Abstract
Deformable models in the “physically-based” paradigm are almost always formulated in an ad-hoc fashion, not related to physical reality — they apply the equations on physics in a fantasy world. This paper discusses some of the drawbacks of this approach. Still these techniques have shown themselves to be useful, so there must be something here. This paper reinterprets these “physics-based” techniques by putting them into a framework of robust statistics. We use this framework to analyze the problems and ad-hoc solutions found in common physically-based formulations. These include incorrect prior shape models; bad relative weights of various energies; and the two-stage approach to minimization (adjusting global, then local shape parameters). We examine the statistical implications of common deformable object formulations. In our reformulation, the units are meaningful, training data plays a fundamental role, different kinds of information may be fused, and certainties can be reported for the segmentation results. The robust aspects of the reformulation are necessary to combat interference from the necessarily large amount of unmodeled image information.
This work supported in part by ARPA Contract DACA-76-92-C-007 and by NSF PYI award #IRI-90-57951, with industrial support from Siemens, and Texas Instruments.
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Boult, T.E., Fenster, S.D., O'Donnell, T. (1995). Physics in a fantasy world vs robust statistical estimation. In: Hebert, M., Ponce, J., Boult, T., Gross, A. (eds) Object Representation in Computer Vision. ORCV 1994. Lecture Notes in Computer Science, vol 994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60477-4_20
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DOI: https://doi.org/10.1007/3-540-60477-4_20
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