Object recognition by three‐dimensional curve matching

Experimental results for the recognition of general curves in three‐space using registered range and intensity images are presented. the matching algorithm uses fast Fourier transforms to determine the least‐squares difference between sequences of points sampled at equal intervals along two piecewise linear approximations of curves in three‐space and returns the rotation and translation required to bring one of the curves into closest juxtaposition with the other. Performance of the algorithm is demonstrated by matching curves in three‐space which are the boundaries of regions of contrasting reflectivity on curved surfaces. the experiments use a recently developed range sensor which is able to generate a 512 × 460 × 12 bit range image (with registered intensity image) in 40 s.

[1]  Ray A. Jarvis,et al.  A Laser Time-of-Flight Range Scanner for Robotic Vision , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  A. R. Johnston,et al.  A Scanning Laser Rangefinder for a Robotic Vechicle , 1977, IJCAI.

[3]  Jake K. Aggarwal,et al.  Experiments in combining intensity and range edge maps , 1983, Comput. Vis. Graph. Image Process..

[4]  Ray A. Jarvis,et al.  A Perspective on Range Finding Techniques for Computer Vision , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Martin Herman,et al.  Generating detailed scene descriptions from range images , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[6]  Yoshiaki Shirai,et al.  Recognition of polyhedrons with a range finder , 1971, IJCAI.

[7]  Jean Ponce,et al.  Toward a surface primal sketch , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[8]  Yoshiaki Shirai,et al.  Object Recognition Using Three-Dimensional Information , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  R. Haralick,et al.  The Topographic Primal Sketch , 1983 .

[10]  J. Hochberg,et al.  Pictorial recognition as an unlearned ability: a study of one child's performance. , 1962, The American journal of psychology.

[11]  Jean-Daniel Boissonnat,et al.  Towards a Flexible Vision System , 1983 .

[12]  Olivier D. Faugeras,et al.  A 3-D Recognition and Positioning Algorithm Using Geometrical Matching Between Primitive Surfaces , 1983, IJCAI.

[13]  Michael Potmesil,et al.  Generating Models of Solid Objects by Matching 3D Surface Segments , 1983, IJCAI.

[14]  Russell H. Taylor,et al.  Automatic Synthesis of Fine-Motion Strategies for Robots , 1984 .

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

[16]  Robert C. Bolles,et al.  A RANSAC-Based Approach to Model Fitting and Its Application to Finding Cylinders in Range Data , 1981, IJCAI.

[17]  Robert C. Bolles,et al.  3DPO: A Three- Dimensional Part Orientation System , 1986, IJCAI.

[18]  S. Inokuchi,et al.  Range-imaging system for 3-D object recognition , 1984 .

[19]  Ann Patricia Fothergill,et al.  Forming Models Of Plane-And-Cylinder Faceled Bodies From Light Stripes , 1975, IJCAI.

[20]  W. Grimson,et al.  Model-Based Recognition and Localization from Sparse Range or Tactile Data , 1984 .

[21]  Robert C. Bolles,et al.  3DPO's strategy for matching three-dimensional objects in range data , 1984, ICRA.

[22]  A. E. Brain,et al.  The measurement and use of registered reflectance and range data in scene analysis , 1977, Proceedings of the IEEE.

[23]  Thomas O. Binford,et al.  Computer Description of Curved Objects , 1973, IEEE Transactions on Computers.