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Object Recognition from Local Scale-Invariant Features

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by David G. Lowe
Citations:2738 - 13 self
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BibTeX

@MISC{Lowe_objectrecognition,
    author = {David G. Lowe},
    title = {Object Recognition from Local Scale-Invariant Features},
    year = {}
}

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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.

Keyphrases

object recognition    local scale-invariant feature    robust object recognition    new class    unknown model parameter    nearest-neighbor indexing method    inferior temporal cortex    low-residual least-squares solution    image gradient    image key    local image feature    object recognition system    final verification    illumination change    multiple orientation plane    stable point    image scaling    computation time    feature share similar property    scale space    candidate object match    primate vision    staged filtering approach    experimental result    local geometric deformation    multiple scale    cluttered partially-occluded image   

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