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In this project correlaton filters are used for?the shape and graylevel structure of the object of interest are explicitly available.
In this project?we propose the design of correlation filters when the appearance of the target is given in a single training image.
The target is assumed to be embedded in a cluttered background and the image is assumed to be corrupted by additive sensor noise. The designed filters are used to detect the target in an input scene modeled by the nonoverlapping signal model.
An optimal correlation filter, with respect to the peak-to-output energy ratio criterion, is proposed for object detection and location estimation.?
Experimental results on synthetic and real images show the ability of the proposed approach to constrain an evolving curve towards a target shapes that may be occluded and cluttered under rigid transformations.