A statistical theory for optimal detection of moving objects in variable corruptive noise

In this paper, the classical analysis of variance is extended to three-dimensional (3-D) Græco-Latin squares design for multiframe processing applications. Conspicuous physical features, including edges, lines, and corners, can then be expressed as contrast functions. This enables the development of a new methodology for detecting moving objects embedded in noise. The new detector exploits spatial and temporal information uniformly most powerful in a Gaussian environment with unknown and time-varying noise variance. Also found is that a moving object detector based on contrast functions coincides with a sufficient statistic of the generalized likelihood ratio test. Extensive image analysis demonstrates the practicality of the detector and compares favorably to other classes of detectors.