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CiteWeb id: 19990000013

CiteWeb score: 11703

DOI: 10.1109/ICCV.1999.790410

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.

The publication "Object recognition from local scale-invariant features" is placed in the Top 1000 of the best publications in CiteWeb. Also in the category Computer Science it is included to the Top 100. Additionally, the publicaiton "Object recognition from local scale-invariant features" is placed in the Top 100 among other scientific works published in 1999.
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