| Abstract |
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This paper investigates applications of a new representation
for images, the similarity template. A similarity template is
a probabilistic representation of the similarity of pixels in
an image patch. It has application to detection of a class
of objects, because it is reasonably invariant to the color of
a particular object. Further, it enables the decomposition
of a class of objects into component parts over which robust
statistics of color can be approximated. These regions
can be used to create a factored color model that is useful
for recognition. Detection results are shown on a system
that learns to detect a class of objects (pedestrians) in static
scenes based on examples of the object provided automatically
by a tracking system. Applications of the factored
color model to image indexing and anomaly detection are
pursued on a database of images of pedestrians.
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 Additional Information
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Citation:
Chris Stauffer, Eric Grimson,
"Similarity templates for detection and recognition,"
cvpr,
p. 221,
2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1,
2001
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