Multi-resolution and slice-oriented feature extraction and segmentation of digitized data

Given an object digitized as sequences of scan lines, we propose an approach to the extraction of feature lines and object segmentation based on a multi-resolution representation and analysis of the scan data. First, the scan lines are represented using a multi-resolution model which provides a flexible and useful reorganization of the data for simplification purposes and especially for the classification of points according to their level of detail, or scale. Then, scan lines are analyzed from a geometrical point of view in order to decompose each profile into basic patterns which identify 2D features of the profile. Merging the scale and geometric classification, 3D feature lines of the digitized object are reconstructed tracking patterns of similar shape across profiles. Finally, a segmentation is achieved which gives a form-feature oriented view of the digitized data. The proposed approach provides a computationally light solution to the simplification of large models and to the segmentation of object digitized as sequences of scan lines, but it can be applied to a wider range of digitized data.

[1]  Mubarak Shah,et al.  A Fast algorithm for active contours and curvature estimation , 1992, CVGIP Image Underst..

[2]  Josef Kittler,et al.  A survey of the hough transform , 1988, Comput. Vis. Graph. Image Process..

[3]  Ralph R. Martin,et al.  Reverse engineering of geometric models - an introduction , 1997, Comput. Aided Des..

[4]  Karl-Heinz Häfele,et al.  Curvature estimation for segmentation of triangulated surfaces , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[5]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[6]  David Salesin,et al.  Wavelets for computer graphics: a primer.1 , 1995, IEEE Computer Graphics and Applications.

[7]  Emanuele Trucco,et al.  Introductory techniques for 3-D computer vision , 1998 .

[8]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Tamás Várady,et al.  Reverse Engineering Regular Objects: Simple Segmentation and Surface Fitting Procedures , 1998, Int. J. Shape Model..

[10]  Horst Bunke,et al.  Fast segmentation of range images into planar regions by scan line grouping , 1994, Machine Vision and Applications.

[11]  Josef Hoschek,et al.  A geometric concept of reverse engineering of shape: approximation and feature lines , 1998 .

[12]  Giuseppe Patanè,et al.  Feature Lines Reconstruction for Reverse Engineering , 2001, Digital Earth Moving.

[13]  E. J. Stollnitz,et al.  Wavelets for Computer Graphics : A Primer , 1994 .

[14]  Leif Kobbelt,et al.  Extraction of feature lines on triangulated surfaces using morphological operators , 2000 .

[15]  Michela Spagnuolo,et al.  Shape abstraction tools for modeling complex objects , 1997, Proceedings of 1997 International Conference on Shape Modeling and Applications.

[16]  Eric Saux,et al.  Data reduction of polygonal curves using B-splines , 1999, Comput. Aided Des..

[17]  Paolo Cignoni,et al.  A comparison of mesh simplification algorithms , 1998, Comput. Graph..

[18]  Markus H. Gross,et al.  Multiresolution feature extraction for unstructured meshes , 2001, Proceedings Visualization, 2001. VIS '01..

[19]  Tosiyasu L. Kunii,et al.  Shape Modeling and Shape Analysis Based on Singularities , 1996, Int. J. Shape Model..

[20]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[21]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .