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Online Classification and Measurement of Pencils using Image Processing Techniques

by Santhosh K V, Bhagya R Navada
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 96 - Number 4
Year of Publication: 2014
Authors: Santhosh K V, Bhagya R Navada
10.5120/16782-6366

Santhosh K V, Bhagya R Navada . Online Classification and Measurement of Pencils using Image Processing Techniques. International Journal of Computer Applications. 96, 4 ( June 2014), 25-30. DOI=10.5120/16782-6366

@article{ 10.5120/16782-6366,
author = { Santhosh K V, Bhagya R Navada },
title = { Online Classification and Measurement of Pencils using Image Processing Techniques },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 96 },
number = { 4 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 25-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume96/number4/16782-6366/ },
doi = { 10.5120/16782-6366 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:20:52.490944+05:30
%A Santhosh K V
%A Bhagya R Navada
%T Online Classification and Measurement of Pencils using Image Processing Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 96
%N 4
%P 25-30
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes an automated method for segregating and counting the different colored pencils by non-contact method. The proposed work is carried out in LabVIEW platform based on the image processing techniques. This work consists of two parts, firstly classification of pencils and secondly counting the number of pencils. The proposed work is implemented on a conveyor running continuously, at a defined speed done without halting the conveyor. The images acquired using camera are processed using support vector machine to classify pencils based on color, and a counting algorithm is incorporated to find the specific number of each colored pencils. Results on testing showed the successful achievement of set objectives.

References
  1. Mira Park, Jesse S. Jin, Sherlock L. Au, Suhuai Luo, and Yue Cui. 2009. Automated Defect Inspection Systems by Pattern Recognition. International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 2, No. 2, (June 2009), 31-42.
  2. Ali Keyvani, N. and KyleStrom. 2013. A fully-automated image processing technique to improve measurement of suspended particles and flocs by removing out-of-focus objects. Computers & Geosciences, Elsevier, Vol. 52, (March 2013), 189–198.
  3. Barbedo, J. G. A. 2012. A Review on Methods for Automatic Counting of Objects in Digital Images. Journal of IEEE Latin America Transactions, Elsevier Vol. 10, (September 2012), 2112-2124.
  4. John Kennedy, Schettino de Souzaa, Marcos Antonio da Silva Pintoa, Pedro Gabrielle Vieirab, Jerome Barona, B. D. and Carlos Julio Tierra-Criolloa. 2013. An open-source, FireWire camera-based Labview-controlled image acquisition system for automated, dynamic pupillometry and blink detection. Computer methods and programs in biomedicine, Elsevier, Vol. 112, (December 2013), 607-623.
  5. Kavitha, K. and Arivazhagan, S. 2010. A Novel Feature Derivation Technique for SVM based Hyper Spectral Image Classification. International Journal of Computer Applications, Vol. 1, No. 15, (2010), 25-31.
  6. Monika Bhatnagar and Prashant Kumar Singh. 2014. an Efficient Method of Image Segmentation for Harvest Time Identification", International Journal of Computer Applications, Volume 87, No. 7, (February 2014), 31-34.
  7. James R. Wooten, Filip To, S. D. Igathinathane, C. and Pordesimo, L. O. 2011. Discrimination of bark from wood chips through texture analysis by image processing. Computers and Electronics in Agriculture, Elsevier, Vol. 79, (2011), 13–19.
  8. Lingfeng Duan, Wanneng Yang, Kun Bi, Shangbin Chen, and Qingming Luo, Qian Liu. 2011. Fast discrimination and counting of filled/unfilled rice spikelets based on bi-modal imaging. Computers and Electronics in Agriculture, Vol. 75, (2011), 196–203.
  9. Zainul Abdin Jaffery, Zaheeruddin, and Laxman Singh. 2013. Performance Analysis of Image Segmentation Methods for the Detection of Masses in Mammograms. International Journal of Computer Applications, Vol. 82, No2, (November 2013), 44-50.
  10. Weyricha, M. Wanga, Y. Winkela, J. and Laurowskib, M. 2012. High Speed Vision Based Automatic Inspection and Path Planning for Processing Conveyed Objects", Procedia CIRP, Vol. 3, Elsevier, (2012), 442 – 447.
  11. Stephan R. Harmsen and Nicole J. J. P. Koenderink. 2009. Multi-target tracking for flower counting using adaptive motion models. Computers and electronics in agriculture, Elsevier, Vol. 65, (2009), 7–18.
  12. Pushpendra Kumar, Rekha Pandit, and Vineet Richhariya. 2014. Retinal Image Segmentation by using Gradient Descent. International Journal of Computer Applications, Vol. 86, No 10, (January 2014), 1-7.
  13. Xue Wei, Son Lam Phung, and Abdesselam Bouzerdoum. 2014. Object segmentation and classification using 3-D range camera. Journal of Visual Communication and Image Representation, Vol. 25, (January 2014), 74–85.
  14. Shreyasi Dattaa, Anwesha Khasnobishb, Amit Konara, Tibarewalab, D. N. and Janarthanan, R. 2013. Performance Analysis of Object Shape Classification and Matching from Tactile Images using Wavelet Energy Features. Procedia Technology, Vol. 10, (2013), 805-812.
  15. Pattan Prakash, Mytri, V. D. and Hiremath, P. S. 2010. Active Contour Multigrid Model for Segmentation and Automatic Quantification of Material Phases of Cast Iron. International Journal of Computer Applications, Vol. 9, No. 4, (November 2010),32-37.
  16. Thomas Deselaers, Georg Heigold, and Hermann Ney. 2010. Object classification by fusing SVMs and Gaussian mixtures. Pattern Recognition, Elsevier, Vol. 43, (2010), 2476–2484.
  17. Sugata Banerji, N. Atreyee Sinha, and Chengjun Liu. 2013. New image descriptors based on color, texture, shape, and wavelets for object and scene image classification. Neurocomputing, Vol. 117, (2013), 173–185.
  18. Amit R. Chavan, A. R. Shastri, R. K. Shastri, S. B. Deosarkar, 2013. Counting of Frozen Semen Straws using Image Processing. In Proceedings of third International Conference on Advances in Computing and Communications, India.
  19. Guruprasad Somasundaram, Vassilios Morellas, and Nikolaos Papanikolopoulos. 2009. Counting Pedestrians and Bicycles in Traffic Scenes. In Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, USA.
  20. Pornpanomchai, C. Liamsanguan, and Vannakosit, V. 2008. Vehicle Detection and Counting From A Video Frame. In Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong.
  21. Joshi, K. V. and Chauhan, N. C. 2011. Edge Detection and Template Matching Approaches for Human Ear Detection", International Journal of Computer Applications, special Issue on Intelligent Systems and Data Processing, (2011), 50-55,
  22. Ballard, D. H. 1981. Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognition, Vol. 13, (1981), 111-122.
  23. Ballard, D. H. and Brown C, 1982. Computer Vision. Prentice Hall.
  24. Burns, J. B. Weiss, R. S. and Riseman, E. M. 1993. View Variation of Point-Set and Line- Segment Features. IEEE trans. Pattern Analysis and Machine Intelligence, Vol. 15, No. 1, (1993), 51-68.
  25. Cosgriff, R. L. 1960. Identification of Shape. Ohio State University Research Foundation, Columbus, Rep. 820-11.
  26. Cootes, T. F. and Taylor, C. J. 1992, Active Shape Models - 'Smart Snakes'. Department of Medical Biophysics, University of Manchester.
  27. Nancy M. Salem and Asoke K. Nandi. 2007. Novel and Adaptive Contribution of the Red Channel in Pre-processing of Colour Fundus Images. Journal of the Franklin Institute Volume 344, (2007), 243-256.
  28. Kurdthongmee, W. 2008. Colour Classification of Rubberwood boards for Finger joint Manufacturing using a SOM Neural Network and Image Processing. Journal of Computers and Electronics in Agriculture, Vol. 64, (2008), 85-92.
  29. Zhang Xuegong. 2000. Statistical learning theory and support vector machines. Journal Acta Automatica Sinica, Vol. 26, No. 1, (2000), 32-42.
  30. Suykens, J. A. K. and Vandewalle, J. 1999. Least Squares Support Vector Machine Classifiers. Kluwer Academic Publisher, Netherlands.
  31. SchÄokopf, B. and Smola, A. 2002. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. MIT Press.
Index Terms

Computer Science
Information Sciences

Keywords

Automation Classification Image Processing Support Vector Machine (SVM)

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