Correlation Pattern Recognition in Nonoverlapping Scene Using a Noisy Reference

Abstract

Abstract. Correlation filters for recognition of a target in nonoverlapping background noise are proposed. The object to be recognized is given implicitly; that is, it is placed in a noisy reference image at unknown coordinates. For the filters design two performance criteria are used: signalto-noise ratio and peak-to-output energy. Computer simulations results obtained with the proposed filters are discussed and compared with those of classical correlation filters in terms of discrimination capability.

Design of Correlation Filters for Pattern Recognition Using a Noisy Training Image

“…Since the introduction of the matched filter [1], correlation filters have been extensively used for pattern recognition [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Two tasks of interest in pattern recognition are detection of a target and the estimation of its location in an observed scene.…”

“…In practical situations, the target may be given in a noisy reference image with a cluttered background. Recently [14,15], a signal model was introduced that accounts for additive noise in the image used for filter design. In this paper, we extend that work to account for the presence of a nonoverlapping background in a training image that is corrupted by additive noise.…”