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Registration of maps and airborne or satellite images is an important problem for tasks such as map updating and change detection. This is a difficult problem because map features such as roads and buildings may be mislocated and features extracted from images may not correspond to map features, which is the challenge for the traditional feature-based registration algorithm. Our algorithm obtains a general global registration of maps and images by applying statistical techniques to map and image features. Finer analysis can then be used to find changes and local mismatches. The Maximization of Mutual Information (MMI) technique has proven to be very robust in image-to-image registration. This paper extends the MMI technique to the map-to-image registration problem through a focus-of-attention mechanism that forces MMI to utilize correspondences that have a high probability of being information rich and evaluates the accuracy and robustness in series experiments. The advantages and limitations are discussed in detail. The experimental results demonstrate map and image registration.