Grouping . ,-, → ,-, into regions , curves , and junctions

We address the problem of extracting segmented, structured information from noisy data obtained through local processing of images. A unified computational framework is developed for the inference of multiple salient structures such as junctions, curves, regions, and surfaces from any combinations of points, curve elements and surface patch elements inputs in 2-D and 3-D. The methodology is grounded in two elements: tensor calculus for representation, and non-linear voting for data communication. Each input site communicates its information (a tensor) to its neighborhood through a predefined (tensor) field, and therefore cast a (tensor) vote. Each site collects all the votes casted at its location and encodes them into a new tensor. A local, parallel routine such as a modified marching cube/square process then simultaneously detects junctions, curves, regions, and surfaces. The proposed method is non-iterative, requires no initial guess or thresholding, can handle the presence of multiple curves, regions and surfaces in a large amount of noise while still preserves discontinuities, and the only free parameter is scale.

[1]  R. Hummel,et al.  Toward a low-level description of dot clusters: Labeling edge, interior, and noise points , 1979 .

[2]  R. Weale.  Vision. A Computational Investigation Into the Human Representation and Processing of Visual Information. David Marr , 1983 .

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

[4]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

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

[6]  Steven W. Zucker,et al.  The Organization Of Curve Detection: Coarse Tangent Fields And Fine Spline Coverings , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[7]  Shimon Ullman,et al.  Structural Saliency: The Detection Of Globally Salient Structures using A Locally Connected Network , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[8]  Steven W. Zucker,et al.  Trace Inference, Curvature Consistency, and Curve Detection , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Narendra Ahuja,et al.  Extraction of early perceptual structure in dot patterns: Integrating region, boundary, and component gestalt , 1989, Comput. Vis. Graph. Image Process..

[10]  Richard S. Weiss,et al.  Perceptual Grouping Of Curved Lines , 1989, Other Conferences.

[11]  Barbara Moore,et al.  Theory of networks for learning , 1990, Defense, Security, and Sensing.

[12]  Tony DeRose,et al.  Surface reconstruction from unorganized points , 1992, SIGGRAPH.

[13]  Pascal Fua,et al.  Segmenting Unstructured 3D Points into Surfaces , 1992, ECCV.

[14]  Kim L. Boyer,et al.  Integration, Inference, and Management of Spatial Information Using Bayesian Networks: Perceptual Organization , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

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

[16]  Gérard G. Medioni,et al.  Inferring global perceptual contours from local features , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Rüdiger von der Heydt,et al.  A computational model of neural contour processing: Figure-ground segregation and illusory contours , 1993, 1993 (4th) International Conference on Computer Vision.

[18]  Charles V. Stewart,et al.  MINPRAN: A New Robust Estimator for Computer Vision , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Laxmi Parida,et al.  Visual organization for figure/ground separation , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Lance R. Williams,et al.  Stochastic Completion Fields: A Neural Model of Illusory Contour Shape and Salience , 1997, Neural Computation.

[21]  Gérard G. Medioni,et al.  Inference of Surfaces, 3D Curves, and Junctions From Sparse, Noisy, 3D Data , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  Gérard G. Medioni,et al.  Integrated surface, curve and junction inference from sparse 3-D data sets , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).