Proceedings PaperReliable Object Acquisition Via Clustering Of Ambiguously Matching Features
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Features which are easily extracted from an image are often at too low a level to be unambiguously matched to features of a model. However, if an elementary feature ei has some structure, only a limited number of transformations Tij can match it to similar model features mj. By extracting a set of features ei,i=l,...,n the transformation parameter space can be populated with a number of potential transformations Tij, i=1,...,n ; j=1,...,k. Clustering in this parameter space derives a transformation T that is supported by a large amount of local matching evidence. Simple clustering techniques are described for handling combined rotation and translation. Results are reported using the clustering technique with edge features and circular neighborhood features to acquire 2D objects.