Name
Abstract | ||
|---|---|---|
Feature detection and matching are essential parts in most computer vision applications. Many researchers have developed various algorithms to achieve good performance, such as SIFT (Scale-Invariant Feature Transform) and SURF (Speeded Up Robust Features). However, they usually fail when the scene has considerable out-of-plane rotation because they only focus on in-plane rotation and scale invariance. In this paper, we propose a novel feature description algorithm based on local graph representation and graph matching based, which is more robust to out-of-plane rotation. The proposed local graph encodes the geometric correlation between the neighboring features. In addition, we propose an efficient score function to compute the matching score between the local graphs. Experimental result shows that the proposed algorithm is more robust to out-of-plane rotation than conventional algorithms. |
Year | Venue | Keywords |
|---|---|---|
2013 | Asia-Pacific Signal and Information Processing Association Annual Summit and Conference | graph theory,feature extraction,computer vision |
Field | DocType | ISSN |
Graph theory,Scale-invariant feature transform,Graph cuts in computer vision,Pattern recognition,Feature (computer vision),Matching (graph theory),Artificial intelligence,Kanade–Lucas–Tomasi feature tracker,3-dimensional matching,Mathematics,Graph (abstract data type) | Conference | 2309-9402 |
Citations | PageRank | References |
1 | 0.35 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Man Hee Lee | 1 | 77 | 8.18 |
| In Kyu Park | 2 | 316 | 35.97 |