Abstract

In many image processing applications, it is desirable to combine partially overlapping images into one larger scene. We introduce a supervised correlation filter method that automatically detects overlapping image pairs and estimates proper alignment. We use on-line composite filter design to achieve rotation- and overlap-invariant image pairing. We present stochastic system analysis that provides closed-form expressions that predict correlation plane response distribution, defines an overlap-invariant filter bank architecture, and obtains the optimum training set size. The resulting high-level image processing method is numerically efficient, suited for optoelectronic implementation, and competitive with manual reconstruction in speed and accuracy.

© 1999 Optical Society of America

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