A Map Mosaicking Method Using Opportunistic Search Approach with a Blackboard Structure
Map mosaicking is to integrate two or more map images having a coincident area by computing the rotational angle, the vertical and horizontal distances a map image has to move to overlap the coincident area. A solution of the problem is represented as a point in the parameter space with three axes: one for the rotational angle and the others for the vertical and horizontal distances. We extract local features from each map image, match them to make feature pairs, and project the feature pairs onto the parameter space. Traditional approaches using parameter spaces have suffered from a huge search space and computing time, for they project all the feature pairs onto the parameter space and search solutions by iterative optimization methods. We propose a new method that can give a solution not projecting all the feature pairs onto the parameter space but search opportunistically in a Blackboard structure.
KeywordsParameter Space Knowledge Source Translation Surface Feature Pair Certainty Factor
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