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From Images to Shape Models for Object Detection
V. Ferrari, F. Jurie, and C. Schmid
, 2008
LEAR - INRIA
Abstract
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 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 made possible 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, while needing no segmented examples for training (only bounding-boxes).
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@Techreport{eth_biwi_00597,
author = {V. Ferrari and F. Jurie and and C. Schmid},
title = {From Images to Shape Models for Object Detection},
year = {2008},
month = {July},
institution = {LEAR - INRIA},
keywords = {object detection, shape matching, contour features}
}