Using grey-level models to improve active shape model search

We describe methods for using flexible models to locate structures in images. We have previously described statistical models of shape and shape variability which can be used for this purpose (active shape models). In this paper we show how statistical models of grey-level appearance can be incorporated, leading to improved reliability and accuracy. We describe experiments designed to: 1) test how well an active shape model can locate an object in a new image; 2) to assess the effects on performance of varying the model parameters; and 3) to compare the results using grey-level models with those using a search for strongest edges. The results demonstrate that the addition of grey-level models leads to considerable improvement over earlier schemes.

[1]  James S. Duncan,et al.  Parametrically deformable contour models , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Alex Pentland,et al.  Closed-Form Solutions for Physically Based Shape Modeling and Recognition , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  C. Taylor,et al.  Active shape models - 'Smart Snakes'. , 1992 .

[4]  Timothy F. Cootes,et al.  Training Models of Shape from Sets of Examples , 1992, BMVC.

[5]  Nicholas Ayache,et al.  Non-Rigid Motion Analysis in Medical Images: a Physically Based Approach , 1993, IPMI.

[6]  Timothy F. Cootes,et al.  The Use of Active Shape Models for Locating Structures in Medical Images , 1993, IPMI.

[7]  Timothy F. Cootes,et al.  Multi-resolution search with active shape models , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[8]  Timothy F. Cootes,et al.  Use of active shape models for locating structures in medical images , 1994, Image Vis. Comput..

[9]  Michael I. Miller,et al.  REPRESENTATIONS OF KNOWLEDGE IN COMPLEX SYSTEMS , 1994 .