Paper
8 May 2012 Comprehensive feature and texture fusion-based image registration approach
Francis Bowen, Eliza Y. Du, Jianghai Hu
Author Affiliations +
Abstract
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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Francis Bowen, Eliza Y. Du, and Jianghai Hu "Comprehensive feature and texture fusion-based image registration approach", Proc. SPIE 8406, Mobile Multimedia/Image Processing, Security, and Applications 2012, 840606 (8 May 2012); https://doi.org/10.1117/12.918693
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image registration

Image segmentation

Detection and tracking algorithms

Transform theory

Image analysis

Image processing

Image filtering

Back to Top