Topology-independent shape modeling scheme

Developing shape models is an important aspect of computer vision research. Geometric and differential properties of the surface can be computed from shape models. They also aid the tasks of object representation and recognition. In this paper we present an innovative new approach for shape modeling which, while retaining important features of the existing methods, overcomes most of their limitations. Our technique can be applied to model arbitrarily complex shapes, shapes with protrusions, and to situations where no a priori assumption about the object's topology can be made. A single instance of our model, when presented with an image having more than one object of interest, has the ability to split freely to represent each object. Our method is based on the level set ideas developed by Osher & Sethian to follow propagating solid/liquid interfaces with curvature-dependent speeds. The interface is a closed, nonintersecting, hypersurface flowing along its gradient field with constant speed or a speed that depends on the curvature. We move the interface by solving a `Hamilton-Jacobi' type equation written for a function in which the interface is a particular level set. A speed function synthesized from the image is used to stop the interface in the vicinity of the object boundaries. The resulting equations of motion are solved by numerical techniques borrowed from the technology of hyperbolic conservation laws. An added advantage of this scheme is that it can easily be extended to any number of space dimensions. The efficacy of the scheme is demonstrated with numerical experiments on synthesized images and noisy medical images.

[1]  R. Samadani Changes in connectivity in active contour models , 1989, [1989] Proceedings. Workshop on Visual Motion.

[2]  Ramin Samadani Adaptive snakes: control of damping and material parameters , 1991, Optics & Photonics.

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

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

[5]  Demetri Terzopoulos,et al.  The Computation of Visible-Surface Representations , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  L. Schumaker Fitting surfaces to scattered data , 1976 .

[7]  L. Evans,et al.  Motion of level sets by mean curvature. II , 1992 .

[8]  Baba C. Vemuri,et al.  Geometric Methods in Computer Vision , 1991 .

[9]  David Lee,et al.  One-Dimensional Regularization with Discontinuities , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Laurent D. Cohen,et al.  Deformable models for 3-D medical images using finite elements and balloons , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Demetri Terzopoulos,et al.  Regularization of Inverse Visual Problems Involving Discontinuities , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Richard Szeliski,et al.  Surface modeling with oriented particle systems , 1992, SIGGRAPH.

[13]  John R. Kender,et al.  Visual Surface Reconstruction Using Sparse Depth Data , 1986, CVPR 1986.

[14]  Baba C. Vemuri,et al.  Constructing Intrinsic Parameters with Active Models for Invariant Surface Reconstruction , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  J. Sethian Curvature and the evolution of fronts , 1985 .

[16]  Katsushi Ikeuchi,et al.  Shape representation and image segmentation using deformable surfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Yuan-Fang Wang,et al.  Surface Reconstruction Using Deformable Models with Interior and Boundary Constraints , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  R. Malladi,et al.  Deformable models: canonical parameters for surface representation and multiple view integration , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Baba C. Vemuri,et al.  Surface griding with intrinsic parameters , 1992, Pattern Recognit. Lett..

[20]  J. Sethian Numerical algorithms for propagating interfaces: Hamilton-Jacobi equations and conservation laws , 1990 .

[21]  Andrew Blake,et al.  Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.

[22]  Baba C. Vemuri,et al.  On Three-Dimensional Surface Reconstruction Methods , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..