Design of Computer Vision System for Objects Recognition in Automation Industries


Affiliations

  • MIET Meerut, Mechanical Engineering Department, Meerut, Uttar Pradesh, India
  • NIT Kurukshetra, Mechanical Engineering Department, Haryana, India
  • 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

Subject Discipline

MECHANICAL ENGG.

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References

Chellappa R, Bagdazian R. Fourier coding of image boundaries. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1984; 1:102–5. https://doi.org/10.1109/TPAMI.1984.4767482

Cosgriff RL. Identification of shape. Report No 820-11 of the Ohio State University Research Foundation; 1960.

Deore SS. Object Detection using Background Elimination. International Journal of Graphics and Image Processing. 2013; 3:35–9.

Itoh H, Hirata T, Member NI. Machine parts recognition and processing using fourier descriptors. Systems and Computers in Japan. 2007; 20:26–36. https://doi.org/10.1002/scj.4690200803

Jain T, Meenu. Computer vision: An enterprise of industrial automation and integration. Advances in Modeling, Optimization and Computing. 2011; 1:10–5.

Jain T, Meenu. Automation and integration of industries through computer vision systems. International Journal of Information and Computation Technology. 2013; 3:963–70.

Jain T, Meenu, Sardana HK. Mechanical CAD parts recognition for industrial automation. Springer SIST; 2017; 2:1014–9.

Jassim FA. Hybrid image segmentation using discerner cluster in FCM and histogram thresholding. International Journal of Graphics and Image Processing. 2012; 2:241–4.

Kauppinen, Hannu, Seppanen T, Pietikainen M. An experimental comparison of autoregressive and fourier-based descriptors in 2D shape classifications. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1995; 17:201–7. https://doi.org/10.1109/34.368168

Kim, Jaechul, Grauman K. Shape sharing for object segmentation. Computer Vision–ECCV. 2012; 444–58.

Kunttu I. Multiscale fourier descriptor for shape-based image retrieval. IEEE Pattern Recognition. 2004; 2:44–9. https://doi.org/10.1109/ICPR.2004.1334371

Malmberg F, Lindblad J, Sladoge N, Nystrom I. A graph-based framework for sub-pixel image segmentation. Theoretical Computer Science. 2011; 412:1338–49. https://doi.org/10.1016/j.tcs.2010.11.030

Otsu N. A threshold selection method from gray-level histograms. Automatica. 1975; 11:23–7.

Oz C, Ercal F, Demir Z. Signature recognition and verification with ANN. IEEE Transaction on Pattern Analysis and Machine Intelligence. 2004; 2:401–11.

Prasad KV, Gandhi GS, Balaji S. Inexpensive colour image segmentation by using mean shift algorithm and clustering. International Journal of Graphics and Image Processing. 2014; 4:260–6.

Wallace, Timothy P, Wintz PA. An efficient three-dimensional aircraft recognition algorithm using normalized fourier descriptors. Computer Graphics and Image Processing. 1980; 13:99–26. https://doi.org/10.1016/S0146-664X(80)80035-9

Wang B, Chaojian S. A novel fourier descriptor for shape retrieval. Fuzzy Systems and Knowledge Discovery. 2006; 822–25. https://doi.org/10.1007/11881599_101

Ye Z, Ye Y, Yin H, Mohamadian H. Integration of wavelet fusion and adaptive contrast stretching for object recognition with quantitative information assessment. ICGST-GVIP Journal. 2009; 8:5–17.

Zhang D, Guojun L. Shape-based image retrieval using generic fourier descriptor. Signal Processing: Image Communication. 2002; 17:825–48. https://doi.org/10.1016/S0923-5965(02)00084-X

Zhang D, Guojun L. Study and evaluation of different fourier methods for image retrieval. Image and Vision Computing. 2005; 23:33–49. https://doi.org/10.1016/j.imavis.2004.09.001


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