Kernel synthetic discriminant function (SDF) filters for fast object recognition

Abstract

In most object recognition applications, the object is present with different distortions (e.g. aspect view and scale) and its location is unknown. Our objective is to develop higher-order classifiers that can be applied (efficiently and fast) for different locations of the object over the test input. A type of classifier, the distortion-invariant filter (DIF), is attractive for fast object recognition, since it can be applied for different shifts using the fast Fourier transform (FFT); a single DIF handles different object distortions, e.g. all aspect views and some range of scale. In our prior work (Proc. SPIE 7252-02), we combined DIFs and the kernel technique to form higher-order "kernel DIFs." In this paper, we present new test results with these kernel DIFs; we emphasize kernel versions of the synthetic discriminant function (SDF) filter, since we recall that they are the most efficient to use. We include new insight into the difference between vector-based and pixel-based kernels. We also present more test results with our recently introduced (Proc. SPIE 6977-03) combination of minimum noise and correlation energy (MINACE) filter preprocessing and kernel SDF filters (these form "preprocessed kernel SDF filters"); in our new work, we consider whether automated selection of the Minace-preprocessing parameter improves filter performance. We consider the classification of different pairs of true-class CAD (computer-aided design) infrared (IR) objects and the rejection of unseen problematic (blob) real IR clutter and unseen confuser-class CAD IR objects with full 360° aspect-view distortions and with different ranges of scale distortions present. We present new test results with more and different confuser-class objects and for both polynomial and Gaussian kernel SDF filters. We also include new test results at farther ranges than before; these are emphasized in this paper.