<title>Fuzzy-logic-based sensor fusion of images</title>

Abstract

The fusion of visual and infrared sensor images of potential driving hazards in static infrared and visual scenes is computed using the Fuzzy Logic Approach (FLA). The FLA is presented as a new method for combining images from different sensors for achieving an image that displays more information than either image separately. Fuzzy logic is a modeling approach that encodes expert knowledge directly and easily using rules. With the help of membership functions designed for the data set under study, the FLA can model and interpolate to enhance the contrast of the imagery. The Mamdani model is used to combine the images. The fused sensor images are compared to metrics to measure the increased perception of a driving hazard in the sensor-fused image. The metrics are correlated to experimental ranking of the image quality. A data set containing IR and visual images of driving hazards under different types of atmospheric contrast conditions is fused using the Fuzzy Logic Approach (FLA). A holographic matched-filter method (HMFM) is used to scan some of the more difficult images for automated detection. The image rankings are obtained by presenting imagery in the TARDEC Visual Perception Lab (VPL) to subjects. Probability of detection of a driving hazard is computed using data obtained in observer tests. The matched-filter is implemented for driving hazard recognition with a spatial filter designed to emulate holographic methods. One of the possible automatic target recognition devices implements digital/optical cross-correlator that would process sensor-fused images of targets. Such a device may be useful for enhanced automotive vision or military signature recognition of camouflaged vehicles. A textured clutter metric is compared to experimental rankings.