Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Architecture of Computing Systems, ARCS 2014 |
| Herausgeber (Verlag) | Springer Verlag |
| Seiten | 1-12 |
| Seitenumfang | 12 |
| ISBN (Print) | 9783319048901 |
| Publikationsstatus | Veröffentlicht - 2014 |
| Extern publiziert | Ja |
| Veranstaltung | 27th International Conference on Architecture of Computing Systems, ARCS 2014 - Luebeck, Deutschland Dauer: 25 Feb. 2014 → 28 Feb. 2014 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 8350 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
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.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Architecture of Computing Systems, ARCS 2014. Springer Verlag, 2014. S. 1-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 8350 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Resource-Aware Harris Corner Detection Based on Adaptive Pruning
AU - Paul, Johny
AU - Stechele, Walter
AU - Kröhnert, Manfred
AU - Asfour, Tamim
AU - Oechslein, Benjamin
AU - Erhardt, Christoph
AU - Schedel, Jens
AU - Lohmann, Daniel
AU - Schröder-Preikschat, Wolfgang
N1 - Funding information: This work was supported by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Centre “Invasive Computing” (SFB/TR 89).
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - adaptive pruning
KW - Harris corner detection
KW - invasive computing
KW - resource-aware programming
UR - http://www.scopus.com/inward/record.url?scp=84958541522&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-04891-8_1
DO - 10.1007/978-3-319-04891-8_1
M3 - Conference contribution
AN - SCOPUS:84958541522
SN - 9783319048901
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 12
BT - Architecture of Computing Systems, ARCS 2014
PB - Springer Verlag
T2 - 27th International Conference on Architecture of Computing Systems, ARCS 2014
Y2 - 25 February 2014 through 28 February 2014
ER -