Distribución binomial aplicada a un sistema de clasificación de piezas con tratamiento digital de imágenes
DOI:
https://doi.org/10.15332/iteckne.v9i1.2750Palabras clave:
Distribución binomial, Reconocimiento de bordes, Procesamiento de imágenes, Control de calidad, Inspección óptica automatizadaResumen
Este trabajo describe el desarrollo de un sistema de clasificación de partes para un lote de producción, donde se utiliza un sistema de procesamiento digital de imágenes que permite reconocer las piezas cuando se reúnen o no las características definidas previamente. Para realizar y analizar el control de calidad para el lote de producción, se utiliza una densidad de probabilidad discreta, que se usa frecuentemente en los procesos de control de calidad. La distribución utilizada fue la distribución binomial, ampliamente empleada en procesos de control de calidad en situaciones cuya solución tiene dos posibles resultados, éxito o fracaso, de un parámetro de un conjunto de muestras establecido.
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[2] E.A. Elsayed, and H. Wang, “Bayes and classical estimation of environmental factors for the binomial distribution,” IEEE Trans. on Reliability, Vol. 45, No. 4, pp. 661 – 665, Dec. 1996.
[3] F. Pareschi, R. Rovatti, and G. Setti, “On Statistical Tests for Randomness included in the NIST SP800- 22 test suite and based on the Binomial Distribution,” IEEE Trans.on Information Forensics and Security, Vol. PP, No.99, pp. 1 – 28, Jan. 2012.
[4] A. Koschan, and M. Abidi, “Detection and classification of edges in color images,” IEEE Signal Processing Magazine, Vol. 22 , No 1, pp. 64 – 73, Jan., 2005.
[5] H. Stark, and J. Woods, Probability, Random Processes and Estimation theory for enginners, Ed. Prentice Hall, 1986.
[6] A. Papoulis, Probability, Random Variables and Stochastic Processes, Ed. McGraw Hill, 1984.
[7] J. A. Gubner, Probability and random processes for electrical and computer engineers, Ed. Cambridge University, 2006.
[8] N. Instruments, Image Processing with LabVIEW™ and IMAQ™ Vision, Ed. Prentice Hall, 2003.
[9] National Instruments, “NI-IMAQ Function Reference Manual”, 2000.
[10] National Instruments, “IMAQ Vision for LabVIEW User Manual”, 2000.
[11] B.R. Harris, W. S. Firsty, S. D. Eppinger, J. L. Kirtley, D. P. Clausing, and R. A. Jenkins, “Employing a computer integrated manufacturing methodology for improved product quality and reduced machine downtime,” in Proc. 1990 Second International Conf. on Computer Integrated Manufacturing, pp. 290 – 295, 1990.
[12] C. Li, J. Gao, and F. Chen, “Integrated quality system based on quality workflow for equipment manufacturing enterprise,” 2008 International Conference on Information and Automation, ICIA 2008, pp. 1061 – 1066, Jun., 2008.
[13] R. Hanai, K. Yamazaki, H. Yaguchi, K. Okada, and M. Inaba, “Electric Appliance Parts Classification Using a Measure Combining the Whole Shape and Local Shape Distribution Similarities,” in Proc. 2011 International Conf. on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), pp. 296 – 303, 2011.
[14] T. Yu, and G. Wang, “Application of Computer-Aided Quality Control System” in 2009 Second International Conf. on Intelligent Computation Technology and Automation, ICICTA ‘09, pp. 742 – 745, 2009.
[15] X. Zheng, and D. Chen, “Computer aided quality control system for manufacturing process,” in 2004 Fifth World Congress on Intelligent Control and Automation, WCICA 2004, pp. 2819 – 2823, Jun., 2004.
[16] P. Saengpongpaew, “Quality build-in computer aided process control for the electronic industry,” in Proc. of the 1994 IEEE International Engineering Management Conference, pp. 261 – 266, 1994.
[17] G. Miller, S. Fels, S. Oldridge, “A Conceptual Structure for Computer Vision,” in 2011 Canadian Conference on Computer and Robot Vision (CRV), pp. 168 – 174, 2011.
[18] I. Stobbe, H. Potter, H. Griese, G. Fotheringham, and H. Reichl, “Quality challenges of reused components,” in Proc. of 2004 International IEEE Conference on the Asian Green Electronics, AGEC, pp. 218 – 225, 2004.
[19] I. Stobbe, H. Potter, H. Griese, L. Stobbe, and H. Reichl, “Quality assured disassembly of electronic components for reuse,” in 2002 IEEE International Symposium on Electronics and the Environment, pp. 299 – 305, 2002.
[20] B. A. Lantz, R.E. Depp, and B. P. McNicholl, “The DESC quality program for electronic parts,” in Proc. of the IEEE 1992 National Aerospace and Electronics Conf., NAECON 1992, pp. 1037 – 1042, Vol.3, 1992.
[21] M. Lazzaroni, “A tool for quality controls in industrial process,” in IEEE Instrumentation and Measurement Technology Conference, I2MTC ‘09, pp. 68 – 73, May., 2009.
[22] H. Zhao, J. Cheng, and J. Jin, “NI vision based automatic optical inspection (AOI) for surface mount devices: Devices and method,” International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2009, pp. 356 – 360, Sept., 2009.
[23] H. Xie, Y. Kuang, and X. Zhang, “A high speed AOI algorithm for chip component based on image difference,” in the International Conference on Information and Automation, 2009. ICIA ‘09, pp. 969 – 974, Jun., 2009.
[24] F. Wu, X. Zhang, Y. Kuan, and Z. He, “An AOI algorithm for PCB based on feature extraction,” 7th World Congress on Intelligent Control and Automation, WCICA 2008, pp. 240 – 247, Jun., 2008.
[25] J. Kolibal, and D. Howard, “Stochastic Interpolation: A Probabilistic View,”Symposium on Bio-inspired Learning and Intelligent Systems for Security, BLISS ‘08, pp. 129 – 135, 2008.
[26] S. Yuan, L. Liu, Z. Wang, N. Xi, Y. Wang, Z. Dong, and Z. Wang, “A probabilistic approach for on-line positioning in nano manipulations,” in 8th World Congress on Intelligent Control and Automation (WCICA), pp. 450 – 455, Jun., 2010.
[27] S. Lin, S. Y. Kung, and L. Lin, “A probabilistic DBNN with applications to sensor fusion and object recognition,” in Proc. of the 1995 IEEE Workshop Neural Networks for Signal Processing, pp. 333 – 342, 1995.
[28] S. Hu, “Optimum truncated sequential test of binomial distribution,” 9th International Conference on Reliability, Maintainability and Safety (ICRMS), pp. 293 – 298, Dec., 1996.
[29] G.V. Weinberg, “Bit error rate approximations using Poisson and negative binomial sampling distributions,” Electronics Letters, Vol. 44, No. 3, pp. 217 – 219, Jan., 2008.
[30] K. S.Sim, L. W. Thong, M.A. Lai, and C.P.Tso, ”Enhancement of optical images using hybrid edge detection technique,” in Conf. on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2009, pp. 186 – 191, Jul., 2009.
[31] J. Manikandan, B. Venkataramani, and M. Jayachandran, “Evaluation of Edge Detection Techniques towards Implementation of Automatic Target Recognition,” International Conference on Computational Intelligence and Multimedia Applications, ICCIMA. 2007, Vol. 2, pp. 441 – 445, 2007.
[32] Z. Musoromy, F. Bensaali, S. Ramalingam, and G. Pissanidis, “Comparison of real-time DSP-based edge detection techniques for license plate detection,” 2010 Sixth International Conf. on Information Assurance and Security (IAS), pp. 323 – 328, 2010.
[33] R. Marmo, and L. Lombardi, “Road bridge sign detection and classification,” IEEE Intelligent Transportation Systems Conf., ITSC ‘06,pp. 823 – 826, Sept., 2006.
[34] C. Cheong, “Design of lane detection system based on color classification and edge clustering,” 2011 3rd Asia Symposium on Quality Electronic Design (ASQED), pp. 266 – 271, 2011.
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Publicado
2014-11-26
Cómo citar
Pardo Beainy, C. E., Gutiérrez Cáceres, Édgar A., Jiménez López, F. R., & Sosa Quintero, L. F. (2014). Distribución binomial aplicada a un sistema de clasificación de piezas con tratamiento digital de imágenes. ITECKNE, 9(1), 90–98. https://doi.org/10.15332/iteckne.v9i1.2750
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