Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Vlfeat: an open and portable library of computer vision algorithms
Proceedings of the international conference on Multimedia
Feature tracking and matching in video using programmable graphics hardware
Machine Vision and Applications
High performance geospatial analysis on emerging parallel architectures
High performance geospatial analysis on emerging parallel architectures
| Hi-index | 0.00 |
We describe an approach to parallelizing SIFT and other scale-space-based feature transformation algorithms. By partitioning the workload in a novel fashion, our approach can take advantage of all forms of parallelism: the shared-memory parallelism of threaded programming, the distributed-memory approach of cluster programming, and GPU-based acceleration. Also described is an implementation of this approach called SOHC, or SIFT on hybrid clusters, which can take advantage of hybrid clusters to accelerate the transformation of arbitrarily large images into sets of features. SOHC is both portable and scalable: it can run on systems ranging from a desktop without any GPU hardware, to a cluster of multi-GPU nodes, with the only difference being time to complete the extraction. It is the only implementation of SIFT capable of operating directly (i.e. without dropping features at tile boundaries) on gigapixel-sized images often encountered in geospatial applications.