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20 August 2010 Correlation filter design using a single cluttered training image for detecting a noisy target in a nonoverlapping scene
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Abstract
Classical correlation filters for object detection and location estimation are designed under the assumption that the shape and intensity values of the object of interest are explicitly known. In this work we assume that the target is given at unknown coordinates in a reference image with a cluttered background corrupted by additive noise. We consider the nonoverlapping signal model for both the reference image and the input scene. Optimal correlation filters, with respect to signal-to-noise ratio and peak-to-output energy, for object detection and location estimation are derived. Estimation techniques are proposed for the parameters required for filter design. Computer simulation results obtained with the proposed filters are presented and compared with those of common correlation filters.
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Pablo Mario Aguilar-González and Vitaly Kober "Correlation filter design using a single cluttered training image for detecting a noisy target in a nonoverlapping scene", Proc. SPIE 7798, Applications of Digital Image Processing XXXIII, 779806 (20 August 2010); https://doi.org/10.1117/12.860215
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