Name
Abstract | ||
|---|---|---|
Image registration is a necessary step in a variety of computer vision applications. One of the recent focus areas in image registration is extracting and matching features that are invariant to affine transformation. This is critical in various applications, including 3D reconstruction and object recognition. In this paper, we present a feature-based image registration method that is robust to scaling and rotation. This is achieved by extracting and matching features in log-polar domain, where rotation and scale correspond to translation. Registration parameters are then estimated by applying the RANSAC technique to the feature correspondences. The RANSAC technique provides a robust estimation even there are moving objects within the scene. Experimental results with synthetic and real images are provided. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICASSP.2005.1415539 | 2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING |
Keywords | Field | DocType |
computer vision,image reconstruction,robustness,detectors,object recognition,image registration,affine transformation,feature extraction,parameter estimation,application software,3d reconstruction | Computer vision,Feature detection (computer vision),Pattern recognition,Feature (computer vision),RANSAC,Computer science,Feature extraction,Artificial intelligence,Kanade–Lucas–Tomasi feature tracker,Real image,Image registration,Homography (computer vision) | Conference |
ISSN | Citations | PageRank |
1520-6149 | 3 | 0.50 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saikiran S. Thunuguntla | 1 | 3 | 0.50 |
| Bahadir K. Gunturk | 2 | 296 | 23.86 |