Infrared imagers used to acquire data for automatic target recognition are inherently limited by the physical properties of their components. Fortunately, image super-resolution techniques can be applied to overcome the limits of these imaging systems. This increase in resolution can have potentially dramatic consequences for improved automatic target recognition (ATR) on the resultant higher-resolution images. We will discuss superresolution techniques in general and specifically review the details of one such algorithm from the literature suited to real-time application on forward-looking infrared (FLIR) images. Following this tutorial, a numerical analysis of the algorithm applied to synthetic IR data will be presented, and we will conclude by discussing the implications of the analysis for improved ATR accuracy.
Selection of the kernel parameters is critical to the performance of Support Vector Machines (SVMs), directly impacting the generalization and classification efficacy of the SVM. An automated procedure for parameter selection is clearly desirable given the intractable problem of exhaustive search methods. The authors' previous work in this area involved analyzing the SVM training data margin distributions for a Gaussian kernel in order to guide the kernel parameter selection process. The approach entailed several iterations of training the SVM in order to minimize the number of support vectors. Our continued investigation of unsupervised kernel parameter selection has led to a scheme employing selection of the parameters before training occurs. Statistical methods are applied to the Gram matrix to determine kernel optimization in an unsupervised fashion. This preprocessing framework removes the requirement for iterative SVM training. Empirical results will be presented for the "toy" checkerboard and quadboard problems.
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