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Design of Computer Vision System for Objects Recognition in Automation Industries


Affiliations
1 Mechanical Engineering Department, MIET Meerut, Meerut, Uttar Pradesh, India
2 Mechanical Engineering Department, NIT Kurukshetra, Thanesar, Haryana, India
3 Central Scientific Instrument Organizations (CSIO), Chandigarh, Punjab, India
     

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The field of machine vision has been developing at quick pace. The development in this field, dissimilar to most settled fields, has been both in expansiveness and profundity of ideas and procedures. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear, and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of different objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial Neural Network (ANN) is used for classification of different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. Invariant example acknowledgment utilizing neural systems is an especially appealing methodology on account of its closeness with natural frameworks. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects.

Keywords

Artificial Neural Network, Computer Vision, Fourier Descriptors, Image Processing, Object Recognition.
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  • Design of Computer Vision System for Objects Recognition in Automation Industries

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Authors

Tushar Jain
Mechanical Engineering Department, MIET Meerut, Meerut, Uttar Pradesh, India
Meenu
Mechanical Engineering Department, NIT Kurukshetra, Thanesar, Haryana, India
H. K. Sardana
Central Scientific Instrument Organizations (CSIO), Chandigarh, Punjab, India

Abstract


The field of machine vision has been developing at quick pace. The development in this field, dissimilar to most settled fields, has been both in expansiveness and profundity of ideas and procedures. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear, and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of different objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial Neural Network (ANN) is used for classification of different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. Invariant example acknowledgment utilizing neural systems is an especially appealing methodology on account of its closeness with natural frameworks. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects.

Keywords


Artificial Neural Network, Computer Vision, Fourier Descriptors, Image Processing, Object Recognition.

References





DOI: https://doi.org/10.18311/gjeis%2F2018%2F20660