Name
Abstract | ||
|---|---|---|
This paper proposes an architecture for an exact object detection system. The implementation as well as the communication between individual system components is detailed in the paper. Well known methods for feature detection and extraction were used. Fast and precise method for feature comparison is presented. The proposed system was evaluated by training the dataset and querying the dataset. With 12 Workers, the response time of querying the dataset consisting of $$100\\,000$$ images were just below 20 seconds. Also system trained dataset of this size with same amount of workers in about an hour. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-20904-3_47 | ICVS |
Keywords | Field | DocType |
CBIR, QBIC, Image retrieval, Hamming exact search, Message broker, Image processing, Computer vision, Sift, Freak | Scale-invariant feature transform,Computer vision,Object detection,Architecture,FREAK,Computer science,Image retrieval,Response time,Image processing,Message broker,Artificial intelligence | Conference |
Volume | ISSN | Citations |
9163 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Josef Hák | 1 | 0 | 0.34 |
| Martin Klíma | 2 | 0 | 0.34 |
| Mikulas Krupicka | 3 | 0 | 0.34 |
| Václav Cadek | 4 | 0 | 0.34 |