Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of image registration techniques
ACM Computing Surveys (CSUR)
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Object Pose: The Link between Weak Perspective,Paraperspective, and Full Perspective
International Journal of Computer Vision
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Robust camera parameter estimation using genetic algorithm
Pattern Recognition Letters
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
Model-Based Pose Estimation Using Genetic Algorithm
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Registration of Cad-Models to Images by Iterative Inverse Perspective Matching
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
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The work presented in this paper concerns visual-based relative navigation of autonomous vehicles for satellite service. An evolutionary-based algorithm for the problem of pose estimation of 3D objects using 2D images is presented. The procedure consists on looking for six position parameters, three for rotation and three for translation, such that the projection of a model best fits a set of points (vertices) extracted from a 2D image. Distinguishing feature of the proposed algorithm is that the best matching model is also looked for. This feature is used for visual inspection in order to detect macro defects. A population of candidate solutions is used, whose goodness is measured in terms of distance between model and image points. Key features of the algorithm are also speed and robustness with respect to noise on the input data. Experimental results conducted both on synthetic and real images demonstrate the effectiveness of the proposed approach.