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Carnegie Mellon Robotics Institute

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Abstract

We frame the problem of object recognition from edge cues in terms of determining whether individual edge pixels belong to the target object or to clutter, based on the configuration of edges in their vicinity. A classifier solves this problem by computing sparse, localized edge features at image locations determined at training time. In order to save computation and solve the aperture problem, we apply a cascade of these classifiers to the image, each of which computes edge features over larger image regions than its predecessors. Experiments apply this approach to the recognition of real objects with holes and wiry components in cluttered scenes under arbitrary out-of-image-plane rotation.

Keywords

object recognition, computer vision

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): 3D Computer Vision Group

Number of pages: 10

Text Reference

Owen Carmichael and Martial Hebert, "Object Recognition by a Cascade of Edge Probes," British Machine Vision Conference 2002, September, 2002, pp. 103-112.

BibTeX Reference

@inproceedings{Carmichael_2002_4061,
   author = "Owen Carmichael and Martial Hebert",
   editor = "Paul Rosin, David Marshall",
   title = "Object Recognition by a Cascade of Edge Probes",
   booktitle = "British Machine Vision Conference 2002",
   pages = "103-112",
   publisher = "British Machine Vision Association",
   month = "September",
   year = "2002",
   volume = "1",
}

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