Model Selection Techniques and Merging Rules for Range Data Segmentation Algorithms-Reference-Cited by-同舟云学术

Model Selection Techniques and Merging Rules for Range Data Segmentation Algorithms

Published:2000-11 Issue:2 Volume:80 Page:215-245
ISSN:1077-3142
Container-title:Computer Vision and Image Understanding
language:en
Short-container-title:Computer Vision and Image Understanding

Author:

Bubna Kishore,Stewart Charles V.

Publisher

Elsevier BV

Subject

Computer Vision and Pattern Recognition,Signal Processing,Software

Reference53 articles.

1. H. Akaike, Information theory and an extension of the maximum likelihood principle, in 2nd International Symposium of Information TheoryB. N. Petrov and F. Csaki, Eds., pp. 267–281. Akad. Kiado, Budapest, 1973.

2. Model-based object recognition in dense-range images—A review;Arman;ACM Comput. Surveys,1993

3. Least-squares fitting using orthogonal multinomials;Bartels;ACM Trans. Math. Software,1985

4. Surfaces in Range Image Understanding

5. P. J. Besl, J. B. Birch, and L. T. Watson, Robust window operators, in Proceedings, IEEE International Conference on Computer Vision, 1988, pp. 591–600.

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A comparative study of model selection criteria for computer vision applications;Image and Vision Computing;2008-12

2. Registration of Challenging Image Pairs: Initialization, Estimation, and Decision;IEEE Transactions on Pattern Analysis and Machine Intelligence;2007-11

3. Uncertainty modeling and model selection for geometric inference;IEEE Transactions on Pattern Analysis and Machine Intelligence;2004-10

4. Range image segmentation by an effective jump-diffusion method;IEEE Transactions on Pattern Analysis and Machine Intelligence;2004-09

5. A generalisation of model selection criteria;Pattern Analysis & Applications;2004-04-01