Blended deformable models

We develop a new class of parameterized models based on the linear interpolation of two parameterized shapes using a blending function. Using a small number of additional parameters, blending extends the coverage of shape primitives. In particular, it offers the ability to construct shapes whose genus can change. Blended models are incorporated into a physics-based shape estimation framework which uses dynamic deformable models with global and local deformations. We present experiments involving the extraction of complex shapes, including an example of dynamic genus change.<<ETX>>

[1]  Alex Pentland,et al.  Closed-form solutions for physically-based shape modeling and recognition , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Richard Szeliski,et al.  Modeling surfaces of arbitrary topology with dynamic particles , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[3]  John E. Hopcroft,et al.  The Geometry of Projective Blending Surfaces , 1988, Artif. Intell..

[4]  Baba C. Vemuri,et al.  Multiresolution stochastic hybrid shape models with fractal priors , 1994, TOGS.

[5]  Claude Brezinski,et al.  Generative modeling for computer graphics and CAD , 1993 .

[6]  Gabriel Taubin,et al.  An improved algorithm for algebraic curve and surface fitting , 1993, 1993 (4th) International Conference on Computer Vision.

[7]  I. Biederman Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[8]  Alex Pentland,et al.  Perceptual Organization and the Representation of Natural Form , 1986, Artif. Intell..

[9]  Baba C. Vemuri,et al.  From global to local, a continuum of shape models with fractal priors , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Jan J. Koenderink,et al.  Solid shape , 1990 .

[11]  Barr,et al.  Faster Calculation of Superquadric Shapes , 1981, IEEE Computer Graphics and Applications.

[12]  Dimitris N. Metaxas,et al.  Dynamic 3D models with local and global deformations: deformable superquadrics , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[13]  Alok Gupta,et al.  The extruded generalized cylinder: a deformable model for object recovery , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Dimitris N. Metaxas,et al.  Physics-based modelling of nonrigid objects for vision and graphics , 1993 .

[15]  Ruzena Bajcsy,et al.  Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  D. DeCarlo,et al.  Blended Deformable Models , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Shigeru Muraki,et al.  Volumetric shape description of range data using “Blobby Model” , 1991, SIGGRAPH.

[18]  Barr,et al.  Superquadrics and Angle-Preserving Transformations , 1981, IEEE Computer Graphics and Applications.

[19]  Dimitris N. Metaxas,et al.  Dynamic deformation of solid primitives with constraints , 1992, SIGGRAPH.

[20]  Dimitris N. Metaxas,et al.  Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Demetri Terzopoulos,et al.  Constraints on Deformable Models: Recovering 3D Shape and Nonrigid Motion , 1988, Artif. Intell..

[22]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  D. Marr,et al.  Representation and recognition of the spatial organization of three-dimensional shapes , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[24]  Gerald Farin,et al.  Curves and surfaces for computer aided geometric design , 1990 .

[25]  Andrew J. Hanson,et al.  Hyperquadrics: Smoothly deformable shapes with convex polyhedral bounds , 1988, Comput. Vis. Graph. Image Process..