Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
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
Faster and Better: A Machine Learning Approach to Corner Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing parallel speeded-up robust features (P-SURF) via POSIX threads
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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The problem of finding corresponding points between images of the same scene is at the heart of many computer vision problems. In this paper we present a real-time approach to finding correspondences under changes in scale, rotation, viewpoint and illumination using Simple Circular Accelerated Robust Features (SCARF). Prominent descriptors such as SIFT and SURF find robust correspondences, but at a computation cost that limits the number of points that can be handled on low-memory, low-power devices. Like SURF, SCARF is based on Haar wavelets. However, SCARF employs a novel non-uniform sampling distribution, structure, and matching technique that provides computation times comparable to the state-of-the-art without compromising distinctiveness and robustness. Computing 512 SCARF descriptors takes 12.6ms on a 2.4GHz processor, and each descriptor occupies just 60 bytes. Therefore the descriptor is ideal for real-time applications which are implemented on low-memory, low-power devices.