Abhijit Mahalanobis

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Correlation methods are becoming increasingly attractive tools for image recognition and location. This renewed interest in correlation methods is spurred by the availability of high-speed image processors and the emergence of correlation filter designs that can optimize relevant figures of merit. In this paper, a new correlation filter design method is(More)
Support vector machine (SVM) classifiers are popular in many computer vision tasks. In most of them, the SVM classifier assumes that the object to be classified is centered in the query image, which might not always be valid, e.g., when locating and classifying a particular class of vehicles in a large scene. In this paper, we introduce a new classifier(More)
In target recognition applications of discriminant or classification analysis, each 'feature' is a result of a convolution of an imagery with a filter, which may be derived from a feature vector. It is important to use relatively few features. We analyze an optimal reduced-rank classifier under the two-class situation. Assuming each population is Gaussian(More)
Lockheed Martin and the University of Central Florida (UCF) are jointly investigating the use of a network of COTS video cameras and computers for a variety of security and surveillance operations. The detection and tracking of humans as well as vehicles is of interest. The three main novel aspects of the work presented in this paper are i) the integration(More)
We introduce subband correlation filters (SCFs) as a solution to the problem of object recognition at multiple resolution levels in quantized transformed imagery. The approach synthesizes correlation filters that operate directly on subband coefficients rather than on image data. We explore two techniques to accomplish the reduced-resolution recognition:(More)