ASN: Image Keypoint Detection from Adaptive Shape Neighborhood

Authors:
Jean-Nicolas Ouellet;Patrick Hébert
Affiliations:
Computer Vision and Systems Laboratory, Laval University, Quebec, Canada G1V 0A6;Computer Vision and Systems Laboratory, Laval University, Quebec, Canada G1V 0A6
Venue:
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Year:
2008

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Abstract

We describe an accurate keypoint detector that is stable under viewpoint change. In this paper, keypoints correspond to actual junctions in the image. The principle of ASN differs fundamentally from other keypoint detectors. At each position in the image and before any detection, it systematically estimates the position of a potential junction from the local gradient field. Keypoints then appear where multiple position estimates are attracted. This approach allows the detector to adapt in shape and size to the image content. One further obtains the area where the keypoint has attracted solutions. Comparative results with other detectors show the improved accuracy and stability with viewpoint change.