參考文獻 |
[1] J. McGrath, ”How Pet Microchipping Works,” 2008 [Online]. Available: http://science.howstuffworks.com/innovation/everyday-innovations/pet-microchip.htm
[2] K. Albrechti, ”Microchip-induced tumors in laboratory rodents and dogs: A review of the literature 1990–2006,” IEEE International Symposium on Technology and Society, pp. 337-349, 2010.
[3] N. Coldea, ”Nose prints as a method of identification in dogs,” Veterinary Quarterly, PP. 60, 2011.
[4] K. Karthik, S. Chakraborty, S. Banik, ”Muzzle Analysis for Biometric Identification of Pigs,” in 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR), pp. 1-6, 2017.
[5] A. Tharwat, T. Gaber, A. E. Hassanien, A. H. Hasssan, F. T. Mohamed, ”Cattle Identification Using Muzzle Print Images Based on Texture Features Approach,” Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA, pp. 217-227, 2014.
[6] W. Kusakunniran, A. Wiratsudakul, U. Chuachan, S. Kanchanapreechakorn, T. Imaromkul, ”Automatic cattle identification based on fusion of texture features extracted from muzzle images,” in 2018 IEEE International Conference on Industrial Technology (ICIT), pp. 1484-1489, 2018.
[7] R. E. Sánchez-Yáñez, E. V. Kurmyshev, F. J. Cuevas, ”A framework for texture classification using the coordinated clusters representation,” Pattern Recognition Letters, vol. 24, no. 1-3, pp. 21-31, 2003.
[8] Z. Shang, M. Li, ”Combined Feature Extraction and Selection in Texture Analysis,” International Symposium on Computational Intelligence and Design (ISCID), vol. 1, pp. 398-401 , 2016.
[9] ”機器視覺表面缺陷檢測綜述,” 2019 [Online]. Available: https://www.itread01.com/content/1547385319.html
[10] T. Ojala, M. Pietikainen, T. Maenpaa, ”Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on pattern analysis and machine intelligence, vol. 24, no. 7, pp. 971–987, 2002.
[11] R. Haralick, K. Shanmugam, I. Dinstein, ”Textural Features for Image Classification,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 3, pp. 610, 1973.
[12] J. Hong, ”Gray level-gradient cooccurrence matrix texture analysis method[J],” Acta Automatica Sinica, vol. 10, no. 1, pp. 22-25, 1984.
[13] A. Padma, R. Sukanesh, ”Automatic Classification and Segmentation of Brain Tumor in CT Images using Optimal Dominant Gray level Run length Texture Features”, International Journal of Advanced Computer Sciences & Applications, vol. 2, no. 10, 2011.
[14] M. Bashar, T. Matsumoto, N. Ohnishi, ”Wavelet transform-based locally orderless images for texture segmentation,” Pattern Recognition Letters, vol. 24, no. 15, pp. 2633-2650, 2003.
[15] S. Grigorescu, N. Petkov, P. Kruizinga, ”Comparison of texture features based on Gabor filters,” IEEE Transactions on Image Processing, vol. 11, no. 10, pp. 142-147, 1999.
[16] W. Wen, A. Xia, ”Verifying edges for visual inspection purposes,” Pattern Recognition Letters, vol. 20, no. 3, pp. 315-328, 1999.
[17] G. Cross, A. Jain, ”Markov Random Field Texture Models,” IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 5, no. 1, pp. 25-39, 1983.
[18] M. Xi, L. Chen, D. Polajnar, W. Tong, ”Local binary pattern network: A deep learning approach for face recognition,” IEEE International Conference on Image Processing (ICIP), pp. 3224-3228, 2016.
[19] R. Touahri, N. AzizI, N. Hammami, M. Aldwairi, F. Benaida,”Automated Breast Tumor Diagnosis Using Local Binary Patterns (LBP) Based on Deep Learning Classification,” International Conference on Computer and Information Sciences (ICCIS), 2019.
[20] J. Tan, Y. Gao, W. Cao, M. Pomeroy, S. Zhang, Y. Huo, L. Li, Z. Liang, ”GLCM-CNN: Gray Level Co-occurrence Matrix based CNN Model for Polyp Diagnosis,” IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2019.
[21] F. Shi, G. Chen, Y. Wang, N. Yang, Y. Chen, N. Dey, R. Sherratt, ”Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks,” in IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp. 432-439, 2019.
[22] S. Aygün, E. Güneş, ”A benchmarking: Feature extraction and classification of agricultural textures using LBP, GLCM, RBO, Neural Networks, k-NN, and random forest,” in 6th International Conference on Agro-Geoinformatics, 2017.
[23] D. Clausi, ”An analysis of co-occurrence texture statistics as a function of grey level quantization,” Canadian Journal of Remote Sensing, vol. 28, no. 1, pp. 45–62, 2002.
[24] D. F. Specht, ”Probabilistic neural networks,” Neural networks, vol. 3, no. 1, pp. 109-118, 1990.
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