Rigid Image Registration by using Corner and Edge Contents with Application to Super-Resolution Duangnapa Wangrattanapranee Department of Communications and Integrated Systems Tokyo Institute of Technology, Meguro-ku,Tokyo, 152-8552, Japan duangnapa@nh.cradle.titech.ac.jp Akinori Nishihara Center for Research and Development of Educational Technology Tokyo Institute of Technology, Meguro-ku,Tokyo, 152-8552, Japan aki@cradle.titech.ac.jp Abstract Image registration method by using corner and edge con- tents is proposed. In this method, the algorithm runs as a coarse-to-fine strategy to find a precise alignment between images in a rigid planar motion. In a coarse stage, land- marks in both images are detected by Harris corner detec- tion. Rough alignment is geometrically found by matching a group of corners, instead of one-on-one, across two im- ages. Next in a refining stage, Radon transform is applied to compute the precision of registration in a subpixel accuracy. Our algorithm is compared with the FFT-based method on both simulated and real world images. With the same ac- curacy level with FFT-based method, the results show su- periorities in both degree of freedom and computational complexity. Finally, super-resolution imaging is introduced to demonstrate the capability of the proposed registration method on real world application. 1 Introduction Image registration is the process of aligning two sets of data or images which differ in some details into the same co- ordinate system. The differences can be caused by different time taken, different perspective viewpoints, and/or differ- ent image acquisition devices. Image registration is a cru- cial step in many applications e.g. super-resolution imag- ing, medical imaging, image mosaicing, satellite imaging, etc. Comprehensive surveys of image registration methods by [1, 2] show that the important factor in image regis- tration problem is to assume the transformation model and correctly find its parameters. This makes image registration method as an application-dependent solution. One way to categorize the existing methods is by using a feature cri- terion: feature-based methods estimate the transformation parameters by extracting feature points of two images, and feature-less methods estimate the transformation parame- ters by either determining the intensity information of the entire images or applying some transformations e.g. FFT- based methods, projection-based methods. To our knowledge, the FFT-based methods are one of the most widely used methods due to its simplicity and ability in subpixel motion estimation. However, it requires very high computational cost which limits existing methods to a certain degree of motion in order to speed up the algorithm, such as in [5]. In this paper, we propose a hybrid regis- tration method that overcomes those limitations: no limita- tions in global planar motion estimation with effective com- putation time, by combining the use of motion flexibility from feature-based methods with the high accuracy align- ment from feature-less methods. We first extract the land- mark features across the two images. Rather than matching the one-by-one feature points, we group them into clusters and then perform a group matching instead. The correspon- dences between the best matched portions and its runner up give out rough geometric estimates of the transformation parameters. Next, the precise value is determined by apply- ing the Radon transform on edge-images. Details of theo- ries behind each stage are described in Section 2. In Sec- tion 3, the experimental results on both simulated and real world images are shown. In Section 4, we provide a possi- ble application of our method in super-resolution imaging. Finally, the conclusion of this article is given in Section 5. Digital Image Computing: Techniques and Applications 978-0-7695-3456-5/08 $25.00 © 2008 IEEE DOI 10.1109/DICTA.2008.24 32