Abstract
Structural errors which arise due to poor image segmentation or clutter pose one of the main obstacles to effective relational graph matching. The aim of this paper is to provide a comparative evaluation of a number of contrasting approaches to the control of structural errors. Unique to this study is the way in which we show how a diverse family of algorithms relate to one-another using a common Bayesian framework. According to our adopted Bayesian approach relational consistency is gauged by Hamming distance. We illustrate three different ways in which this consistency measure may be used to rectify structural errors. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter.
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© 1996 Springer-Verlag Berlin Heidelberg
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Hancock, E.R., Wilson, R.C. (1996). Rectifying structural matching errors. In: Li, S.Z., Mital, D.P., Teoh, E.K., Wang, H. (eds) Recent Developments in Computer Vision. ACCV 1995. Lecture Notes in Computer Science, vol 1035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60793-5_89
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DOI: https://doi.org/10.1007/3-540-60793-5_89
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