Segmentation and determination of grid points of curve points in terrestrial laser scanning data for regular curve surfaces via C-means integrated fuzzy logic approach

Abstract Terrestrial Laser Scanners (TLS) are used frequently in three dimensional documentation studies and present an alternative method for three dimensional modeling without any deformation of scale. In this study, point cloud data segmentation is used for photogrammetrical image data production from laser scanner data. The segmentation studies suggest several methods for automation of curve surface determination for digital terrain modeling. In this study, fuzzy logic approach has been used for the automatic segmentation of the regular curve surfaces which differ in their depths to the instrument. This type of shapes has been usually observed in the dome surfaces for close range architectural documentation. The model of C-means integrated fuzzy logic approach has been developed with MatLAB 7.0 software. Gauss2mf membership functions algorithm has been tested with original data set. These results were used in photogrammetric 3D modeling process. As the result of the study, testing the results of point cloud data set has been discussed and interpreted with all of its advantages and disadvantages in Section 5 .

[1]  Jie Chen,et al.  Architectural Modeling from Sparsely Scanned Range Data , 2008, International Journal of Computer Vision.

[2]  Song Han,et al.  On Recovering Hyperquadrics from Range Data , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Michael Spann,et al.  MIR: An Approach to Robust Clustering-Application to Range Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  F. Bookstein.  Fitting conic sections to scattered data , 1979 .

[5]  D. Lichti,et al.  Error Propagation in Directly Georeferenced Terrestrial Laser Scanner Point Clouds for Cultural Heritage Recording , 2004 .

[6]  Horst Bunke,et al.  Fast range image segmentation using high-level segmentation primitives , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[7]  Ramesh C. Jain,et al.  Segmentation through Variable-Order Surface Fitting , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Chau-Chang Wang,et al.  Laser Scanning, Theory and Applications , 2011 .

[9]  Andrew W. Fitzgibbon,et al.  A Buyer's Guide to Conic Fitting , 1995, BMVC.

[10]  George Vosselman,et al.  Segmentation of point clouds using smoothness constraints , 2006 .

[11]  E. K. Forkuo.  Automatic fusion of photogrammetric imagery and laser scanner point clouds , 2003 .

[12]  Laurent D. Cohen,et al.  A Hybrid Hyperquadric Model for 2-D and 3-D Data Fitting , 1996, Comput. Vis. Image Underst..

[13]  Naokazu Yokoya,et al.  Range Image Segmentation Based on Differential Geometry: A Hybrid Approach , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Sugata Ghosal,et al.  Segmentation of range images: an orthogonal moment-based integrated approach , 1993, IEEE Trans. Robotics Autom..

[15]  Marc Jaeger,et al.  Segmentation of architecture shape information from 3D point cloud , 2009, VRCAI '09.

[16]  Bir Bhanu,et al.  RANGE DATA PROCESSING: REPRESENTATION OF SURFACES BY EDGES. , 1986 .

[17]  Bf Buxton,et al.  Three-Dimensional Surface Curvature Estimation using Quadric Surface Patches , 2002 .

[18]  A. Gruen,et al.  Least squares 3D surface and curve matching , 2005 .

[19]  Andrew W. Fitzgibbon,et al.  Extracting surface patches from complete range descriptions , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[20]  Xin Li,et al.  An estimation-based approach for range image segmentation: on the reliability of primitive extraction , 2003, Pattern Recognit..

[21]  In Su Chang,et al.  Segmentation based on fusion of range and intensity images using robust trimmed methods , 2001, Pattern Recognit..

[22]  José Luis Lerma,et al.  Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods , 2008 .

[23]  Michel Devy,et al.  Fast range image segmentation by an edge detection strategy , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[24]  Tormod Næs,et al.  New modifications and applications of fuzzy C-means methodology , 2008, Comput. Stat. Data Anal..

[25]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[26]  Ramakant Nevatia,et al.  Segmented descriptions of 3-D surfaces , 1987, IEEE Journal on Robotics and Automation.