On the intrinsic reconstruction of shape from its symmetries

We address the issue of the use of symmetry-based representations, such as the medial axis and an augmented form of it, the shock structure, to regenerate shapes. First, we address pointwise reconstruction of the boundary from points of the medial axis. As classified into three generic types (A/sup 2//1 mid-branch, A/sub 3/ end point of a branch, and A/sub 1//sup 3/ junction). Second, we examine the intrinsic reconstruction of shape when differential properties of the axis are also available. We show the surprising result that the tangent and curvature of the medial axis, coupled with the speed and acceleration of the shock flowing along the's axis, i.e., first and second order properties, are sufficient to determine the boundary tangents and curvatures at corresponding points of the boundary. This implies that for a rather coarse sampling of the symmetry axis, the location together with its tangent, curvature: speed, and acceleration is sufficient to accurately regenerate a local neighborhood of shape at this point. Together with reconstruction properties at junction (A/sup 3//sub 1/) and end points (A/sub 3/), these results lead to the full intrinsic regeneration of a shape from a representation of it as a directed planar graph (where the links represent curvature and acceleration functions, and where the nodes contain tangent and speed information): a representation ideally suited for the design and manipulation of free-form shape.

[1]  S. Zucker,et al.  On the psychophysics of the shape triangle , 2001, Vision Research.

[2]  Benjamin B. Kimia,et al.  Shock-based approach for indexing of image databases using shape , 1997, Other Conferences.

[3]  Benjamin B. Kimia,et al.  On the evolution of curves via a function of curvature , 1992 .

[4]  Kaleem Siddiqi,et al.  Matching Hierarchical Structures Using Association Graphs , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Paul A. Yushkevich,et al.  Segmentation, registration, and measurement of shape variation via image object shape , 1999, IEEE Transactions on Medical Imaging.

[6]  Daniel Thalmann,et al.  Fast realistic human body deformations for animation and VR applications , 1996, Proceedings of CG International '96.

[7]  Philip N. Klein,et al.  Recognition of Shapes by Editing Shock Graphs , 2001, ICCV.

[8]  Pascal Fua,et al.  Tracking and Modeling People in Video Sequences , 2001, Comput. Vis. Image Underst..

[9]  Daniel Thalmann,et al.  Interactive Shape Design Using Metaballs and Splines , 1995 .

[10]  H. Blum Biological shape and visual science (part I) , 1973 .

[11]  Kaleem Siddiqi,et al.  A shock grammar for recognition , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  D. Siersma,et al.  Properties of Conflict Sets in the Plane , 1999 .

[13]  Edward L. Chaney,et al.  Segmentation of Medical Image Objects Using Deformable Shape Loci , 1996, IPMI.

[14]  H. Blum Biological shape and visual science. I. , 1973, Journal of theoretical biology.

[15]  Alfred M. Bruckstein,et al.  The Curve Axis , 1996, Comput. Vis. Image Underst..

[16]  Benjamin B. Kimia,et al.  Symmetry-Based Indexing of Image Databases , 1998, J. Vis. Commun. Image Represent..

[17]  Seth J. Teller,et al.  Assisted articulation of closed polygonal models , 1998, SIGGRAPH '98.

[18]  HARRY BLUM,et al.  Shape description using weighted symmetric axis features , 1978, Pattern Recognit..