Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A bee colony optimization algorithm to job shop scheduling
Proceedings of the 38th conference on Winter simulation
Visual Navigation for Mobile Robots: A Survey
Journal of Intelligent and Robotic Systems
Segmentation of Brain MR Images Using an Ant Colony Optimization Algorithm
BIBE '09 Proceedings of the 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering
Image edge detection using ant colony optimization
WSEAS Transactions on Signal Processing
Vision-based global localization and mapping for mobile robots
IEEE Transactions on Robotics
Bent fingers' angle calculation using supervised ANN to control electro-mechanical robotic hand
Computers and Electrical Engineering
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This paper introduces a robust, adaptive and learning approach, called a nonlinear Log-Artificial-Bee-Colony in ('a','b') color space (Log-ab), for the recognition of colored markers. Log-ab optimizes the Recognition Performance Index (RPI) of the marker's templates by using the proposed on-line Bee-colony method for the purpose of adapting in the varied light environment. Furthermore, Log-ab guides a multidirectional robot accurately to move on a desired path in the dynamic light's disturbance by using Log-ab controller. Simultaneously, the proposed multidirectional robot with Kinect performs pattern recognition as well as measures the depth and orientation of a marker quite precisely. Then, for verification of the effectiveness of dynamic ('a','b') color space, the results of Signal to Noise (S/N) run as well as these results show the advantages of the proposed method over the existing color-based methods. Finally, Tracking Success Rate (TSR) of robot for a specific colorful marker shows the robustness of the proposed case as compared with the popular Scale Invariant Feature Transform (SIFT) and Phase-Only Correlation method (POC).