Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine
Joint Authors
Liu, Yan-Chen
Liu, Yi-Hung
Cheng, Wei-Teng
Hsiao, Yu-Tsung
Su, Jui-Yiao
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-24
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly.
First, an image reprocessing procedure is designed to enhance the contrast of the tactile image.
In the second layer, geometric features and Fourier descriptors are extracted from the image.
Then, kernel principal component analysis (kernel PCA) is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion.
The transformed features are fed into the third layer for classification.
In this paper, we design a classifier by combining the multiple kernel learning (MKL) algorithm and support vector machine (SVM).
We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements.
Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors.
Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy.
The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.
American Psychological Association (APA)
Liu, Yi-Hung& Hsiao, Yu-Tsung& Cheng, Wei-Teng& Liu, Yan-Chen& Su, Jui-Yiao. 2014. Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-476383
Modern Language Association (MLA)
Liu, Yi-Hung…[et al.]. Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine. Mathematical Problems in Engineering No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-476383
American Medical Association (AMA)
Liu, Yi-Hung& Hsiao, Yu-Tsung& Cheng, Wei-Teng& Liu, Yan-Chen& Su, Jui-Yiao. Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-476383
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-476383