Name
Abstract | ||
|---|---|---|
This paper presents a method for fast feature-based matching which enables 7 in- dependent targets to be localised in a video sequence with an average total processing time of 7.46ms per frame. We extend recent work (14) on fast matching using His- togrammed Intensity Patches (HIPs) by adding a rotation invariant framework and a tree- based lookup scheme. Compared to state-of-the-art fast localisation schemes (15) we achieve better matching robustness in under a quarter of the computation time and re- quiring 5-10 times less memory. |
Year | Venue | Field |
|---|---|---|
2009 | BMVC | Histogram,Computer vision,Scale-invariant feature transform,Image stitching,Image warping,Pattern recognition,Interest point detection,Computer science,Image retrieval,Robustness (computer science),Artificial intelligence,Pixel |
DocType | Citations | PageRank |
Conference | 29 | 1.60 |
References | Authors | |
15 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Taylor | 1 | 29 | 1.60 |
| Tom Drummond | 2 | 2676 | 159.45 |