Name
Abstract | ||
|---|---|---|
In this paper, we present a methodology to categorize camera captured documents into pre-defined logo classes. Unlike scanned documents, camera captured documents suffer from intensity variations, partial occlusions, cluttering, and large scale variations. Furthermore, the existence of non-uniform folds and the lack of document being flat make this task more challenging. We present the selection of robust local features and the corresponding parameters by comparisons among SIFT, SURF, MSER, Hessian-affine, and Harris-affine. We evaluate the system not only with respect to amount of space required to store the local features information but also with respect to categorization accuracy. Moreover, the system handles the identification of multiple logos on the document at the same time. Experimental results on a challenging set of real images demonstrate the efficiency of our approach. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23678-5_14 | CAIP (2) |
Keywords | Field | DocType |
logo identification,non-uniform fold,large scale variation,local features information,corresponding parameter,robust local feature,scanned document,intensity variation,multiple logo,challenging set,clustering | Computer vision,Categorization,Scale-invariant feature transform,Pattern recognition,Computer science,Logo,Artificial intelligence,Cluttering,Real image,Cluster analysis | Conference |
Volume | ISSN | Citations |
6855 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Venkata Gopal Edupuganti | 1 | 7 | 0.79 |
| Frank Y. Shih | 2 | 1103 | 89.56 |
| Suryaprakash Kompalli | 3 | 71 | 7.25 |