Prediction of object detection, recognition, and identification [DRI] ranges at color scene images based on quantifying human color contrast perception

Abstract

We propose a novel approach to predict, for specified color imaging system and for objects with known characteristics, their detection, recognition, identification (DRI) ranges in a colored dynamic scene, based on quantifying the human color contrast perception. The method refers to the well established L*a*b*, 3D color space. The nonlinear relations of this space are intended to mimic the nonlinear response of the human eye. The metrics of L*a*b* color space is such that the Euclidian distance between any two colors in this space is approximately proportional to the color contrast as perceived by the human eye/brain. The result of this metrics leads to the outcome that color contrast of any two points is always greater (or equal) than their equivalent grey scale contrast. This meets our sense that looking on a colored image, contrast is superior to the gray scale contrast of the same image. Yet, color loss by scattering at very long ranges should be considered as well. The color contrast derived from the distance between the colored object pixels and to the nearby colored background pixels, as derived from the L*a*b* color space metrics, is expressed in terms of gray scale contrast. This contrast replaces the original standard gray scale contrast component of that image. As expected, the resulted DRI ranges are, in most cases, larger than those predicted by the standard gray scale image. Upon further elaboration and validation of this method, it may be combined with the next versions of the well accepted TRM codes for DRI predictions. Consistent prediction of DRI ranges implies a careful evaluation of the object and background color contrast reduction along the range. Clearly, additional processing for reconstructing the objects and background true colors and hence the color contrast along the range, will further increase the DRI ranges.