Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cooperative Navigation of Micro-Rovers Using Color Segmentation
Autonomous Robots
CM-Pack'01: Fast Legged Robot Walking, Robust Localization, and Team Behaviors
RoboCup 2001: Robot Soccer World Cup V
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Autonomous Planned Color Learning on a Legged Robot
RoboCup 2006: Robot Soccer World Cup X
Robust Color Segmentation Through Adaptive Color Distribution Transformation
RoboCup 2006: Robot Soccer World Cup X
Automatic Acquisition of Robot Motion and Sensor Models
RoboCup 2006: Robot Soccer World Cup X
Color learning and illumination invariance on mobile robots: A survey
Robotics and Autonomous Systems
Robust autonomous structure-based color learning on a mobile robot
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Color learning on a mobile robot: towards full autonomy under changing illumination
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Artificial intelligence in robocup
Reasoning, Action and Interaction in AI Theories and Systems
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Color segmentation is a challenging subtask in computer vision. Most popular approaches are computationally expensive, involve an extensive off-line training phase and/or rely on a stationary camera. This paper presents an approach for color learning on-board a legged robot with limited computational and memory resources. A key defining feature of the approach is that it works without any labeled training data. Rather, it trains autonomously from a color-coded model of its environment. The process is fully implemented, completely autonomous, and provides high degree of segmentation accuracy.