Graphical Templates for Model Registration

A new method of model registration is proposed using graphical templates. A graph of landmarks is chosen in the template image. All possible candidates for these landmarks are found in the data image using local operators. A dynamic programming algorithm on decomposable subgraphs of the template graph finds the optimal match to a subset of the candidate points in polynomial time. This combination of local operators to describe points of interest/landmarks and a graph to describe their geometric orientation in the plane, yields fast and precise matches of the model to the data, with no initialization required.

[1]  Ulf Grenander,et al.  A Unified Approach to Pattern Analysis , 1970, Adv. Comput..

[2]  Umberto Bertelè,et al.  Nonserial Dynamic Programming , 1972 .

[3]  Claude Berge,et al.  Graphs and Hypergraphs , 2021, Clustering.

[4]  Robert E. Tarjan,et al.  Algorithmic Aspects of Vertex Elimination on Graphs , 1976, SIAM J. Comput..

[5]  J. Meinguet Multivariate interpolation at arbitrary points made simple , 1979 .

[6]  T. Speed,et al.  Markov Fields and Log-Linear Interaction Models for Contingency Tables , 1980 .

[7]  Thomas S. Huang,et al.  Image Sequence Analysis: Motion Estimation , 1981 .

[8]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[9]  Hans-Hellmut Nagel,et al.  Displacement vectors derived from second-order intensity variations in image sequences , 1983, Comput. Vis. Graph. Image Process..

[10]  Takeo Kanade,et al.  Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Hideo Ogawa Labeled point pattern matching by Delaunay triangulation and maximal cliques , 1986, Pattern Recognit..

[12]  Demetri Terzopoulos,et al.  Image Analysis Using Multigrid Relaxation Methods , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  F. Bookstein Size and Shape Spaces for Landmark Data in Two Dimensions , 1986 .

[14]  F. Bookstein Toward a Notion of Feature Extraction for Plane Mappings , 1988 .

[15]  Ruzena Bajcsy,et al.  Multiresolution elastic matching , 1989, Comput. Vis. Graph. Image Process..

[16]  U. Grenander,et al.  Structural Image Restoration through Deformable Templates , 1991 .

[17]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[18]  Timothy F. Cootes,et al.  Active Shape Models - 'smart snakes' , 1992, BMVC.

[19]  Jonathan L. Elion,et al.  A new method for structure recognition in unsubtracted digital angiograms , 1992, Proceedings Computers in Cardiology.

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

[21]  M I Miller,et al.  Mathematical textbook of deformable neuroanatomies. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Yali Amit,et al.  A Nonlinear Variational Problem for Image Matching , 1994, SIAM J. Sci. Comput..

[23]  Emanuele Trucco,et al.  Computer and Robot Vision , 1995 .

[24]  David B. Cooper,et al.  Automatic Finding of Main Roads in Aerial Images by Using Geometric-Stochastic Models and Estimation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  H. Damasio,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence: Special Issue on Perceptual Organization in Computer Vision , 1998 .

[26]  F. Bookstein,et al.  Morphometric Tools for Landmark Data: Geometry and Biology , 1999 .