Abstract
It is necessary to monitor the grain size characteristics of particles at production site to control the production equipment, for the assurance of product quality. In this respect, prior research finds that it is critical to evaluate the accuracy of tiny particles since the current practice indicates that the existing methods illustrate multiple shortcomings including large measure error, low accuracy and poor repeatability. Therefore, to improve the accuracy of particles, monitoring this study proposed a calibration method based on SVR algorithm to predict the accurate pixel size of the particles. Results revealed that high-precision pixel mapping of the sensitive area transforms the pixel mapping of the particle image closer to the actual size and improves the measurement precision of the whole system.
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References
Xu GTDFF (2012) Camera calibration under small field of view. Chin J Lasers 8:34
Tsatsoulis C, Fu K-s (1985) A computer vision system for assembly inspection. In: Intelligent robots and computer vision, vol 521, pp 352–357. International Society for Optics and Photonics
Sternberg SR, Sternberg ES (1983) Industrial inspection by morphological virtual gauging. In: Proceedings of IEEE computer society workshop on computer architecture for pattern analysis and image database management, pp 237–47
Li X, Zhang T, Zhang S, Nan B, Guo X (2008) Small objects dimension measure and three-dimension reconstruction system based on laser triangulation. Opt Instrum 30(6):21–26
Mills R (1991) Development of a line-scan camera for 2D high accuracy measurement. Machine Vis 267–277
Angrisani L, Daponte P, Pietrosanto A, Liguori C (1999) An image-based measurement system for the characterisation of automotive gaskets. Measurement 25(3):169–181
Chen M-C (2002) Roundness measurements for discontinuous perimeters via machine visions. Comput Indus 47(2):185–197
Ni KS, Nguyen TQ (2007) Image superresolution using support vector regression. IEEE Trans Image Process 16(6):1596–1610
Smola AJ, Schölkopf B (1998) On a kernel-based method for pattern recognition, regression, approximation, and operator inversion. Algorithmica 22(1–2):211–231
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Han H, Dang J, Ren E (2012) Comparative study of two uncertain support vector machines. In: 2012 IEEE fifth international conference on advanced computational intelligence (ICACI), pp. 388–390. IEEE
Okujeni A, Van der Linden S, Jakimow B, Rabe A, Verrelst J, Hostert P (2014) A comparison of advanced regression algorithms for quantifying urban land cover. Remote Sens 6(7):6324–6346
Zhang W, Du Y, Yoshida T, Wang Q, Li X (2018) SamEn-SVR: using sample entropy and support vector regression for bug number prediction. IET Softw 12(3):183–189
任俊, 胡晓峰, and 李宁 (2018) 基于 SDA 与 SVR 混合模型的迁移学习预测算法. 计算机科学 45(1): 280–284
Utkin LV, Coolen FPA (2018) A robust weighted SVR-based software reliability growth model. Reliab Eng Syst Saf 176:93–101
Amraei S, Mehdizadeh SA, Sallary S (2017) Application of computer vision and support vector regression for weight prediction of live broiler chicken. Eng Agric Environ Food 10(4):266–271
Mukherjee A, Ramachandran P (2018) Prediction of GWL with the help of GRACE TWS for unevenly spaced time series data in India: analysis of comparative performances of SVR, ANN and LRM. J Hydrol 558:647–658
Liu Y, Liu Y (2010) Incremental learning method of least squares support vector machine. In: 2010 international conference on intelligent computation technology and automation, vol 2, pp 529–532. IEEE
Huang C-L, Tsai C-Y (2009) A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting. Exp Syst Appl 36(2):1529–1539
Castro-Neto M, Jeong Y-S, Jeong M-K, Han LD (2009) Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Exp Syst Appl 36(3):6164–6173
王杰, 刚轶金, 石成辉 (2008) SVM—RBF 网络在混沌时间序列预测中的应用. 微计算机信息 24(33):136–137
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Jing, H., Yadav, A., Khan, A., Yadav, D. (2021). A High-Precision Pixel Mapping Method for Image-Sensitive Areas Based on SVR. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-6584-7_4
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DOI: https://doi.org/10.1007/978-981-15-6584-7_4
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