This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence
March 2000 (vol. 22 no. 3)
pp. 241-251

Abstract—A framework for automatic landmark indentification is presented based on an algorithm for corresponding the boundaries of two shapes. The auto-landmarking framework employs a binary tree of corresponded pairs of shapes to generate landmarks automatically on each of a set of example shapes. The landmarks are used to train statistical shape models known as Point Distribution Models. The correspondence algorithm locates a matching pair of sparse polygonal approximations, one for each of a pair of boundaries by minimizing a cost function, using a greedy algorithm. The cost function expresses the dissimilarity in both the shape and representation error (with respect to the defining boundary) of the sparse polygons. Results are presented for three classes of shape which exhibit various types of nonrigid deformation.

[1] T.F. Cootes, D.H. Cooper, C.J. Taylor, and J. Graham, “A Trainable Method of Parametric Shape Description,” Image and Vision Computing, vol. 10, pp. 289-294, June 1992.
[2] F.L. Bookstein, Morphometric Tools for Landmark Data. Cambridge Univ. Press, 1991.
[3] C. Goodall, “Procrustes Methods in the Statistical Analysis of Shape,” J. Royal Statistical Soc. B, vol. 53, no. 2, pp. 285-339, 1991.
[4] T.F. Cootes, C.J. Taylor, D.H. Cooper, and J. Graham, "Active Shape Models—Their Training and Application," Computer Vision and Image Understanding, vol. 61, no. 1, pp. 38-59, Jan. 1995.
[5] A. Hill, T.F. Cootes, C.J. Taylor, and K. Lindley, “Medical Image Interpretation: A Generic Approach Using Deformable Templates,” J. Medical Informatics, vol. 19, no. 1, pp. 47-59, 1994.
[6] A. Lanitis, C.J. Taylor, and T.F. Cootes, "A Unified Approach to Coding and Interpreting Face Images," Int'l Conf. Computer Vision, 1995, pp. 368-373.
[7] J.A. Marchant and C.M. Onyango, “Fitting Grey Level Point Distribution Models to Animals in Scenes,” Image and Vision Computing, vol. 13, pp. 3-12, Feb. 1995.
[8] A. Baumberg and D. Hogg, “Learning Flexible Models from Image Sequences,” Computer Vision-ECCV '94, J.-O. Eklundh, ed., Springer-Verlag, vol. 1, pp. 299-308, 1994.
[9] A. Baumberg and D. Hogg, “An Adaptive Eigenshape Model,” Proc. Sixth British Machine Vison Conf., D. Pycock, ed., pp. 87-96, Sept. 1995.
[10] A. Hill and C.J. Taylor, “Automatic Landmark Generation for Point Distribution Models,” Proc. British Machine Vision Conf., pp. 429-438, 1994.
[11] S.C. Joshi, A. Banerjee, G.E. Christensen, J.G. Csernansky, J.W. Haller, M.I. Miller, and L. Wang, “Gaussian Random Fields on Sub-Manifolds for Characterizing Brain Surfaces,” Proc. 15th Conf. Information Processing in Medical Imaging, J. Duncan and G. Gindi, eds., pp. 381-386, 1997.
[12] G.E. Christensen, S.C. Joshi, and M. Miller, “Volumetric Transformation of Brain Anatomy,” IEEE Trans. Medical Image, vol. 16, pp. 864-877, 1997.
[13] M. Fleute and S. Lavallée, “Building a Complete Surface Model from Sparse Data Using Statistical Shape Models: Application to Computer Assisted Knee Surgery,” Medical Image Computing and Computer-Assisted Intervention, pp. 878-887, 1998.
[14] G. Szeliski and S. Lavalée, “Matching 3D Anatomical Surface with Non-Rigid Deformations Using Octree-Splines,” Int'l J. Computer Vision, vol. 18, no. 2, pp. 171-186, 1996.
[15] A. Kelemen, G. Székely, and G.G. Gerig, “Three-Dimensional Model-Based Segmentation,” Technical Report 178, Image Science Lab, ETH Zürich, 1997.
[16] C. Brechbühler, G. Gerig, and O. Kübler, “Parametrization of Closed Surfaces for 3D Shape Description,” Computer Vision and Image Understanding, vol. 61, no. 2, pp. 154-170, 1995.
[17] A.C.W. Kotcheff and C.J. Taylor, “Automatic Construction of Eigenshape Models by Direct Optimisation,” Medical Image Analysis, vol. 2, no. 4, pp. 303-314, 1998.
[18] P. Zhu and P.M. Chirlian, “On Critical Point Detection of Digital Shapes,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 737-748, Aug. 1995.
[19] J. Duncan, R.L. Owen, L.H. Staib, and P. Anandan, “Measurement of Non-Rigid Motion Using Contour Shape Descriptors,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 318-324, 1991.
[20] C. Kambhamettu and D.B. Goldgof, Point Correspondence Recovery in Non-Rigid Motion Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 222-227, 1992.
[21] I. Cohen, N. Ayache, and P. Sulger, “Tracking Points on Deformable Objects Using Curvature Information,” Proc. European Conf. Computer Vision, pp. 458-466, 1992.
[22] H.D. Tagare, D. O'Shea, and A. Rangarajan, “A Geometric Criterion for Shape-Based Non-Rigid Correspondence,” Proc. Fifth Int'l Conf. Computer Vision, pp. 434-439, June 1995.
[23] G.L. Scott and H.C. Longuet-Higgins, “An Algorithm for Associating the Features of Two Images,” Proc. Royal Statistical Soc. of London, vol. 244, pp. 21-26, 1991.
[24] L.S. Shapiro and J.M. Brady, “A Modal Approach to Feature-Based Correspondence,” Proc. Second British Machine Vison Conf., P. Mowforth, ed., pp. 78-85, Sept. 1991.
[25] S. Sclaroff and A.P. Pentland, Modal Matching for Correspondence and Recognition IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 6, pp. 545-561, 1995.
[26] A. Rangarajan, E. Mjolsness, S. Pappu, L. Davachi, P.S. Goldman-Rakic, and J.S. Duncan, “A Robust Point Matching Algorithm for Autoradiograph Alignment,” Visualisation in Biomedical Computing, pp. 277-286, 1996.
[27] A. Rangagajan, H. Chui, and F.L. Bookstein, “The Softassign Procrustes Matching Algorithm,” Proc. 15th Conf Information Processing in Medical Imaging, pp. 29-42, 1997.
[28] S. Umeyama, "Least-Squares Estimation of Transformation Parameters Between Two Point Patterns," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 4, pp. 376-380, Apr. 1991.
[29] B.K.P. Horn, “Closed-Form Solution of Absolute Orientation Using Orthonormal Matrices,” J. Optical Soc. Am., vol. 5, pp. 1,127-1,135, July 1988.

Index Terms:
Correspondence, critical points, polygonal approximation, automatic landmarks, flexible templates, point distribution models.
Citation:
Andrew Hill, Chris J. Taylor, Alan D. Brett, "A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 3, pp. 241-251, March 2000, doi:10.1109/34.841756
Usage of this product signifies your acceptance of the Terms of Use.