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Abstract:
The most popular algorithms for object detection require the
use of exhaustive spatial and scale search procedures. In such
approaches, an object is defined by means of local features. In
this paper we show that including contextual information in
object detection procedures provides an efficient way of cutting
down the need for exhaustive search. We present results with real
images showing that the proposed scheme is able to accurately
predict likely object classes, locations and sizes.
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