ACM Home Page
Please provide us with feedback. Feedback
Reconstructing occlusal surfaces of teeth using a genetic algorithm with simulated annealing type selection
Full text PdfPdf (708 KB)
Source ACM Symposium on Solid and Physical Modeling archive
Proceedings of the sixth ACM symposium on Solid modeling and applications table of contents
Ann Arbor, Michigan, United States
Pages: 39 - 46  
Year of Publication: 2001
ISBN:1-58113-366-9
Authors
Vladimir Savchenko  Faculty of Computer and Information Sciences, Hosei University, 3-7-2, Kajino-cho, Koganei-shi, Tokyo 184-8584, Japan
Lothar Schmitt  School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu City, Fukushima, 965-8580, Japan
Sponsor
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 4,   Downloads (12 Months): 22,   Citation Count: 2
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues   peer to peer  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/376957.376962
What is a DOI?

ABSTRACT

In this paper, we present an application of numerical optimization for surface reconstruction (more precisely: reconstruction of missing parts of a real geometric object represented by volume data) by employing a specially designed genetic algorithm to solve a problem concerning computer-aided design in dentistry. Using a space mapping technique the surface of a given model tooth is fitted by a shape transformation to extrapolate (or reconstruct) the remaining surface of a patient's tooth with occurring damage such as a “drill hole.” Thereby, the genetic algorithm minimizes the error of the approximation by optimizing a set of control points that determine the coefficients for spline functions, which in turn define a space transformation. The fitness function to be minimized by the genetic algorithm is the error between the transformed occlusal surface of the model tooth and the remaining occlusal surface of the damaged (drilled) tooth. The algorithm, that is used, is based upon a proposal by Mahfoud and Goldberg. It uses a simulated-annealing type selection scheme, which is applied sequentially (pair-wise, or one-by-one) to the members in the parent generation and their respective offspring generated by mutation-crossover. We outline a proof of convergence for this algorithm. The algorithm is parallel in regard to computing the fitness-values of creatures.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
E.H.L. Aarts and P.J.M. van Laarhoven, Simulated Annealing: An Introduction, Statistica Neerlandica 43, 1989, 31-52
 
2
H. Akakie, A new look at the statistical model identification, IEEE Transactions Automatic Control, Vol. AC-19, 6, 1974, 716-723
 
3
 
4
 
5
F. Duret, J.L. Blouin, and B. Duret, CAD/CAM in dentistry, J. Am. Dent. Assoc., 117(11), 1988, 715-720
 
6
 
7
8
 
9
T. Hermann, Z. Kovacs, and T. Varady, Special applications in surface fitting, Geometric Modeling: Theory and Practice, W. Strasser, R. Klein and R.Rau (Eds), Springer, 1997, 14-31
 
10
 
11
D.L. Isaacson and R.W. Madsen, Markov Chains: Theory and Applications, Prentice-Hall, 1961
 
12
 
13
J.R. Koza, Genetic Programming 11, MIT Press, 1994
 
14
J. Loos, G. Greiner, and H.-P. Seidel, A variational approach to progressive lens design, Computer-Aided Design, Vol. 30, No. 8, 1998, 595-602
 
15
J. Loos, Ph. Slusailek, and H.-P. Seidel, Using wavefront tracing for the visualization and optimization of progressive lenses, EUROGRAPHICS'98, Computer Graphics Forum, vol. 17, No. 3, 1998, 255-265
 
16
S.W. Mahfoud and D.E. Goldberg, A Genetic Algorithm for Parallel Simulated Annealing, in: R. Manner, B. Manderick (eds.), Parallel Problem Solving from Nature 2, Elsevier, 1992, 301-310
 
17
 
18
 
19
 
20
 
21
A. Pasko, V. Adzhiev, A. Sourin, and V. Savchenko, Function representation in geometric modeling: concepts, implementation and applications, The Visual Computer, 11(6), 1995, 429-446
 
22
V.V. Savchenko, 3-D Geometric Modeller with Haptic Feedback: Engraving Simulation, in proceedings 2 m PHANTOM Users Research Symposium 2000 , M. Harders and S. Huber, (eds.), Zurich, Switzerland, July 6-7, 2000, 35- 42
 
23
V.V. Savchenko and A.A. Pasko, Transformation of functionally defined shapes by extended space mappings, The Visual Computer, 1998,257-270
 
24
 
25
V.V. Savchenko, A.A. Pasko, T.L. Kunii, and A.V. Savchenko, Feature based sculpting of functionally defined 3-D geometric objects, T.S. Chua et al. (eds), Proc. of the MMM'95, Nov. 1995, 341-348
 
26
V.V. Savchenko, A.A. Pasko, O. Okunev and T.L. Kunii, Function representation of solids reconstructed from scattered surface points and countours, Computer Graphics Forum, 14(4), 1995, 181-188
 
27
 
28
 
29
 
30
E. Seneta, Non-negative Matrices and Markov Chains, Springer Series in Statistics, Springer, 1981
 
31
 
32
T. Sohmura and J. Takahashi, Improvement of CAD to produce crown by considering occlusion., Dental Materials Journal, 12(2), 1993, 190-195
 
33
V. A. Vasilenko, Spline functions: theory, algorithms and programs, (in Russian), Nauka (Novosibirsk), 1983
 
34


Collaborative Colleagues:
Vladimir Savchenko: colleagues
Lothar Schmitt: colleagues

Peer to Peer - Readers of this Article have also read: