Name
Abstract | ||
|---|---|---|
Corner-detection techniques are being widely used in computer vision for example in object recognition to find suitable candidate points for feature registration and matching. Most computer-vision applications have to operate on real-time video sequences, hence maintaining a consistent throughput and high accuracy are important constrains that ensure high-quality object recognition. A high throughput can be achieved by exploiting the inherent parallelism within the algorithm on massively parallel architectures like many-core processors. However, accelerating such algorithms on many-core CPUs offers several challenges as the achieved speedup depends on the instantaneous load on the processing elements. In this work, we present a new resource-aware Harris corner-detection algorithm for many-core processors. The novel algorithm can adapt itself to the dynamically varying load on a many-core processor to process the frame within a predefined time interval. The results show a 19% improvement in throughput and an 18% improvement in accuracy. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-04891-8_1 | ARCS |
Field | DocType | Citations |
Corner detection,Computer science,Massively parallel,Parallel computing,Real-time computing,Throughput,Speedup,Pruning,Cognitive neuroscience of visual object recognition | Conference | 5 |
PageRank | References | Authors |
0.46 | 15 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johny Paul | 1 | 13 | 3.98 |
| Walter Stechele | 2 | 365 | 52.77 |
| Manfred Kröhnert | 3 | 54 | 6.16 |
| tamim asfour | 4 | 1889 | 151.86 |
| Benjamin Oechslein | 5 | 19 | 2.77 |
| Christoph Erhardt | 6 | 24 | 4.18 |
| Jens Schedel | 7 | 6 | 1.15 |
| Daniel Lohmann | 8 | 348 | 27.16 |
| Wolfgang Schröder-Preikschat | 9 | 896 | 90.63 |