Hierarchical Decomposition and Axial Shape Description

A method for producing a segmented axial description of a given shape together with a hierarchical decomposition of the shape into its parts is presented. The novelty of this approach lies in the combination of several competing approaches and tools into a unified scheme and an efficient implementation producing natural descriptions. Smooth local symmetries are used for the axial description of parts, which are suggested by curvature sign changes. Parallel symmetries are used to provide information on global relationships within the shape. This information is used for parsing shape into a hierarchy of parts. This approach uses both region and contour information, can handle shapes with corners, and addresses the issue of local versus global information, the issue of scale, and the notion of part. The method is computationally efficient, robust, and stable. Results that show that it provides an intuitive shape description are included. >

[1]  F. Attneave.  Some informational aspects of visual perception. , 1954, Psychological review.

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

[3]  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.

[4]  Rodney A. Brooks,et al.  Symbolic Reasoning Among 3-D Models and 2-D Images , 1981, Artif. Intell..

[5]  Donald D. Hoffman,et al.  Parts of recognition , 1984, Cognition.

[6]  M. Brady,et al.  Smoothed Local Symmetries and Their Implementation , 1984 .

[7]  Jonathan H Connell,et al.  Learning Shape Descriptions: Generating and Generalizing Models of Visual Objects , 1985 .

[8]  Peter Giblin,et al.  Local Symmetry of Plane Curves , 1985 .

[9]  Donald D. Hoffman,et al.  Codon constraints on closed 2D shapes , 1985, Comput. Vis. Graph. Image Process..

[10]  Azriel Rosenfeld,et al.  Axial representations of shape , 1986, Computer Vision Graphics and Image Processing.

[11]  Michael Leyton,et al.  Symmetry-curvature duality , 1987, Comput. Vis. Graph. Image Process..

[12]  B. Barsky,et al.  An Introduction to Splines for Use in Computer Graphics and Geometric Modeling , 1987 .

[13]  Michael Brady,et al.  Generating and Generalizing Models of Visual Objects , 1987, Artif. Intell..

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

[15]  Michael Leyton,et al.  A Process-Grammar for Shape , 1988, Artif. Intell..

[16]  M. Leyton.  Inferring Causal History from Shape , 1989 .

[17]  S. Zucker,et al.  Toward a computational theory of shape: an overview , 1990, eccv 1990.

[18]  F. Ulupinar,et al.  Inferring shape from contour for curved surfaces , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[19]  Philippe Saint-Marc,et al.  B-Spline Contour Representation and Symmetry Detection , 1990, ECCV.

[20]  J. Brian Subirana-Vilanova,et al.  Curved inertia frames and the skeleton sketch: Finding salient frames of reference , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[21]  Jean Ponce,et al.  On characterizing ribbons and finding skewed symmetries , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[22]  Philippe Saint-Marc,et al.  B-spline Contour Representation and Symmetry Detection , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Ram Nevatia,et al.  Specifying heterogeneous suites for vision tasks , 1993, 1993 Computer Architectures for Machine Perception.