Name
Abstract | ||
|---|---|---|
Given two views of a static scene, estimation of correspondences between them is required for various computer vision tasks, such as 3D reconstruction and registration, motion and structure estimation, and object recognition. Without loss of generality, this paper treats the correspondence-estimation problem in the context of feature-based rangescan registration of widely separated views and presents a novel approach to obtain globally consistent set of correspondences. In this paper, we define the notion of a weak feature, and follow the approach that avoids early commitment to the "best" match. It instead considers multiple candidate matches for each feature, and eventually models and solves correspondence-estimation as an optimization problem viz. weighted bipartite matching. We focus on developing a robust approach that suceeds in the presence of significant noise and sparsity in the input. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2425333.2425355 | ICVGIP |
Keywords | Field | DocType |
optimization problem viz,feature-based rangescan registration,multiple candidate,novel approach,consistent set,robust approach,early commitment,structure estimation,weak feature,point-set registration,correspondence-estimation problem | Computer vision,Point set registration,Pattern recognition,Computer science,Bipartite graph,Without loss of generality,Artificial intelligence,Kanade–Lucas–Tomasi feature tracker,Optimization problem,3D reconstruction,Cognitive neuroscience of visual object recognition | Conference |
Citations | PageRank | References |
1 | 0.35 | 26 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neeraj A. Kulkarni | 1 | 1 | 0.35 |
| Subodh Kumar | 2 | 527 | 49.65 |