Object Recognition in High Clutter Images Using Line Features

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Object Recognition in High Clutter Images Using Line Features
We present an object recognition algorithm that uses model and image line features to locate complex objects in high clutter environments. Finding correspondences between model and image features is the main challenge in most object recognition systems. In our approach, corresponding line features are determined by a three-stage process. The first stage generates a large number of approximate pose hypotheses from correspondences of one or two lines in the model and image. Next, the pose hypotheses from the previous stage are quickly ranked by comparing local image neighborhoods to the corresponding local model neighborhoods. Fast nearest neighbor and range search algorithms are used to implement a distance measure that is unaffected by clutter and partial occlusion. The ranking of pose hypotheses is invariant to changes in image scale, orientation, and partially invariant to affine distortion. Finally, a robust pose estimation algorithm is applied for refinement and verification, star...
Philip David, Daniel DeMenthon
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2005
Where ICCV
Authors Philip David, Daniel DeMenthon
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