An object-based approach to automated image matching

An object-based algorithm for automated image matching is proposed. Working on the objects (closed edges) detected from images, the authors develop a new method for determination of region correspondence using combined criteria of moment invariant distance and chain code correlation. Each object is first represented by moment invariants and improved chain codes that are affine-invariant features describing the shape of the objects. Region matching is then implemented in feature space and sequentially in image space. In feature space, minimum distance classification is used to identify the most robust control points for initial image resampling. In image space, region-to-region correspondence is established by the root-mean-square-error rule. The technique developed has significant implications in an operational context