The best results are received using watershed transform which uses gray scale images which means that color information has not been used to improve performance. The limitations of the PSD should be tested with more particle size distribution classes with different variances inside the size distributions. Different kind of segmentation supposed to be tested for example using clustering of superpixels. The mapping of segmented areas to the ground truth of the PSD needs more research and more data. More labeled classes images with ground truth needs to be done to achieve better performance of the system.
By this method is should be tested how accurately it is possible to describe the material using tens or hundreds of images.
9 CONCLUSIONS
Machine vision has become useful tool for RDF manufacturing process. Hyperpectral, NIR and X-ray cameras are already used for recycling and increasing the biomass frac-tion of RDF end product. NIR and X-ray fluorescence spectrometry is used to determine the composition RDF. Grayscale and RGB cameras have been used for determining the particle size of RDF. There are also machine solutions to characterize RDF or SRF in lab-oratory conditions by sampling. However, in this thesis on-line machine vision systems are study to characterize waste fuels. Characterize in realtime in the middle of the pro-cess makes the task more challenging and possibility to adjust automation propro-cess and to detect malfunctions. A good particle size analysis in this work has not come from a good segmentation of particles that would extract every particle in the image correctly. It has come from by learning the segmentation results for images with different PSD. Distinc-tiveness is measured with ICC. The meaning segmentation result is learned to represent a specific PSD. The proposed system for particle size distribution analysis shows promising results and the implementation of such machine vision system is feasible.
REFERENCES
[1] Cen tc 343 - "solid recovered fuel". http://erfo.info/cen-tc-343.26.
0.html. Accessed: October 2014.
[2] R Sarc and Karl E Lorber. Production, quality and quality assurance of refuse de-rived fuels (rdfs). Waste management, 33(9):1825–1834, 2013.
[3] Antonio Gallardo, Mar Carlos, MD Bovea, Francisco J Colomer, and Fernando Al-barrán. Analysis of refuse-derived fuel from the municipal solid waste reject fraction and its compliance with quality standards. Journal of Cleaner Production, 83:118–
125, 2014.
[4] Emmanuel Kakaras, Panagiotis Grammelis, Michails Agraniotis, Willy Derichs, Hans-Peter Schiffer, Jörg Maier, Thomas Hilber, Thomas Glorius, and Uwe Becker.
Solid recovered fuel as coal substitute in the electricity generation sector. Thermal Science, 9(2):17–30, 2005.
[5] Petr Stehlík. Contribution to advances in waste-to-energy technologies. Journal of Cleaner Production, 17(10):919–931, 2009.
[6] Masaru Tanaka. Waste management for a sustainable society. Journal of Material Cycles and Waste Management, 9(1):2–6, 2007.
[7] European Parliament and Council of the European Union. Directive 2000/76/EU, 2000.
[8] European Parliament and Council of the European Union. Directive 2010/75/EU, 2010.
[9] Wenchao Ma and Susanne Rotter. Overview on the chlorine origin of msw and cl-originated corrosion during msw & rdf combustion process. InBioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on, pages 4255–4258. IEEE, 2008.
[10] Sabine Flamme and Julia Geiping. Quality standards and requirements for solid recovered fuels: a review. Waste Management & Research, 30(4):335–353, 2012.
[11] Karl E Lorber, Renato Sarc, and Alexia Aldrian. Design and quality assurance for solid recovered fuel. Waste Management & Research, 30(4):370–380, 2012.
[12] CA Velis, Philip J Longhurst, Gillian H Drew, Richard Smith, and Simon JT Pol-lard. Production and quality assurance of solid recovered fuels using mechanical
biological treatment (mbt) of waste: a comprehensive assessment. Critical Reviews in Environmental Science and Technology, 40(12):979–1105, 2010.
[13] C Eswaraiah, T Kavitha, S Vidyasagar, and SS Narayanan. Classification of metals and plastics from printed circuit boards (pcb) using air classifier. Chemical Engi-neering and Processing: Process Intensification, 47(4):565–576, 2008.
[14] M Shapiro and V Galperin. Air classification of solid particles: a review. Chemical Engineering and Processing: Process Intensification, 44(2):279–285, 2005.
[15] Md Abdur Rahman and MCM Bakker. Sensor-based control in eddy current sepa-ration of incinerator bottom ash. Waste management, 33(6):1418–1424, 2013.
[16] Ruan Jujun, Qian Yiming, and Xu Zhenming. Environment-friendly technology for recovering nonferrous metals from e-waste: Eddy current separation. Resources, Conservation and Recycling, 87:109–116, 2014.
[17] Ren C Luo, Chih-Chen Yih, and Kuo Lan Su. Multisensor fusion and integration:
approaches, applications, and future research directions. Sensors Journal, IEEE, 2(2):107–119, 2002.
[18] Liu Bin and Peng Jiaxiong. Image fusion method based on nonseparable wavelets.
Machine Vision and Applications, 16(3):189–196, 2005.
[19] Tako PR DE JONG, JA van Houwelingen, and W Kuilman. Automatic sorting and control in solid fuel processing: opportunities in european perspective. Geologica belgica, 7(3-4):325–333, 2004.
[20] Tuomas J Lukka, Timo Tossavainen, Janne V Kujala, and Tapani Raiko. Zenrobotics recycler–robotic sorting using machine learning.
[21] Ian T Young, Jan J Gerbrands, and Lucas J Van Vliet. Fundamentals of image processing. Delft University of Technology Delft, The Netherlands, 1998.
[22] Norbert Huber, Simon Eschlböck-Fuchs, H Scherndl, A Freimund, J Heitz, and JD Pedarnig. In-line measurements of chlorine containing polymers in an indus-trial waste sorting plant by laser-induced breakdown spectroscopy. Applied Surface Science, 302:280–285, 2014.
[23] Petra Tatzer, Markus Wolf, and Thomas Panner. Industrial application for inline material sorting using hyperspectral imaging in the nir range. Real-Time Imaging, 11(2):99–107, 2005.
[24] W KUILMAN, JA van HOUWELINGEN, et al. Automatic sorting and control in solid fuel processing: Opportunities in european perspective. Geologica Belgica, 2006.
[25] Gregory Dunnu, Thomas Hilber, and Uwe Schnell. Advanced size measurements and aerodynamic classification of solid recovered fuel particles. Energy & fuels, 20(4):1685–1690, 2006.
[26] Artzai Picon, Ovidiu Ghita, Pedro M Iriondo, Aranzazu Bereciartua, and Paul F Whelan. Automation of waste recycling using hyperspectral image analysis. In Emerging Technologies and Factory Automation (ETFA), 2010 IEEE Conference on, pages 1–4. IEEE, 2010.
[27] Silvia Serranti, Aldo Gargiulo, and Giuseppe Bonifazi. Characterization of post-consumer polyolefin wastes by hyperspectral imaging for quality control in recy-cling processes. Waste Management, 31(11):2217–2227, 2011.
[28] Tomra sorting devices. http://tomra.com/en/. Accessed: October 2014.
[29] Gary G Koch. Intraclass correlation coefficient.Encyclopedia of statistical sciences, 1982.
[30] Kenneth O McGraw and Seok P Wong. Forming inferences about some intraclass correlation coefficients. Psychological methods, 1(1):30, 1996.
[31] William M Stanish and Noel Taylor. Estimation of the intraclass correlation coef-ficient for the analysis of covariance model. The American Statistician, 37(3):221–
224, 1983.
[32] James MacQueen et al. Some methods for classification and analysis of multivari-ate observations. InProceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume 1, pages 281–297. Oakland, CA, USA., 1967.
[33] M Emre Celebi, Hassan A Kingravi, Hitoshi Iyatomi, Y Alp Aslandogan, William V Stoecker, Randy H Moss, Joseph M Malters, James M Grichnik, Ashfaq A Marghoob, Harold S Rabinovitz, et al. Border detection in dermoscopy images using statistical region merging. Skin Research and Technology, 14(3):347–353, 2008.
[34] Zhijian Huang, Jinfang Zhang, Xiang Li, and Hui Zhang. Remote sensing im-age segmentation based on dynamic statistical region merging. Optik-International Journal for Light and Electron Optics, 125(2):870–875, 2014.
[35] Richard Nock and Frank Nielsen. Statistical region merging. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(11):1452–1458, 2004.
[36] David A. Forsyth and Jean Ponce. Computer Vision: A Modern Approach. Prentice Hall Professional Technical Reference, 2002.
[37] Fernand Meyer and Serge Beucher. Morphological segmentation. Journal of visual communication and image representation, 1(1):21–46, 1990.
[38] MS Sulaiman, SK Sinnakaudan, SF Ng, and K Strom. Application of automated grain sizing technique (ags) for bed load samples at rasil river: A case study for supply limited channel. Catena, 121:330–343, 2014.
[39] Fi-John Chang and Chang-Han Chung. Estimation of riverbed grain-size distribution using image-processing techniques. Journal of Hydrology, 440:102–112, 2012.
[40] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk. Slic superpixels compared to state-of-the-art superpixel methods.
Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(11):2274–
2282, 2012.
[41] C Lawrence Zitnick and Sing Bing Kang. Stereo for image-based rendering using image over-segmentation. International Journal of Computer Vision, 75(1):49–65, 2007.