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Object Recognition by a Cascade of Edge Probes

Owen Carmichael and Martial Hebert
Conference Paper, Carnegie Mellon University, British Machine Vision Conference 2002, Vol. 1, pp. 103-112, September, 2002

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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.

BibTeX Reference
title = {Object Recognition by a Cascade of Edge Probes},
author = {Owen Carmichael and Martial Hebert},
booktitle = {British Machine Vision Conference 2002},
keyword = {object recognition, computer vision},
publisher = {British Machine Vision Association},
school = {Robotics Institute , Carnegie Mellon University},
month = {September},
year = {2002},
volume = {1},
pages = {103-112},
address = {Pittsburgh, PA},