posted on 2003-01-01, 00:00authored byOwen Carmichael, Martial Hebert
We present an approach to the recognition of complexshaped
objects in cluttered environments based on edge
cues. We first use example images of the desired object
in typical backgrounds to train a classifier cascade which
determines whether edge pixels in an image belong to an
instance of the object or the clutter. Presented with a novel
image, we use the cascade to discard clutter edge pixels.
The features used for this classification are localized, sparse
edge density operations. Experiments validate the effectiveness
of the technique for recognition of complex objects in
cluttered indoor scenes under arbitrary out-of-image-plane
rotation.