3D Region representation based on run-lengths: operations and efficiency

Abstract This paper discusses a volumetric representation of 3D regions achieved by the generalisation of run-length coding for 2D binary images. Methods for representing 3D regions/objects are discussed. The data structure for recording the runs describing the space occupation of the region is specifically investigated with a performance analysis of the memory requirement. We show that, in the worst case, the run-length description for 3D regions has a better performance on the memory expense than the traditional octree representation. A set of region operations which are basic to many applications are implemented based on the chosen data structure for describing the region. Techniques for achieving an efficient implementation of these operations are described, and detailed algorithms are presented. A quantitative analysis on the computational cost of these operations is also carried out. The representation scheme has been tested in our work on segmentation of 3D grey-level images. With these basic operations, the representation scheme is useful to applications such as computer vision, computer graphics and robotics.

[1]  Thomas S. Huang,et al.  Image processing , 1971 .

[2]  Azriel Rosenfeld,et al.  Hierarchical Image Analysis Using Irregular Tessellations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Donald Meagher,et al.  Geometric modeling using octree encoding , 1982, Comput. Graph. Image Process..

[4]  Xinquan Shen,et al.  Generic 3-D Shape Model: Acquisitions and Applications , 1995, CAIP.

[5]  Juyang Weng,et al.  Octree of objects in arbitrary motion: representation and efficiency , 1987 .

[6]  Hanan Samet,et al.  Region representation: quadtrees from boundary codes , 1980, CACM.

[7]  P. Besl Geometric modeling and computer vision , 1988, Proc. IEEE.

[8]  Narendra Ahuja,et al.  Interference Detection and Collision Avoidance Among Three Dimensional Objects , 1980, AAAI.

[9]  F. R. A. Hopgood,et al.  Machine Intelligence 5 , 1971, The Mathematical Gazette.

[10]  Narendra Ahuja,et al.  Octree representations of moving objects , 1984, Comput. Vis. Graph. Image Process..

[11]  Andrew Blake,et al.  Real-time tracking of surfaces with structured light , 1994, BMVC.

[12]  David C. Hogg Shape in machine vision , 1993, Image Vis. Comput..

[13]  Ramesh C. Jain,et al.  Three-dimensional object recognition , 1985, CSUR.

[14]  Jake K. Aggarwal,et al.  TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2008 .

[15]  Peter F. M. Nacken Image segmentation by connectivity preserving relinking in hierarchical graph structures , 1995, Pattern Recognit..

[16]  Michael Spann,et al.  3D shape modelling using a multi-scale surface model , 1997, Proceedings of International Conference on Image Processing.

[17]  Alex Pentland,et al.  Closed-Form Solutions for Physically Based Shape Modeling and Recognition , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Chris L. Jackins,et al.  Oct-trees and their use in representing three-dimensional objects , 1980 .

[19]  XINQUAN SHEN,et al.  Segmentation Of 2D And 3D Images Through A Hierarchical Clustering Based On Region Modelling , 1998, Pattern Recognit..

[20]  Claudio Montani Region representation: Parallel connected stripes , 1984, Comput. Vis. Graph. Image Process..

[21]  Charles R. Dyer,et al.  Model-based recognition in robot vision , 1986, CSUR.