A maximum-likelihood approach to segmenting range data

The problem of segmenting a range image into homogeneous regions in each of which the range data correspond to a different surface is considered. The segmentation sought is a maximum-likelihood (ML) segmentation. Only planes, cylinders, and spheres are considered as presented in the image. The basic approach to segmentation is to divide the range image into windows, classify each window as a particular surface primitive, and group like windows into surface regions. Mixed windows are detected by testing the hypothesis that a window is homogeneous. Homogeneous windows are classified according to a generalized likelihood ratio test which is computationally simple and incorporates information from adjacent windows. Grouping windows of the same surface types is cast as a weighted ML clustering problem. Finally, mixed windows are segmented using an ML hierarchical segmentation algorithm. A similar approach is taken for segmenting visible-light images of Lambertian objects illuminated by a point source at infinity. >

[1]  David B. Cooper,et al.  Bayesian Recognition of Local 3-D Shape by Approximating Image Intensity Functions with Quadric Polynomials , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[3]  Bir Bhanu,et al.  Representation and Shape Matching of 3-D Objects , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[5]  Zhigang Fan,et al.  Unsupervised Textured Image Segmentation , 1986, IAS.

[6]  Ramakant Nevatia,et al.  Structured Descriptions of Complex Objects , 1973, IJCAI.

[7]  D. Lindley.  The Use of Prior Probability Distributions in Statistical Inference and Decisions , 1961 .

[8]  T. W. Anderson,et al.  Statistical analysis of time series , 1972 .

[9]  David B. Cooper,et al.  Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random Fields , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Fernand S. Cohen,et al.  Segmentation of three dimensional range data , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Fernand S. Cohen,et al.  Real Time 3-D Object Recognition From A Range Or Visible Light Image , 1986, Other Conferences.

[12]  David B. Cooper,et al.  On Optimally Combining Pieces of Information, with Application to Estimating 3-D Complex-Object Position from Range Data , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Fernand S. Cohen.  Adaptive hierarchical algorithm for accurate image segmentation , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[14]  Olivier D. Faugeras,et al.  Geometric matcher for recognizing and positioning 3-D rigid objects from passive stereo , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.