Model-based image matching using location
Model-based image matching using location
Three-dimensional object recognition
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Rigid, affine and locally affine registration of free-form surfaces
International Journal of Computer Vision
Multi-Level Shape Representation Using Global Deformations andLocally Adaptive Finite Elements
International Journal of Computer Vision
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.00 |
Estimating planar projective transform (homography) from a pair of images is a classical problem in computer vision. In this paper, we propose a novel algorithm for direct registering two point sets in R2 using projective transform without using intensity values. In this very general context, there is no easily established correspondences that can be used to estimate the projective transform, and most of the existing techniques become either inadequate or inappropriate. While the planar projective transforms form an eight-dimensional Lie group, we show that for registering 2D point sets, the search space for the homographies can be effectively reduced to a three-dimensional space. To further improve on the running time without significantly reducing the accuracy of the registration, we propose a matching cost function constructed using local polynomial moments of the point sets and a coarse to fine approach. The resulting registration algorithm has linear time complexity with respect to the number of input points. We have validated the algorithm using points sets collected from real images. Preliminary experimental results are encouraging and they show that the proposed method is both efficient and accurate.