International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 8, No. 1, 8 Jan. 2016
Cover page and Table of Contents: PDF (size: 726KB)
Automated Visual Inspection: Position Identification of Object for Industrial Robot Application based on Color and Shape
Full Text (PDF, 726KB), PP.9-17
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Author(s)
Muralindran Mariappan
1,*
Tony Chua Tung Ming
1
Manimehala Nadarajan
1
Index Terms
Color, shape, position, RGB color space, winner take all approach, compactness, centroid
Abstract
Inspection task is traditionally carried out by human. However, Automated Visual Inspection (AVI) has gradually become more popular than human inspection due to the advantageous in the aspect of high precision and short processing time. Therefore, this paper proposed a system which identifies the object’s position for industrial robot based on colors and shapes where, red, green, blue and circle, square, triangle are recognizable. The proposed system is capable to identify the object’s position in three modes, either based on color, shape or both color and shape of the desired objects. During the image processing, RGB color space is utilized by the proposed system while winner take all approach is used to classify the color of the object through the evaluation of the pixel’s intensity value of the R, G and B channel. Meanwhile, the shapes and position of the objects are determined based on the compactness and the centroid of the region respectively. Camera settings, such as brightness, contrast and exposure is another important factor which can affect the performance of the proposed system. Lastly, a Graphical User Interface was developed. The experimental result shows that the developed system is highly efficient when implemented in the selected database.
Cite This Paper
Muralindran Mariappan, Tony Chua Tung Ming, Manimehala Nadarajan, "Automated Visual Inspection: Position Identification of Object for Industrial Robot Application based on Color and Shape", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.1, pp.9-17, 2016. DOI:10.5815/ijisa.2016.01.02
Reference
[1]S. C. Wang, Human Reliability in Visual Inspection. Quality. 1975. 14(9):24-35.
[2]E. N. Malamas, E. G. M. Petrakis, M. Zervakis, L. Petit & J. Legat, “A survey on industrial vision systems, applications and tools，” Image and Vision Computing, vol.2, issue 2, pp. 171-188, December 2002.
[3]J. Moore, G. Hobson, G. Waldman & J. Wootton, “A Real-Time Visual Inspection System for Automated Prescription Dispensing Systems,” IEEE International Conference on Man and Cybernetics, vol.1, pp.577-582, October 08-11, 2000.
[4]C. Anthony, C. Benjamin, M. William, J. T. Camillo & L. Maxim, “Perception and Motion Planning for Pick-and-Place of Dynamic Objects,” International Conference on Intelligent Robots and Systems (IROS), pp. 816-823, November 2013.
[5]G. Fei, D. Ji-CAir & Z. Dong-bo, “A Connected Domain Analysis Based on Color Localization Method and Its Implementation in Embedded Robot System”, International Journal Image, Graphic and Image Processing, Volume 5, pp. 37-43, 2011.
[6]T. Murai, M. Morimoto & K. Fujii, “A Visual Inspection System for Medical Capsules,” IEEE International Conference on Systems, Man, and Cybernetics, pp.1433-1437, October 14-17, 2012.
[7]K. Kiratiratanapruk & W. Sinthupinyo, “Color and Texture for Com Seed Classification by Machine Vision,” International Symposium on Intelligent Signal Processing and Communication System (ISSPCS), December 07-09, 2011.
[8]H. H. Wu, X. M. Zhang & S. L. Hong, “A Visual Inspection System for Surface Mounted Components Based on Color Features,” Proceedings of the IEEE International Conference on Information and Automation, China, pp.571-576, June 22-24, 2009.
[9]S. Yogitha & P. Sakthivel, “A Distributed Computer Machine Vision System for Automated Inspection and Grading of Fruits,” International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Coimbatore, pp.1-4, March 06-08, 2014.
[10]D. J. Lee, K. Archibald, & G. M. Xiong, “Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping,” IEEE Trans. Automation Science and Engineering, vol.8, issue 2, pp.292-302, April 2011.
[11]C. H. Ling & C. H. Su, “Color Image Segmentation Using the Relative Values of RGB,” Proceedings of the 9th WSEAS International Conference on Applications of Computer Engineering, pp.46-51, 2010.
[12]H. B. Liu, Y. M. Wang & Y. J. Dong, “Fast Recognition Based on Color Image Segmentation in Mobile Robot,” Proceedings of the Third International Symposium on Computer Science and Computational Technology, China, pp.1-4, August 14-15, 2010.
[13]H. Altun, R. Sinekli, U. Tekbas, F. Karakaya & M. Peker, “An Efficient Color Detection in RGB Space Using Hierarchical Neural Network Structure,” International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp.154-158, June 2011.
[14]S. Rege, R. Memane, M. Phatak & P. Agarwal, “2D geometric shape and color recognition using digital image processing,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 2, issue 6, pp.2479-2489, June 2013.
[15]M. F. Zakaria, H. S. Choon & S. A. Suandi, “Object Shape Recognition in Image for Machine Vision Application,” vol. 4, no. 1, pp.76-80, October 5, 2014.
[16]T. Barbu, “Automatic Unsupervised Shape Recognition Technique using Moment Invariants,” 15th International Conference on System Theory, Control, and Computing (ICSTCC), pp.1-4, October 14-16, 2011.
[17]S. Xu, “Robust traffic sign shape recognition using geometric matching,” IET Intelligent Transport Systems, vol. 3, issue 1, pp.10-18, March 2009.
[18]F. A. Cheikh, A. Quddus & M. Gabbouj, “Shape Recognition based on Wavelet-Transform Modulus Maxima,” 7th IEEE International Conference on Electronics, Circuits and Systems (ICECS), vol.1, pp.461-464, December 2000.
[19]S. Ajay Kumar, S. V. P, T. Shamik, B. Sangappa, “Wavelet Based Histogram of Oriented Gradients Feature Descriptors for Classification of Partially Occluded Objects”, International Journal of Intelligent Systems and Applications, Vol 3, pp. 54- 61, 2015.
[20]K. Saka, P. Santi & K. V. Vijaya, “A Color- Texture Based Segmentation Method to Extract Objects from Background”, International Journal Image, Graphic and Image Processing, Vol. 3, pp- 19- 25, 2013.