Abstract
The image processing has widely delved into mining activities for years. The image processing is actually a simulation of the human eye which is able to distinguish differences. This agent empowers any neural network or intelligent system to detect valuable minerals of any given gangue. Similarly, morphology is defined as an extensive set of image processing algorithms that processes the images based on geometric shapes. Morphological operations refer to insertion of a structure element to an input image in order to create an equal output image. In the morphological operations, the value of each pixel in the output image is taken into account in relation to a corresponding pixel in the input image along with its neighbors. Having selected a local size and shape, you may run morphological operations which are sensitive to certain shapes in the structure of input images. Since it has been argued that dilation and erosion are the most basic morphological operations, they are discussed in this article. The texture features of images have been used in image processing. These features, which are called Haralick texture features, are characterized with specific definitions and matrix formula. In this paper, it has been attempted to examine the minerals”™ ore grade using the emotional network and image processing techniques. This method is fundamentally based on emotional learning and temporal difference learning. Besides, it is characterized with a fuzzy-neural structure.
References
Haralick, R.M., Shanmugam, K. and Dinstein, I. (1973), Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, 6, 610-621.
Singh, V., Rao, S.M., (2005), Application of image processing and radial basis neural network techniques for ore sorting and ore classification, Mineral Engineering, 18 (15), 1412–1420.
Al-Thyabat S., Miles N. J., Koh T. S., (2007), Estimation of the size distribution of particles moving on a conveyor belt. Miner. Eng., 20((1) :):72–83.
Hatami, M., Ameri Siahooei, E. (2013). Examines criteria applicable in the optimal location new cities, with approach for sustainable urban development. Middle-East Journal of Scientific Research, 14 (5), 734-743.
TASDEMIR A. OZDAG H. ONAL G., (2011), Image analysis of narrow size fractions obtained by sieve analysis - an evaluation by log-normal distribution and shape factors, Physicochem. Probl. Miner. Process. 46 (2011) 95-106.
Xia W,Yang J,Zhao Y,et al., (2012), Improving floatability of Taixi anthracite coal of mild oxidation by grinding, Physicochemical Problems of Mineral Processing,2012,48(2):393-401.
Xia et al., Xi a W., Ya n g J., Z h u B., (2012), Flotation of oxidized coal dry-ground with collector, Powder Technol, 228, 324–326.
Andersson. T., Thurley M.J., Carlson J.E., (2012), A machine vision system for estimationof size distributions by weight of limestone particles, Minerals Eng. 25 38–46.
Zhang Z., Yang j, Ding L. and Zhao Y., (2012), An improved estimation of coal particle mass using image analysis, Journal of Powder Technology, vol.229.pp 178-184.
Shafieardekani, M., Hatami, M. (2013). Forecasting Land Use Change in suburb by using Time series and Spatial Approach; Evidence from Intermediate Cities of Iran. European Journal of Scientific Research, 116 (2), 199-208.
Zhao Y., (2013), Multi-level denoising and enhancement method based on wavelet transform for mine monitoring. International Journal of Mining Science and Technology 23, 163–166.
Yang H., Liu Y., Xie H., Xu Y., Sun Q., Wang, S., (2013), Integrative method in lithofacies characteristics and 3D velocity volume of the Permian igneous rocks in H area, Tarim Basin. International Journal of Mining Science and Technology 23, 167–172.
Tootoonchy H, Hashemi HH, (2013), Fuzzy logic modeling and controller design for a fluidized catalytic cracking unit. In: Proceedings of the world congress on engineering and computer science 2013, WCECS 2013, 23–25 Oct 2013. Lecture notes in engineering and computer science, San Francisco, USA, pp 982–987.
Bringmann LF, Pe ML, Vissers N, Ceulemans E, Borsboom D, Vanpaemel W, Tuerlinckx F, Kuppens P, (2016), Assessing temporal emotion dynamics using networks. Assessment 23(4):425–435.
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