Comparison of Optimisation Algorithms for Deformable Template Matching

Authors:
Vasileios Zografos
Affiliations:
Computer Vision Laboratory, ISY, Linköping University, Linköping, Sweden SE-581 83
Venue:
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Year:
2009

Citing 13
Cited 0

Deformable template models: a review

Signal Processing - Special issue on deformable models and techniques for image and signal processing
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence

Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
On Advances in Statistical Modeling of Natural Images

Journal of Mathematical Imaging and Vision
Analysis of Generalized Pattern Searches

SIAM Journal on Optimization
Object Recognition by Flexible Template Matching using Genetic Algorithms

ECCV '92 Proceedings of the Second European Conference on Computer Vision
Fast object recognition in noisy images using simulated annealing

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
The CMU Pose, Illumination, and Expression (PIE) Database

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Genetic Search for Face Detection and Verification

ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Flexible Template and Model Matching Using Intensity

DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
Object tracking: A survey

ACM Computing Surveys (CSUR)
Affine invariant, model-based object recognition using robust metrics and bayesian statistics

ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work we examine in detail the use of optimisation algorithms on deformable template matching problems. We start with the examination of simple, direct-search methods and move on to more complicated evolutionary approaches. Our goal is twofold: first, evaluate a number of methods examined under different template matching settings and introduce the use of certain, novel evolutionary optimisation algorithms to computer vision, and second, explore and analyse any additional advantages of using a hybrid approach over existing methods. We show that in computer vision tasks, evolutionary strategies provide very good choices for optimisation. Our experiments have also indicated that we can improve the convergence speed and results of existing algorithms by using a hybrid approach.