Adaptive segmentation of color image for vision navigation of mobile robots

Authors:
Zeng-Shun Zhao;Zeng-Guang Hou;Min Tan;Yong-Qian Zhang
Affiliations:
Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 6
Cited 0

Uniqueness of the Gaussian Kernel for Scale-Space Filtering

IEEE Transactions on Pattern Analysis and Machine Intelligence
On the color image segmentation algorithm based on the thresholding and the fuzzy C-means techniques

Pattern Recognition
Reduction of color space dimensionality by moment-preserving thresholding and its application for edge detection in color images

Pattern Recognition Letters
Principal component neural networks: theory and applications

Principal component neural networks: theory and applications
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Scale-space filtering

IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

The self-localization problem is very important when the mobile robot has to move in autonomous way. Among techniques for self-localization, landmark-based approach is preferred for its simplicity and much less memory demanding for descriptions of robot surroundings. Door-plates are selected as visual landmarks. In this paper, we present an adaptive segmentation approach based on Principal Component Analysis (PCA) and scale-space filtering. To speed up the entire color segmentation and use the color information as a whole, PCA is implemented to project tristimulus R, G and B color space to the first principal component (1st PC) axis direction and scale-space filtering is used to get the centers of color classes. This method has been tested in the color segmentation of door-plate images captured by mobile robot CASIA-1. Experimental results are provided to demonstrate the effectiveness of this proposed method.