Distortion-invariant kernel filters for general pattern recognition

We note several key general pattern recognition (GPR) issues that have been ignored in all prior distortion-invariant kernel filter (kernel DEF) work. These include: the unrealistic assumption of centered test data, the lack of a fast FFT-based on-line implementation, the significantly larger storage and on-line computation requirements, incorrect formulation of the kernel filter in the FT domain, incorrect formulation of prior image-domain kernel SDF and Mace filters, and the unrealistic use of test set data for parameter selection. We present several improvements to prior kernel DIF work. Our primary objective is to examine the viability of kernel DIFs for GPR and automatic target recognition (ATR) applications (where the location of the object in the test input is not known). Thus, in this paper, we apply our improved kernel DIFs to CAD ATR data. We address range and full 360 degrees aspect view variations; we also address rejection of unseen confuser objects and clutter. We use training and validation set data (not test set data) to select the kernel parameter. We show that kernel filters (higher-order features) can improve classification and confuser rejection performance. We consider only kernel SDF filters, since their on-line computation requirements are reasonable; we present test results for both polynomial and Gaussian kernels. The main purposes of this paper are to: note issues of importance ignored in all prior kernel DIF work, detail how to properly perform energy minimization in kernel DIFs, show that kernel SDF filters can correct errors for ATR data, and compare the performance of kernel SDF filters and standard Minace DIFs. We also introduce our new Minace-preprocessed kernel SDF filter.