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Abstract:
This paper describes a simple and efficient method to make
template-based object classification invariant to in-plane
rotations. The task is divided into two parts: orientation
discrimination and classification. The key idea is to perform the
orientation discrimination before the classification. This can be
accomplished by hypothesizing, in turn, that the input image
belongs to each class of interest. The image can then be rotated to
maximize its similarity to the training images in each class (these
contain the prototype object in an upright orientation). This
process yields a set of images, at least one of which will have the
object in an upright position. The resulting images can then be
classified by models which have been trained with only upright
examples. This approach has been successfully applied to two
real-world vision-based tasks: rotated handwritten digit
recognition and rotated face detection in cluttered scenes.
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