Distinctive Image Features from Scale-Invariant Keypoints

Distinctive Image Features from Scale-Invariant Keypoints (2003)

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by David G. Lowe

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Datum	Value	Source
TITLE	Distinctive Image Features from Scale-Invariant Keypoints	user correction - Legacy Corrections
AUTHOR NAME	David G. Lowe	SVM HeaderParse 0.1
AUTHOR AFFIL	Computer Science Department; University of British Columbia	SVM HeaderParse 0.2
AUTHOR ADDR	Vancouver, B.C., Canada	SVM HeaderParse 0.1
ABSTRACT	This paper presents a method for extracting distinctive invariant features from images, which can be used to perform reliable matching between different images of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substantial range of affine distortion, addition of noise, change in 3D viewpoint, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through leastsquares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.	user correction - Legacy Corrections
YEAR	2003	user correction - Legacy Corrections
CITATIONS	37 found	ParsCit 1.0