Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we use inexact graph matching to detect changes between spatial features coming from different data sources, e.g. image derived information versus a GIS layer. Corresponding features in the data sources need to be matched taking into account outliers and spatial inaccuracy. We discuss the notion of consistency in inexact graph matching to be able to correctly determine the optimal solution of the matching problem. A condition based on the expected graph error is presented which allows to determine the bounds of error tolerance and in this way characterizes acceptable over inacceptable data inconsistencies.