Computational Efficiency Of Multiple Image Registration Algorithms

Abstract

The problem of multiple image registration is that of finding n subimages in a larger image (Search area S) which best match the n smaller images (windows or references) obtained from different sensors, assuming that all smaller images are completely located within the larger image. Although correlation and sequential similarity detection algorithms are commonly used, the method of moments which has been successfully used for automatic classification of an unknown pattern as one of several known patterns can also be used for digital image registration. This paper compares the computational efficiency of the three methods stated above for software as well as hardware implementations. For single image registration problem, the moments method requires more computation time than correlation and sequential similarity detection methods. However, for the multiple image registration problem the moments method becomes computationally more efficient as the number of windows increases. This paper shows that the moments method requires less computation time if implemented in software and less hardware for real time implementation when the number of windows is large.© (1980) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.