing applications that need visual information from multiple images for comparison, integration, or analysis. Recently, researchers have introduced image registration techniques using the log-polar transform (LPT) for its rotation and scale invariant properties. However, it suffers from nonuniform sampling which makes it not suitable for applications in which the registered images are altered or occluded. Inspired by LPT, this paper presents a new registration algorithm that addresses the problems of the conventional LPT while maintaining the robustness to scale and rotation. We introduce a novel adaptive polar transform (APT) technique that evenly and effectively samples the image in the Cartesian coordinates. Combining APT with an innovative projection transform along with a matching mechanism, the proposed method yields less computational load and more accurate registration than that of the conventional LPT. Translation between the registered images is recovered with the new search scheme using Gabor feature extraction to accelerate the localization procedure. Moreover an image comparison scheme is proposed for locating the area where the image pairs differ. Experiments on real images demonstrate the effectiveness and robustness of the proposed approach for registering images that are subjected to occlusion and alteration in addition to scale, rotation, and translation.

An Analysis of Registration of Brain Images using Fast Walsh Hadamard Transform

“…A novel approach that addresses the range image registration problem for views having low overlap and which may include substantial noise for image registration was described by Silva et al in [6]. Matungka et al proposed an approach that involved Adaptive Polar Transform (APT) for Image registration in [7,10]. A feature-based fully non supervised methodology dedicated to the fast registration of medical images was described by Khaissidi et al in [8].…”

“…There is no universal registration [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] algorithm that can work reasonably well for all images. An appropriate registration algorithm for the particular problem must be chosen or developed, as they are adhoc in nature.…”

Efficient non-uniform deblurring based on generalized additive convolution model

“…where θ t =[ 0, t x , t y ] T , K θ t x denotes the in-plane translation operation, and PT(·) and IPT(·) stand for the polar transform and inverse polar transform [41], respectively. In the polar transform, we use the same interval to discretize angular and roll angles, and thus, the basis filter w t can be defined as w t = w t,θ 1 , w t,θ 1 , · · · , w t,θ nz (19) where θ 1 is the minimal roll angle and θ nz is the maximal roll angle.…”

Polar Transform for Segmentation of Optic Disk in Fundus Images

“…Log-Polar Transform (LPT) [21] is a widely used method to convert an image from the Cartesian coordinates (x, y) to the log-polar coordinates (ρ, θ) using the expressions given in Eqn. 1 and Eqn.…”