A space variant Maximum Average Correlation Height (MACH) filter for object recognition in real time thermal images for security applications

We propose a space variant Maximum Average Correlation Height (MACH) filter which can be locally modified depending upon its position in the input frame. This can be used to detect targets in an environment from varying ranges and in unpredictable weather conditions using thermal images. It enables adaptation of the filter dependant on background heat signature variances and also enables the normalization of the filter energy levels. The kernel can be normalized to remove a non-uniform brightness distribution if this occurs in different regions of the image. The main constraint in this implementation is the dependence on computational ability of the system. This can be minimized with the recent advances in optical correlators using scanning holographic memory, as proposed by Birch et al. [1] In this paper we describe the discrimination abilities of the MACH filter against background heat signature variances and tolerance to changes in scale and calculate the improvement in detection capabilities with the introduction of a nonlinearity. We propose a security detection system which exhibits a joint process where human and an automated pattern recognition system contribute to the overall solution for the detection of pre-defined targets. © 2010 SPIE.