Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
Web-based 3D Reconstruction Service
Machine Vision and Applications
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time robust image feature description and matching
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
A GPU-based high-throughput image retrieval algorithm
Proceedings of the 5th Annual Workshop on General Purpose Processing with Graphics Processing Units
Enabling task-level scheduling on heterogeneous platforms
Proceedings of the 5th Annual Workshop on General Purpose Processing with Graphics Processing Units
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Speeded-Up Robust Features (SURF), an image local feature extracting and describing method, finds and describes point correspondences between images with different viewing conditions. Despite the fact that it has recently been developed, SURF has already successfully found its applications in the area of computer vision, and was reported to be more appealing than the earlier Scale-Invariant Feature Transform (SIFT) in terms of robustness and performance. This paper presents a multi-threaded algorithm and its implementation that computes the same SURF. The algorithm parallelises several stages of computations in the original, sequential design. The main benefit brought about is the acceleration in computing the descriptor. Tests have been performed to show that the parallel SURF (P-SURF) generally shortened the computation time by a factor of 2 to 6 than the original, sequential method when running on multi-core processors.