Image binarization techniques for correlation-based pattern recognition

Correlation using binary images is suited to efficient digital realization or convenient optical implementation. Binarization algorithms are required in order to match grayscale imagery to these binary correlation architectures. We present several novel point-wise and block-wise binarization techniques all of which outperform the grayscale matched filter for large values of input signal-to-noise ratio (SNR = 0 dB). We discuss direct binarization methods based on global thresholds, local thresholds, histogram equalization, edge-enhancement, and statistical binarization, as well as indirect methods based on auto- and cross-correlation techniques. These point-wise methods are shown to offer poor noise tolerance and a new block-wise binarization method is introduced to enhance recognition at low values of SNR. This block-wise technique is motivated by vector quantization-based image compression and offers performance superior to the grayscale matched filter for an input SNR as low as -12 dB.