Abstract
A method for building flexible shape models is presented in which a shape is represented by a set of labelled points. The technique determines the statistics of the points over a collection of example shapes. The mean positions of the points give an average shape and a number of modes of variation are determined describing the main ways in which the example shapes tend to deform from the average. In this way allowed variation in shape can be included in the model. The method produces a compact flexible ‘Point Distribution Model’ with a small number of linearly independent parameters, which can be used during image search. We demonstrate the application of the Point Distribution Model in describing two classes of shapes.
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References
R. Chin and C.R. Dyer, Model-Based Recognition in Robot Vision. Computing Surveys 1986; Vol 18, No 1
W.E.L. Grimson, Object Recognition by Computer : The Role of Geometric Constraints, The MIT Press, Cambridge, MA, USA, 1990.
AX. Yuille, D.S. Cohen and P. Hallinan, Feature extraction from faces using deformable templates, Proc. Computer Vision and Pattern Recognition (1989) pp104–109.
P. Iipson, A.L. Yuille, D. O’Keeffe, J. Cavanaugh, J. Taaffe and D. Rosenthal, Deformable Templates for Feature Extraction from Medical Images, Proceedings of the First European Conference on Computer Vision (Lecture Notes in Computer Science, ed. O. Faugeras, pub. Springer-Verlag) 1990 pp413–417.
M. Kass, A. Witkin and D. Terzopoulos, Snakes: Active Contour Models. First International Conference on Computer Vision, pub. IEEE Computer Society Press, 1987, pp 259–268.
L.H. Staib and J.S. Duncan, Parametrically Deformable Contour Models. IEEE Computer Society conference on Computer Vision and Pattern Recognition, San Diego, 1989
D. Terzopoulos and D. Metaxas, Dynamic 3D Models with Local and Global Deformations : Deformable Superquadrics. IEEE Trans, on Pattern Analysis and Machine Intelligence 1991; Vol.13 No.7 pp703–714.
A. Pentland and S, Sclaroff, Closed-Form Solutions for Physically Based Modelling and Recognition. IEEE Trans, on Pattern Analysis and Machine Intelligence 1991; Vol.13 No.7 pp703–714.
F.L. Bookstein, Morphometric Tools for Landmark Data. Cambridge University Press, 1991.
K.V. Mardia, J.T. Kent and AN. Walder, Statistical Shape Models in Image Analysis. Proceedings of the 23rd Symposium on the Interface, Seattle 1991, pp 550–557.
J.C. Gower, Generalized Procrustes Analysis. Psychometrika. 40, 1975, 33–51.
T.F. Cootes, D. Cooper, C.J. Taylor and J. Graham, ATrainable Method of Parametric Shape Description. Proc. BMVC 1991 pub. Springer-Verlag, pp54–61.
K. Fukunaga and W.L.G. Koontz, Application of the Karhunen-Loeve Expansion to Feature Selection and Ordering. IEEE Trans. on Computers 1970; 4.
J. Graham, T.F. Cootes, D.Cooper and C.J. Taylor, VISAGE Progress Report -Deliverable D4, Wolfson Image Analysis Unit, Manchester University 1992.
T.F. Cootes and C.J. Taylor, Active Shape Models -‘Smart Snakes’. This Volume.
A. Hill, T.F. Cootes and C J. Taylor, A Generic System for Image Interpretation Using Flexible Templates. This Volume.
A. Lanitis, Optical Character Recognition of Hand-written Characters using Flexible Templates. Internal Report, Wolfson Image Analysis Unit, Manchester University 1992.
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© 1992 Springer-Verlag London Limited
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Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J. (1992). Training Models of Shape from Sets of Examples. In: Hogg, D., Boyle, R. (eds) BMVC92. Springer, London. https://doi.org/10.1007/978-1-4471-3201-1_2
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DOI: https://doi.org/10.1007/978-1-4471-3201-1_2
Publisher Name: Springer, London
Print ISBN: 978-3-540-19777-5
Online ISBN: 978-1-4471-3201-1
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