CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Testing and Calibration of Temperature Gauges using Webcam based Non-Invasive Technique

by Zainul Abdin Jaffery, Ashwani Kumar Dubey
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 79 - Number 1
Year of Publication: 2013
Authors: Zainul Abdin Jaffery, Ashwani Kumar Dubey
10.5120/13709-1462

Zainul Abdin Jaffery, Ashwani Kumar Dubey . Testing and Calibration of Temperature Gauges using Webcam based Non-Invasive Technique. International Journal of Computer Applications. 79, 1 ( October 2013), 41-47. DOI=10.5120/13709-1462

@article{ 10.5120/13709-1462,
author = { Zainul Abdin Jaffery, Ashwani Kumar Dubey },
title = { Testing and Calibration of Temperature Gauges using Webcam based Non-Invasive Technique },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 79 },
number = { 1 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 41-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume79/number1/13709-1462/ },
doi = { 10.5120/13709-1462 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:51:55.866024+05:30
%A Zainul Abdin Jaffery
%A Ashwani Kumar Dubey
%T Testing and Calibration of Temperature Gauges using Webcam based Non-Invasive Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 79
%N 1
%P 41-47
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a contact less testing and calibration system is developed for temperature gauges. The system captures the images of temperature gauge at a regular interval, detects the desired region of interest using algorithms and segments the needle for further processing. The segmented image properties are calculated and matched with the standard data to indentify the indicated value. The system is capable enough to generate alarm and emboss 'defective sample' on the gauge under test. In the field manufacturing, non invasive visual inspection systems are replacing the need of human inspector to prevent the inclusion of incorrect parts or to check the quality of goods.

References
  1. B. R. Suresh, , R. A. Fundakowski, T. S. Levitt, and J. E. Overland, "A Real-Time Automated Visual Inspection System for Hot Steel Slabs," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, no. 6, pp. 563-572, June 1983.
  2. F. Pernkopf and P. O'Leary, "Image acquisition techniques for automatic visual inspection of metallic surfaces," NDT&E International, vol. 36, no. 8, pp. 609-617, Dec. 2003.
  3. A. Gonzalez, M. A. Garrido, D. F. Llorca, M. Gavilan, J. P. Fernandez, P. F. Alcantarilla, I. Parra, F. Herranz, L. M. Bergasa, M. A. Sotelo, and P. R. Toro, "Automatic traffic signs and panels inspection system using computer vision," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 485-499, Jun. 2011.
  4. F. Marino, A. Distante, P. L. Mazzeo, and E. Stella, "A Real-Time Visual Inspection System for Railway Maintenance: Automatic Hexagonal-Headed Bolts Detection," IEEE Transactions on Systems, Man and Cybernetics—Part C: Applications and Reviews, vol. 37, no. 3, pp. 418-428, May 2007.
  5. R. Sablatnig, "Increasing Flexibility for Automatic Visual Inspection: The General Analysis Graph," Machine Vision and Applications, vol. 12, no. 4, pp. 158–169, Dec. 2000.
  6. H. D. Lin, "Automated Defect Inspection of Light-Emitting Diode Chips using Neural Network and Statistical Approaches," Expert Systems with Applications, vol. 36, no. 1, pp. 219-226, Jan. 2009.
  7. M. Shirvaikar, "Trends in automated visual inspection," Journal of Real-Time Image Processing, vol. 1, no. 1, pp. 41-43, Oct. 2006.
  8. H. C. Garcia, J. R. Villalobos, R. Pang, and G. C. Runger, "A novel feature selection methodology for automated inspection systems," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 7, pp. 1338-1344, Jul. 2009.
  9. G. Ming and X. Yangsheng, "An Intelligent Online Monitoring and Diagnostic System for Manufacturing Automation,' IEEE Transactions on Automation Science and Engineering, vol. 5, no. 1, pp. 127-139, Jan. 2008.
  10. I. Edinbarough, R. Balderas, and S. Bose, "A Vision and Robot Based On-Line Inspection Monitoring System for Electronic Manufacturing. Computers in Industry," Machine Vision Special Issue, vol. 56, no. 8-9, pp. 986-996, Dec. 2005.
  11. G. L. Foresti, "Visual Inspection of Sea Bottom Structures by an Autonomous Underwater Vehicle," IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, vol. 31, no. 5, pp. 691-705, Oct. 2001.
  12. D. Petkovic and E. B. Hinkle, "A Rule-Based System for Verifying Engineering Specifications in Industrial Visual Inspection Applications," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 9, no. 2, pp. 306-311, Feb. 1987.
  13. Ž. Špiclin, M. Bukovec, F. Pernuš, and B. Likar, "Image Registration for Visual Inspection of Imprinted Pharmaceutical Tablets," Machine Vision and Applications, vol. 22, no. 1, pp. 197-206, Jan. 2011.
  14. X. Zhang, Y. Ding, L. Yan-yun, S. Ai-ye, and L. Rui-yu, "A Vision Inspection System for the Surface Defects of Strongly Reflected Metal Based on Multi-Class SVM," Expert Systems with Applications, vol. 38, no. 5, pp. 5930-5939, May 2011.
  15. H. Jia, F. Xi, A. Ghasempoor, and A. Dawoud, "A Tolerance Method for Industrial Image based Inspection," Intl. J. Advanced Manufacturing Technology, vol. 43, no. 11-12, pp. 1223-1234, Aug. 2009.
  16. N. R. Pal and S. K. Pal, "A review on image segmentation techniques," Pattern Recognition, vol. 26, no. 9, pp. 1277–1294, Sep. 1993
  17. J. Melendez, M. A. Garcia, D. Puig, and M. Petrou, "Unsupervised texture-based image segmentation through pattern discovery", Computer Vision and Image Understanding, vol. 115, no. 8, pp. 1121-1133, Aug. 2011.
  18. H. Mobahi, S. R. Rao, A. Y. Yang, S. S. Sastry, and Y. Ma, "Segmentation of natural images by texture and boundary compression," International Journal of Computer Vision, vol. 95,no. 1, pp. 86-98, Oct. 2011.
  19. Z A Jaffery and A. K. Dubey, "Architecture of non-invasive real time visual monitoring system for dial type measuring instrument," IEEE Sensors Journal, vol. 13, no. 4, pp. 1236-1244, Apr. 2013.
  20. K. W. Ko and H. S. Cho, "Solder Joints Inspection using a Neural Network and Fuzzy Rule-Based Classification Method," IEEE Transactions on Electronics Packaging Manufacturing, vol. 23, no. 2, pp. 93-103, Apr. 2000.
  21. K. L. Chung, Z. W. Lin, S. T. Huang, Y. H. Huang, and H. Y. M. Liao, "New orientation-based elimination approach for accurate line-detection," Pattern Recognition Letters, vol. 31, no. 1, pp. 11-19, Jan. 2010.
  22. W. Krzanowski, "Between-groups comparison of principal components," Journal of the American Statistical Association, vol. 74, no. 367, pp. 703-707, Sep. , 1979.
  23. K. Yang, and C. Shahabi, "A PCA-based similarity measure for multivariate time series," in Proc. 2nd ACM Int. Workshop on Multimedia Databases, Washington, 2004, pp. 65-74.
Index Terms

Computer Science
Information Sciences

Keywords

AVIS Image Acquisition NITCS ROI Threshold.

Powered by PhDFocusTM