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).