Description: We present an object class detection approach
which fully integrates the complementary strengths offered
by shape matchers. Like an object detector, it can
learn class models directly from images, and can localize
novel instances in the presence of intra-class variations,
clutter, and scale changes. Like a shape matcher,
it finds the boundaries of objects, rather than just their
bounding-boxes. This is achieved by a novel technique
for learning a shape model of an object class given images
of example instances. Furthermore, we also integrate
Hough-style voting with a non-rigid point matching
algorithm to localize the model in cluttered images.
As demonstrated by an extensive evaluation, our
method can localize object boundaries accurately and
does not need segmented examples for training (only
bounding-boxes).
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