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.
This work was supported by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Centre “Invasive Computing” (SFB/TR 89).
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Paul, J. et al. (2014). Resource-Aware Harris Corner Detection Based on Adaptive Pruning. In: Maehle, E., Römer, K., Karl, W., Tovar, E. (eds) Architecture of Computing Systems – ARCS 2014. ARCS 2014. Lecture Notes in Computer Science, vol 8350. Springer, Cham. https://doi.org/10.1007/978-3-319-04891-8_1
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DOI: https://doi.org/10.1007/978-3-319-04891-8_1
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