Object Recognition from Local Scale-Invariant Features

Object Recognition from Local Scale-Invariant Features (1999)

by David G. Lowe

Venue:	PROC. OF THE INTERNATIONAL CONFERENCE ON COMPUTER VISION, CORFU
Citations:	1032 - 14 self

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Datum	Value	Source
TITLE	Object Recognition from Local Scale-Invariant Features	SVM HeaderParse 0.1
AUTHOR NAME	David G. Lowe	user correction
ABSTRACT	An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest-neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low-residual least-squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially-occluded images with a computation time of under 2 seconds.	user correction
YEAR	1999	user correction - Legacy Corrections
VENUE	PROC. OF THE INTERNATIONAL CONFERENCE ON COMPUTER VISION, CORFU	user correction
CITATIONS	23 found	ParsCit 1.0