T. Chirakitsakul et al.: Integration of Convolutional Neural Network and Image Processing
PHICHIT SOMBOON received the B.Sc. degree in forestry from the Faculty of Forestry, Kasetsart University, Thailand, in 1997, and the M.Sc. (Tech.) degree in paper technology and the D.Sc. (Tech.) degree in paper and printing technology from the Helsinki University of Technology, Finland, in 2003 and 2009, respectively. Currently, he is an Assistant Professor with the Pulp and Paper Technology Program, Department of Forest Products, Faculty of Forestry, Kasetsart Univer- sity. His research interests include mechanical pulping, papermaking, paper recycling, and process engineering in paper and board industries.
[30] H. Kangas, P. Lahtinen, A. Sneck, A.-M. Saariaho, O. Laitinen, and E. Hellén, ‘‘Characterization of fibrillated celluloses. A short review and evaluation of characteristics with a combination of methods,’’ Nordic Pulp Paper Res. J. , vol. 29, no. 1, pp. 129–143, Jan. 2014. [31] Y.-P. Lv, S.-B. Qiu, and B. Yuan, ‘‘Pulp fibre recognition expert system based on neural network,’’ in Proc. Int. Conf. Wavelet Anal. Pattern Recognit. , Nov. 2007, pp. 788–792. [32] D. Almonti, G. Baiocco, V. Tagliaferri, and N. Ucciardello, ‘‘Artificial neural network in fibres length prediction for high precision control of cellulose refining,’’ Materials , vol. 12, no. 22, p. 3730, Nov. 2019. [33] P. Kontschieder, M. Donoser, J. Kritzinger, W. Bauer, and H. Bischof, ‘‘Detecting paper fibre cross sections in microtomy images,’’ in Proc. 20th Int. Conf. Pattern Recognit. , Aug. 2010, pp. 316–319. [34] S. B. Lindström, R. Amjad, E. Gåhlin, L. Andersson, M. Kaarto, K. Liubytska, J. Persson, J.-E. Berg, B. A. Engberg, and F. Nilsson, ‘‘Pulp particle classification based on optical fiber analysis and machine learning techniques,’’ Fibers , vol. 12, no. 1, p. 2, Dec. 2023. [35] D. Bradley and G. Roth, ‘‘Adaptive thresholding using the integral image,’’ J. Graph. Tools , vol. 12, no. 2, pp. 13–21, Jan. 2007. [36] T. Boonchuaychu, P. Wattuya, and W. Taparhudee, ‘‘A skeleton reconstruc- tion algorithm for identifying individual fish fry in a population image,’’ in Proc. IEEE Int. Conf. Imag. Syst. Techn. (IST) , Sep. 2015, pp. 1–6.
PANTHIRA JANSAKRA received the Bachelor of Science degree (Hons.) in wood and paper products technology, specializing in pulp and paper technology from the Faculty of Forestry, Kasetsart University, Thailand. She was a Trainee with Riverpro Pulp and Paper Company Ltd., in the summer, where she gained practical experience in pulp and paper production. Her research interests include the optimization of refining processes and advancements in tissue paper production.
TANACHOT CHIRAKITSAKUL received the B.Sc. degree in information technology from the King Mongkut’s Institute of Technology Ladkrabang (KMITL), in 2022. He is currently pursuing the degree with the Department of Computer Science, Faculty of Science, Kasetsart University. His research interests include machine learning, image processing, and the Internet of Things.
CHAKRIT WATCHAROPAS (Member, IEEE) received the M.S. degree in computer science from the University of Southern California, in 1997, and the Ph.D. degree in computer science from Clemson University, in 2004. He is an Assis- tant Professor with the Department of Computer Science, Kasetsart University, Thailand. He is a member of the Artificial Intelligence Innova- tion Center for Healthcare, Kasetsart University. From 2014 to 2016, he was a Postdoctoral Fellow
PAKAKET WATTUYA received the B.Sc. degree in computer science from the Faculty of Science, and the M.Eng. degree in computer engineering from the Faculty of Engineering, Kasetsart University, Thailand, in 2000 and 2004, respectively, and the Dr. rer. nat. degree in computer science from the University of Müenster, Germany, in 2010. Currently, she is an Assistant Professor with the Department of Computer Science, Kasetsart University. Her research interests include image
with Clemson University. His research interests include computer graphics and deep learning, with a focus on fracture simulation, surface extraction, phase transitions, and photovoltaic power forecasting.
processing, computer vision, machine learning, and deep learning.
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