PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Discrete minimum ratio curves and surfaces
Fred Nicolls and Philip Torr
In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 13-18 June 2010, San Francisco, CA, USA.


Graph cuts have proven useful for image segmentation and for volumetric reconstruction in multiple view stereo. However, solutions are biased: the cost function tends to favour either a short boundary (in 2D) or a boundary with a small area (in 3D). This bias can be avoided by instead minimising the cut ratio, which normalises the cost by a measure of the boundary size. This paper uses ideas from discrete differential geometry to develop a linear programming formulation for finding a minimum ratio cut in arbitrary dimension, which allows constraints on the solution to be specified in a natural manner, and which admits an efficient and globally optimal solution. Results are shown for 2D segmentation and for 3D volumetric reconstruction.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:6966
Deposited By:Karteek Alahari
Deposited On:25 June 2010