An Information Theoretic Approach to Statistical Shape Modelling

Statistical shape models have been used widely as a basis for segmenting and interpreting images. A major drawback of the approach is the need to establish a set of dense correspondences across a training set of segmented shapes. By posing the problem as one of minimising the description length of the model, we develop an efficient method that automatically defines correspondences across a set of shapes. Results are given for several different training sets of shapes, showing that the automatic method constructs significantly better models than those built by hand - the current gold standard.

• Type: Conference
• Year: 2001
• Where: BMVC
• Authors: Rhodri H. Davies, Timothy F. Cootes, Carole J. Twining, Christopher J. Taylor
• Keywords: BMVC 2001, Computer Vision, Segmented Shapes, Statistical Shape Models, Training Sets