[1]. ISO, (1994), ISO 8402, Quality Management and Quality Assurance – Vocabulary. International Organization for Standardization, Geneva, Switzerland.
[2]. Lomas, S. (2004), QAQC Program. General Discussion. AMEC Internal document.
[3]. Sketchley, D. (1999), Case history guidelines for establishing sampling protocols and monitoring quality control. Proceedings of CIMM Annual General Meeting Symposium on Quality Control of Resource Estimations: an ISO Perspective.
[4]. Chatterjee, S. (2006). Geostatistical and image based quality control models for Indian mineral industry. Unpublished Ph.D. Thesis dissertation, IIT Kharagour, India, 272 pp.
[5]. Jimenez, A.R., Jain, A.K., Ceres, R., Pons, J.L. (2001). Automatic fruit recognition: a survey and new results using range/attenuation images. Pattern Recognition 32. 10: 1719–1736.
[6]. Miller, W., Jaskot, J., McCoy, B., and Schiller, E. (1996). A distributed system for 100% inspection of aluminum sheet products. International Conference on Signal Processing Applications and Technology (ICSPAT’96).
[7]. Khandogin, I., Kummert, A., Maiwald, D. (2000). DSP algorithms for the automatic inspection of fixing devices of railroad lines. International Conference on Signal Processing Applications and Technology (ICSPAT’98).
[8]. Hou, T. H., Pern, M. D. (2001). A computer vision-based shape-classification system using image projection and a neural network. International Journal of Advanced Manufacturing Technology 15: 843–850.
[9]. Yu, H. (2003). Development of vision-based Inferential Sensors for Process Monitoring and control. Ph.D. Thesis, McMaster University, Department of Chemical Engineering, Hamilton, Ontario, Canada. [10] Janannathan, S. (1997). Automatic inspection of wave soldered joints using neural networks. Journal of Manufacturing Systems 16:389–398.
[10]. Oyeleye, O., Lehtihet, E.A. (2000). A classification algorithm and optimal feature selection methodology for automated solder joint inspection. Journal of Manufacturing Systems 17: 251–262.
[11]. Oestreich, J. Tolley, W. and Rice, D. (1995). The development of a color sensor system to measure mineral compositions. Minerals Engineering 1.2:31-39.
[12]. Perez, C., Casali, A., Gonzalez, G., Vallebuona, G., and Vargas, R. (2000). Lithological composition sensor based on digital image feature extraction, genetic selection of features and neural classification. In Proc. of the Int. Con. on Information Intelligence & Systems, IEEE, Bethesda, pp. 236-241.
[13]. Casali, A., Gonzalez, G., Vallebuona, G., Perezq C. and Vargas, R. (2001). Grind ability Soft-Sensors Based on Lithological Composition and On-Line Measurements. Minerals Engineering 14.7:689-700.
[14]. Aydemir, S., Keskin, S., Drees, L.R. (2004). Quantification soil features using digital image processing (DIP) techniques. Geoderma 119.1:1-8.
[15]. Marmo, R., Amodio, S. (2005). Textural identification of carbonate rocks by image processing and neural network: Methodology proposal and examples. Computers & Geosciences 31:649–659.
[16]. Donskoi, E., Clout, J.M.F. (2005). Recognition – a specialized software package for iron ore characterization. In Iron Ore 2005. Aus. IMM, Fremantle pp. 203–211.
[17]. Singh, V., Singh T.N., Singh V. (2010). Image processing applications for customized mining and ore classification. Int. J. Arab Geosci. DOI: 10.1007/s12517-010-0125-2.[19] Management and Planning Organization of Iran (MPOI). (1990). Iranian Concrete Code (ABA): Technical note # 120.
[18]. Khorram F. Memarian H. Tokhmechi B. Soltanian Zadeh H. (2012) Lithological Classification Using Image Processing Technique, SME Annual Meeting and Exhibition, Washington, USA, 4 pages.
[19]. Khorram F. Memarian H. Tokhmechi B. Soltanian Zadeh H. (2012) Limestone Chemical Component Estimation using Image Processing & Pattern Recognition Techniques, Journal of Mining and Environment, Vol. 2, No. 2, pp. 49-58.