Quadratic programming vs. concurrent correlation matching under non-uniform image contrast and offset

Recognition of objects-of-interest by similarity of prototypes (templates) to sensed images of natural scenes is important in computer vision, digital photogrammetry and multimedia data retrieval. However, accurate template matching in the presence of non-uniform nuisance contrast and offset deviations remains a challenging problem. Uniform deviations leading to cross-correlation matching scores have been studied for decades, but non-uniform ones have been little explored - mostly, only computationally intensive gradient-based matching for low order polynomial deviation model. This paper presents two computationally simpler alternative approaches: (i) an analytical least-squares template matching for polynomial deviation models of an arbitrary order using a bank of concurrent cross-correlation scores and (ii) a fast numerical quadratic programming based matching for a new general model of loosely constrained non-uniform contrast and offset deviations. Experiments confirm that these approaches hold great promise in practice.