Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Performance of optical flow techniques
International Journal of Computer Vision
Seeing the light: artificial evolution, real vision
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Evolving Task Specific Image Operator
EvoIASP '99/EuroEcTel '99 Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications
Appearance-Based Obstacle Detection with Monocular Color Vision
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Active vision and feature selection in evolutionary behavioral systems
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
High speed obstacle avoidance using monocular vision and reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Parisian evolution with honeybees for three-dimensional reconstruction
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Synthesis of interest point detectors through genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolving visual sonar: Depth from monocular images
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Strongly typed genetic programming
Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Evolving Vision Controllers with a Two-Phase Genetic Programming System Using Imitation
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
| Hi-index | 0.00 |
The work presented in this paper is part of the developmentof a robotic system able to learn context dependent visual clues to navigatein its environment. We focus on the obstacle avoidance problem asit is a necessary function for a mobile robot. As a first step, we use an offlineprocedure to automatically design algorithms adapted to the visualcontext. This procedure is based on genetic programming and the candidatealgorithms are evaluated in a simulation environment. The evolutionaryprocess selects meaningful visual primitives in the given contextand an adapted strategy to use them. The results show the emergenceof several different behaviors outperforming hand-designed controllers.