Registration of 3-D images by genetic optimization
Pattern Recognition Letters - Special issue on genetic algorithms
Practical genetic algorithms
The digital Michelangelo project: 3D scanning of large statues
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Using Photo-Consistency to Register 2D Optical Images of the Human Face to a 3D Surface Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
SoftPOSIT: Simultaneous Pose and Correspondence Determination
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
The Trimmed Iterative Closest Point Algorithm
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Genetic Algorithms for Free-Form Surface Matching
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Registration of an Uncalibrated Image Pair to a 3D Surface Model
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Building a digital model of Michelangelo's Florentine Pieta
IEEE Computer Graphics and Applications
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In this paper we present a successful application of genetic algorithms to the registration of uncalibrated optical images to a 3D surface model. The problem is to find the projection matrices corresponding to the images in order to project the texture on the surface as precisely as possible. Recently, we have proposed a novel method that generalises the photo-consistency approach by Clarkson et al. to the case of uncalibrated cameras by using a genetic algorithm. In previous studies we focus on the computer vision aspects of the method, while here we analyse the genetic part. In particular, we use semi-synthetic data to study the performance of different GAs and various types of selector, mutation and crossover. New experimental results on real data are also presented to demonstrate the efficiency of the method.