Model-based image interpretation using genetic algorithms
Image and Vision Computing - Special issue: BMVC 1991
A computational approach for corner and vertex detection
International Journal of Computer Vision
Affine Morphological Multiscale Analysis of Corners andMultiple Junctions
International Journal of Computer Vision
What Tasks can be Performed with an Uncalibrated Stereo Vision System?
International Journal of Computer Vision
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Validation of a parallel genetic algorithm for image reconstruction from projections
Journal of Parallel and Distributed Computing
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Autonomous robot calibration using vision technology
Robotics and Computer-Integrated Manufacturing
Epipolar geometry estimation based on evolutionary agents
Pattern Recognition
Rapid surface registration of 3D volumes using a neural network approach
Image and Vision Computing
Panoramic stereo reconstruction using non-SVP optics
Computer Vision and Image Understanding
Using a genetic algorithm to register an uncalibrated image pair to a 3D surface model
Engineering Applications of Artificial Intelligence
Editorial: Omnidirectional robot vision
Robotics and Autonomous Systems
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In this paper, we propose an original evolutionary-based method for 3D panoramic reconstruction from an uncalibrated stereovision system (USS). The USS is composed of five cameras located on an arc of a circle around the object to be analyzed. The main originality of this work concerns the process of the calculation of the 3D information. Actually, with our method, 3D coordinates are directly obtained without any prior estimation of the fundamental matrix. The method operates in two steps. Firstly, points of interest are detected in pairs of images acquired by two consecutive cameras of the USS are matched. And secondly, using evolutionary algorithms, we jointly compute the transformed matrix between the two images and the respective depth of the points of interest. The accuracy of the proposed method is validated through a comparison with the depth values obtained using a traditional method. In order to perform 3D panoramic object reconstruction, the process is repeated for all the pairs of consecutive cameras. The 3D points thus obtained throughout the successive steps of the process which correspond to the different points of interest, are combined in order to obtain a set of 3D points all around the analyzed object.