New adaptive correlation filters for reliable recognition of geometrically distorted objects in blurred and noisy scenes are proposed. The filters are based on modified synthetic discriminant functions. The information about objects to be recognized, false objects, disjoint background, additive noise, and expected degradations of targets and input scenes are utilized in an iterative training algorithm. The algorithm is used to design a correlation filter with a specified discrimination capability. Computer simulation results obtained with the proposed adaptive filters in test scenes are discussed and compared with those of various correlation filters in terms of discrimination capability and location errors.
Most of captured images present degradations due to blurring and additive noise; moreover objects of interest can be
geometrically distorted. The classical methods for pattern recognition based on correlation are very sensitive to intensity
degradations and geometric distortions. In this work, we propose an adaptive generalized filter based on synthetic
discriminant function (SDF). With the help of computer simulation we analyze and compare the performance of the adaptive correlation filter with that of common correlation filters in terms of discrimination capability and accuracy of target location when input scenes are degraded and a target is geometrically distorted.
In pattern recognition two different tasks are distinguished; that is, detection of objects and estimation of their exact
positions (localization) in images. Traditional methods for pattern recognition are based on correlation or template
matching. These methods are attractive because they can be easily implemented with digital or optical processors.
However, they are very sensitive to intensity degradations that always are present in observed images. In this paper we
analyze and compare correlation-based methods for reliable detection and localization of degraded objects.
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