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This paper presents two new methods, the Joint Moment Method (JMM) and the Window Variance Method (WVM), for image matching and 3-D object surface reconstruction using multiple perspective views. The viewing positions and orientations for these perspective views are known a priori, as is usually the case for such applications as robotics and industrial vision as well as close range photogrammetry. Like the conventional two-frame correlation method, the JMM and WVM require finding the extrema of 1-D curves, which are proved to theoretically approach a delta function exponentially as the number of frames increases for the JMM and are much sharper than the two-frame correlation function for both the JMM and the WVM, even when the image point to be matched cannot be easily distinguished from some of the other points. The theoretical findings have been supported by simulations. It is also proved that JMM and WVM are not sensitive to certain radiometric effects. If the same window size is used, the computational complexity for the proposed methods is about n - 1 times that for the two-frame method where n is the number of frames. Simulation results show that the JMM and WVM require smaller windows than the two-frame correlation method with better accuracy, and therefore may even be more computationally feasible than the latter since the computational complexity increases quadratically as a function of the window size.
Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:PAMI-5 , Issue: 2 )
Date of Publication: March 1983