طبقه‌بندی هوشمند ماهی کپور معمولی (Cyprinus carpio) بر اساس تازگی با استفاده از پردازش تصویر و سامانه استنتاج فازی عصبی تطبیقی

Balaban, M.O., Odabasi, A.Z., Damar S., Oliveira, A.C. (2011). Quality Evaluation of. Computer Vision Technology for Food Quality Evaluation, 189.

Brosnan, T. & Sun, D.-W. (2004). Improving quality inspection of food products by computer vision – a review. Journal of Food Engineering, 61(1), 3–16.

 Castellano, G. & Faneli, A.M. (2001). A selforganizing neuro network inference network. University dehli Studi di Bari, Dipartimento di Informatica, Via E. Orabona, 4-70126 Bari-ITALY.

Chaudhary, P., Chaudhari, A. K.,  Dr. Cheeran, A. N. & Godara., Sh. (2012). Color Transform Based Approach for Disease Spot Detection on Plant Leaf. International journal of computer science and telecommunications, 3(6), 65-70.

Cheng, J. H., Sun, D.-W., Han, Z. & Zeng, X. A. (2014). Texture and structure measurements and analyses for evaluation of fish and fillet freshness quality: a review. Comprehensive Reviews in Food Science and Food Safety, 13(1), 52-61.

Cheng, J., Sun, D.-W., Zeng, X.-A. & Liu, D. (2013). Recent advances in methods and techniques for freshness quality determination and evaluation of fish and fish fillets: a review. Critical Reviews in Food Science and Nutrition, 55(7), 1012-1025.

Costa, C., Antonucci, F., Menesatti, P., Pallottino, F., Boglione, C. & Cataudella, S. (2013). An advanced colour calibration method for fish freshness assessment: a comparison between standard and passive refrigeration modalities. Food and BioprocessTechnology, 6(8), 2190–2195.

Dowlati, M., Mohtasebi, S. S., Omid, M., Razavi, S. H., Jamzad, M. & de la Guardia, M. (2013). Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes. Journal of Food Engineering, 119(2), 277–287.

Food and Agriculture Organization (FAO) (2016): FAO yearbook. Fishery and aquaculture statistics. Rome, Italy.

Gelman, A., Pasteur, R., & Rave, M. (1990). Quality changes and storage life of common carp (Cyprinus carpio) at various storage temperatures. Journal of the Science of Food and Agriculture, 52(2), 231-247.

Ghasemi-Varnamkhasti, M., Goli, R., Forina, M., Mohtasebi, S. S., Shafiee, S. & Naderi-Boldaji, M. (2016). Application of image analysis combined with computational expert approaches for shrimp freshness evaluation. International Journal of Food Properties, 19(10), 2202-2222.

Goñi, S.M. & Salvadori, V. O. (2017). Color measurement: comparison of colorimeter vs. computer vision system. Food Measurement and Characterization, 11(2), 538-547

Gonzalez, R.C., Woods, R.E. & Eddins, S.L. (2004). Digital Image Processing Using MATLAB, Pearson Prentice Hall: New Jersey, USA.

Hall, M. A. (2000). Correlation-based Feature Selection for Discrete and Numeric Class Machine
Learning. In:Proceedings of 17th International Conference on Machine Learning, 29 June – 02 July., Waikato University, Hamilton, New Zealand, PP. 359-366.

Huang, X., Xu, H., Wu, L., Dai, H., Yao, L., & Han, F. (2016). A data fusion detection method for fish freshness based on computer vision and near-infrared spectroscopy. Analytical Methods, 8(14): 2929-2935.

Judal, A. & Bhadania, A. G. (2015). Role of Machine Vision System in Food Quality and Safety Evaluation. International Journal of Advance Research and Innovation, 3(4), 611-615.

Kanamori, K., Shirataki, Y., Liao, Q., Ogawa, Y., Suzuki, T., & Kondo, N. (2017, May). Fish freshness estimation using eye image processing under white and UV lightings. In Sensing for Agriculture and Food Quality and Safety IX (Vol. 10217, p. 102170E). International Society for Optics and Photonics.

Kavi Niranjana, K. & Kalpana Devi, M. (2015). RGB to Lab Transformation Using Image Segmentation.  International Journal of Advance Research in Computer Science and Management Studies, 3(11), 8-16.

Khulal, U., Zhao, J., Hu, W. &  Chen, Q. (2016). Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food Chemistry, 197, 1191–1199.

Kishore Dutta, M., Issac, A., Minhas, N. & Sarkar, B. (2016).  Image processing based method to assess fish quality and freshness. Journal of Food Engineering, 177, 50-58.

Komani., M. H, Mortazavi., A., Safari., Omid. & Mehraban Sang Atash, M. (2013). Investigation the amount of oxidation of rainbow trout (Oncorhynchus mykiss) fillet fat stored at 4 ± 1 ° C using image processing technique. In: Proceedings of 2nd National Coference on Food Science and Technology, 29-30 April., Islamic Azad University, Quchan Branch, Quchan, Iran (In Farsi)

Labatut, V., & Cherifi, H. (2012). Accuracy measures for the comparison of classifiers. arXiv preprint arXiv:1207.3790.

Li, C., Heinemann, P. & Sherry, R. (2007). Neuro network and Bayesian network fusion models
to fuse electronic nose and surface acoustic wave sensor data for apple defect detection.
Sensors and Actuators B: Chemical, 125(1), 301-310.

Liu, D., Zeng, X. & Sun, D.-W. (2013). NIR Spectroscopy and Imaging Techniques for Evaluation of Fish Quality—A Review. Applied Spectroscopy Reviews, 48(8), 609–628.

Lunda, R., Linhartova, Z., Masilko, J., Dvorak, P., Smole Možina, S. & Mraz, J. (2016). Effect of different types of descaling methods on shelf life of air-/vacuum-packaged common carp (Cyprinus carpio L.) fillets under refrigerated storage conditions. Aquaculture International, 24(6), 1555-1568.

Ma, J., Sun, D.-W., Qu, J.-h., Liu, D., Pu, H., Gao, W.-h. & Zeng, X.a. (2016). Applications of computer vision for assessing quality of agri-food products: a review of recent research advances. Critical Reviews in Food Science and Nutrition, 56(1),113-127.

Menesatti, P., Costa, C., & Aguzzi, J. (2010). Quality evaluation of fish by hyperspectral Imaging. In D. -W. Sun (Ed.), Hyperspectral imaging for food quality: Analysis and control (pp. 273–294). San Diego, California, USA: Academic Press/Elsevier.

Misimi, E., Erikson, U. & Skavhaug, A. (2008). Quality grading of Atlantic salmon (Salmo salar) by computer vision. Journal of Food Science, 73(5), E211–E217.

Nouri Khajavi, M. & Nasernia, E. (2015). Online diagnosis of tool wear in milling operation using vibration analysis and intelligent methods. Modares Mechanical Engineering,15(2), 261-269, (In Farsi)

Oveisi Argane, F. & Erfanian Omidvar, A. (2008). Extracting features using cross-sectional information for classification of brain signals in brain-computer communication systems. The CIS Journal on Computer Science and Engineering, 6(3), 60-67. (In Farsi)

Piri, N., Ansari, H. & Farid Hoseini, A.R. (2013). Modeling of solar radiation received the Earth using ANFIS and empirical models (Case Study: Zahedan and Bojnourd stations). Iranian Journal of Energy, 16 (3), 37-58. (In Farsi)

Quevedo, R. & Aguilera, J.M. (2010). Computer vision and stereoscopy for estimating firmness in the salmon (Salmon salar) fillets. Food and Bioprocess Technology, 3(4), 561–567.

Saberioon, M., Gholizadeh, A., Cisar, P., Pautsina, A. & Urban, J. (2017). Application of machine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues. Reviews in Aquaculture, 9(4), 369-387.

Safavi, H.R. & Golmohammadi, M.H. (2016). Evaluating the Water Resource Systems Performance Using Fuzzy Reliability, Resilience and Vulnerability. Iran-Water Resources Research, 12(1), 68-83. (In Farsi)

Sangwine, S.J. & Horne, R. E. N. (1998) (Eds.), The Colour Image Processing Handbook. Chapman & Hall, London.

Shahabi Ghoyonlooyi, M., Rafiei, Sh., Mohtasebi, S.S. & Hoseinpour, S. (2014). Application of artificial neuro network and adaptive neuro-fuzzy inference systems in determining the moisture content in green tea sheets based on colored parameters. Biosystem Engineering of Iran, 44(2), 125-133. (In Farsi)

Shahriar Sazzad, T. M., Islam, S., Mahbubur Rahman Khan Mamun, M. & Zahid Hasan, Md. (2013). Establishment of an Efficient Color Model from Existing Models for Better Gamma Encoding In Image Processing. International Journal of Image Processing, 7(1), 90-100.

Shi, Z. & He, L. (2010). Application of neuro networks in medical image processing. In: Proceedings of 2nd International Symposium on Networking and Network Security, 2-4 April., Jinggangshan, China.

 Sokolova, M. & Lapalme, Guy. (2009). A systematic analysis of performance measures for classification tasks.Information Processing & Management, 45(4), 427–437.

Taheri-Garavand, A. (2015). Implementation and development fault diagnosis of cooling system radiator using combined thermography and artificial intelligence techniques, PhD. dissertation, University of Tehran, Iran. (In Farsi)

Taheri-Garavand, A., Ahmadi, H., Omid, M., Mohtasebi, S.S., Mollazade, K.,  Russell Smith, A. J. &  Carlomagno, G. M. (2015). An intelligent approach for cooling radiator fault diagnosis based oninfrared thermal image processing technique. Applied Thermal Engineering, 87, 434-443.

Wang, F., Zang, Y., Wo, Q., Zou, C., Wang, N., Wang, X., & Li, D. (2013). Fish freshness rapid detection based on fish-eye image. In PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering (Vol. 8761, p. 87610A). International Society for Optics and Photonics.

Yagoobi-Soureh, A., Alizadeh-Khaled Abad, M. & Rezazad Bari, M. (2013). Application of image processing for determination of L*, a*and b*indices in color measurement of foods, Journal of Food Research (University oF Tabriz), 23(3), 411- 422.

Zhou, X., Yuan, J. & Liu, H. (2015).  A traffic light recognition algorithm based on compressive tracking.  International Journal of Hybrid Information Technology, 8(6), 323-332.