Abstract
This paper presents a new approach for automated parts recognition. It is based on the use of the signature and autocorrelation functions for feature extraction and a neural network for the analysis of recognition. The signature represents the shapes of boundaries detected in digitized binary images of the parts. The autocorrelation coefficients computed from the signature are invariant to transformations such as scaling, translation and rotation of the parts. These unique extracted features are fed to the neural network. A multilayer perceptron with two hidden layers, along with a backpropagation learning algorithm, is used as a pattern classifier. In addition, the position information of the part for a robot with a vision system is described to permit grasping and pick-up. Experimental results indicate that the proposed approach is appropriate for the accurate and fast recognition and inspection of parts in automated manufacturing systems.
| Original language | English |
|---|---|
| Pages (from-to) | 167-175 |
| Number of pages | 9 |
| Journal | Journal of Intelligent Manufacturing |
| Volume | 8 |
| Issue number | 3 |
| Publication status | Published - 1997 Dec 1 |
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Keywords
- Feature extraction
- Neural networks
- Pattern recognition
- Time series
ASJC Scopus subject areas
- Artificial Intelligence
- Industrial and Manufacturing Engineering
Cite this
A new approach for automated parts recognition using time series analysis and neural networks. / Lee, Young Hae; Moon, Seungki; Lee, Hong Chul.
In: Journal of Intelligent Manufacturing, Vol. 8, No. 3, 01.12.1997, p. 167-175.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A new approach for automated parts recognition using time series analysis and neural networks
AU - Lee, Young Hae
AU - Moon, Seungki
AU - Lee, Hong Chul
PY - 1997/12/1
Y1 - 1997/12/1
N2 - This paper presents a new approach for automated parts recognition. It is based on the use of the signature and autocorrelation functions for feature extraction and a neural network for the analysis of recognition. The signature represents the shapes of boundaries detected in digitized binary images of the parts. The autocorrelation coefficients computed from the signature are invariant to transformations such as scaling, translation and rotation of the parts. These unique extracted features are fed to the neural network. A multilayer perceptron with two hidden layers, along with a backpropagation learning algorithm, is used as a pattern classifier. In addition, the position information of the part for a robot with a vision system is described to permit grasping and pick-up. Experimental results indicate that the proposed approach is appropriate for the accurate and fast recognition and inspection of parts in automated manufacturing systems.
AB - This paper presents a new approach for automated parts recognition. It is based on the use of the signature and autocorrelation functions for feature extraction and a neural network for the analysis of recognition. The signature represents the shapes of boundaries detected in digitized binary images of the parts. The autocorrelation coefficients computed from the signature are invariant to transformations such as scaling, translation and rotation of the parts. These unique extracted features are fed to the neural network. A multilayer perceptron with two hidden layers, along with a backpropagation learning algorithm, is used as a pattern classifier. In addition, the position information of the part for a robot with a vision system is described to permit grasping and pick-up. Experimental results indicate that the proposed approach is appropriate for the accurate and fast recognition and inspection of parts in automated manufacturing systems.
KW - Feature extraction
KW - Neural networks
KW - Pattern recognition
KW - Time series
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M3 - Article
AN - SCOPUS:0031169384
VL - 8
SP - 167
EP - 175
JO - Journal of Intelligent Manufacturing
JF - Journal of Intelligent Manufacturing
SN - 0956-5515
IS - 3
ER -