On the Representation of Shapes Using Implicit Functions

In this chapter, we explore shape representation, registration, and modeling through implicit functions. To this end, we propose novel techniques for global and local registration of shapes through the alignment of the corresponding distance transforms by defining objective functions that minimize metrics between the implicit representations of shapes.

[1]  Haifeng Chen,et al.  Robust Computer Vision through Kernel Density Estimation , 2002, ECCV.

[2]  A. Yezzi,et al.  A variational framework for joint segmentation and registration , 2001, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001).

[3]  Philip N. Klein,et al.  Alignment-Based Recognition of Shape Outlines , 2001, IWVF.

[4]  Nikos Paragios,et al.  Shape Priors for Level Set Representations , 2002, ECCV.

[5]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[6]  Petros Faloutsos,et al.  Dynamic Free-Form Deformations for Animation Synthesis , 1997, IEEE Trans. Vis. Comput. Graph..

[7]  Baba C. Vemuri,et al.  Front Propagation: A Framework for Topology Independent Shape Modeling and Recovery , 1994 .

[8]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[9]  Nikos Paragios,et al.  Motion-based background subtraction using adaptive kernel density estimation , 2004, CVPR 2004.

[10]  Thomas W. Sederberg,et al.  Free-form deformation of solid geometric models , 1986, SIGGRAPH.

[11]  Yunmei Chen,et al.  On the incorporation of shape priors into geometric active contours , 2001, Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision.

[12]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Dimitris N. Metaxas,et al.  A Hierarchical Framework For High Resolution Facial Expression Tracking , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[14]  W. Eric L. Grimson,et al.  Model-based curve evolution technique for image segmentation , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[15]  Nicholas Ayache,et al.  Rigid, affine and locally affine registration of free-form surfaces , 1996, International Journal of Computer Vision.

[16]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[17]  Matthew P. Wand,et al.  Kernel Smoothing , 1995 .

[18]  Dimitris N. Metaxas Physics-Based Deformable Models , 1996 .

[19]  Eero P. Simoncelli Bayesian multi-scale differential optical flow , 1999 .

[20]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[21]  A. Dervieux,et al.  Multifluid incompressible flows by a finite element method , 1981 .

[22]  R. Deriche,et al.  A variational framework for active and adaptative segmentation of vector valued images , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[23]  J. A. Sethian,et al.  Fast Marching Methods , 1999, SIAM Rev..

[24]  Nikos Paragios,et al.  Establishing Local Correspondences towards Compact Representations of Anatomical Structures , 2003, MICCAI.

[25]  Nikos Paragios,et al.  Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration , 2002, ECCV.

[26]  S. Lippman,et al.  The Scripps Institution of Oceanography , 1959, Nature.

[27]  Nikos Paragios,et al.  Registration of Structures in Arbitrary Dimensions: Implicit Representations, Mutual Information & Free form Deformations , 2003 .

[28]  Remco C. Veltkamp,et al.  State of the Art in Shape Matching , 2001, Principles of Visual Information Retrieval.

[29]  S. Osher,et al.  Geometric Level Set Methods in Imaging, Vision, and Graphics , 2011, Springer New York.

[30]  Gunilla Borgefors,et al.  Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Timothy F. Cootes,et al.  3D Statistical Shape Models Using Direct Optimisation of Description Length , 2002, ECCV.

[32]  PaperNo Recognition of shapes by editing shock graphs , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[33]  Rachid Deriche,et al.  Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Charles V. Stewart,et al.  A Feature-Based, Robust, Hierarchical Algorithm for Registering Pairs of Images of the Curved Human Retina , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  O. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2002, 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002..

[36]  Adrian Hilton,et al.  Estimating pose uncertainty for surface registration , 1998, Image Vis. Comput..

[37]  M. Jolly,et al.  Introducing error estimation in the shape learning framework : Spline based registration and non-parametric density estimator in the space of higher order polynomials , 2005 .

[38]  Stefano Soatto,et al.  A Pseudo-distance for Shape Priors in Level Set Segmentation , 2003 .

[39]  James S. Duncan,et al.  Boundary Finding with Parametrically Deformable Models , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  A. Dervieux,et al.  A finite element method for the simulation of a Rayleigh-Taylor instability , 1980 .

[41]  Stanley Osher,et al.  Level Set Methods , 2003 .

[42]  Rachid Deriche,et al.  Implicit Active Shape Models for 3D Segmentation in MR Imaging , 2004, MICCAI.

[43]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1995, Proceedings of IEEE International Conference on Computer Vision.

[44]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[45]  David R. Forsey,et al.  Hierarchical B-spline refinement , 1988, SIGGRAPH.

[46]  Rachid Deriche,et al.  Geodesic active regions for supervised texture segmentation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[47]  O. Faugeras,et al.  A variational approach to multi-modal image matching , 2001, Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision.

[48]  Guillermo Sapiro,et al.  Minimal Surfaces Based Object Segmentation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[49]  Gerald Farin,et al.  Deformation with hierarchical B-splines , 2001 .

[50]  Anand Rangarajan,et al.  A new algorithm for non-rigid point matching , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[51]  Nikos Paragios,et al.  Non-rigid registration using distance functions , 2003, Comput. Vis. Image Underst..

[52]  T. Chan,et al.  A Variational Level Set Approach to Multiphase Motion , 1996 .

[53]  A Collignon,et al.  Automated multimodality image registration using information theory , 1995 .

[54]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[55]  Xavier Pennec,et al.  A Framework for Uncertainty and Validation of 3-D Registration Methods Based on Points and Frames , 2004, International Journal of Computer Vision.

[56]  Kenichi Kanatani,et al.  Uncertainty modeling and model selection for geometric inference , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[57]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[58]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[59]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[60]  S. Osher,et al.  A level set approach for computing solutions to incompressible two-phase flow , 1994 .

[61]  Chia-Ling Tsai,et al.  The dual-bootstrap iterative closest point algorithm with application to retinal image registration , 2003, IEEE Transactions on Medical Imaging.