Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Ukrit Watchareeruetai"'
Autor:
Ukrit Watchareeruetai, Benjaphan Sommana, Sanjana Jain, Pavit Noinongyao, Ankush Ganguly, Aubin Samacoits, Samuel W. F. Earp, Nakarin Sritrakool
Publikováno v:
IEEE Access, Vol 10, Pp 16530-16543 (2022)
This paper presents a novel Transformer-based facial landmark localization network named Localization Transformer (LOTR). The proposed framework is a direct coordinate regression approach leveraging a Transformer network to better utilize the spatial
Externí odkaz:
https://doaj.org/article/f7898488681742af8b39dd68d19c9746
Publikováno v:
Journal of Artificial Intelligence Research; 2023, Vol. 78, p167-215, 49p
Autor:
Benjaphan Sommana, Ukrit Watchareeruetai, Ankush Ganguly, Samuel W.F. Earp, Taya Kitiyakara, Suparee Boonmanunt, Ratchainant Thammasudjarit
During the SARS-Cov-2 pandemic, mask-wearing became an effective tool to prevent spreading and contracting the virus. The ability to monitor the mask-wearing rate in the population would be useful for determining public health strategies against the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff60b238f43c326470fc9e61080f21a9
Publikováno v:
2020 8th International Electrical Engineering Congress (iEECON).
Plant nutrient deficiency classification is vital for the agricultural industry to improve both the qualities and the quantities of crops. Computer vision and deep learning technologies, especially convolutional neural networks, perform an essential
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::086fd396f073faed62d8f44181f18356
https://doi.org/10.1109/ieecon48109.2020.229562
https://doi.org/10.1109/ieecon48109.2020.229562
Publikováno v:
JCSSE
This paper investigates the use of various deep convolutional neural networks (CNNs) with transfer learning to identify nutrient deficiencies from a leaf image. Experiments were conducted with a dataset containing 4,088 images of black gram (Vigna mu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::45c1273576fb65d58a5c8ba800746c2c
https://doi.org/10.1109/jcsse.2019.8864224
https://doi.org/10.1109/jcsse.2019.8864224
Publikováno v:
SCIS&ISIS
One approach in training a deep neural network to perform effectively is to do unsupervised pretraining on each layer, followed by fine-tuning the whole network. A common way is to train an unsupervised model of neural network such as restricted Bolt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a0f62bf4bb55146abfb0d35e603c8272
https://doi.org/10.1109/scis-isis.2018.00105
https://doi.org/10.1109/scis-isis.2018.00105
Publikováno v:
2018 22nd International Computer Science and Engineering Conference (ICSEC).
This paper proposes a simple but effective method to improve the generalization performance of extreme learning machine (ELM), which is an extremely fast learning method for a single-hidden-layer feedforward neural network (SLFN). The proposed method
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24a397cf433f12314f0999392bda8e8a
https://doi.org/10.1109/icsec.2018.8712783
https://doi.org/10.1109/icsec.2018.8712783
Publikováno v:
2018 22nd International Computer Science and Engineering Conference (ICSEC).
Extreme learning machine (ELM) is an extremely fast learning algorithm proposed for a single-hidden-layer feed-forward neural network (SLFN). ELM projects a set of training instances into a random feature space, and then analytically calculates the w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d6cc360fefacd89db9cea8b7d7ffda9c
https://doi.org/10.1109/icsec.2018.8712758
https://doi.org/10.1109/icsec.2018.8712758
Autor:
Puriwat Khantiviriya, Chaiwat Wattanapaiboonsuk, Sutsawat Duangsrisai, Ukrit Watchareeruetai, Pavit Noinongyao
Publikováno v:
2018 International Electrical Engineering Congress (iEECON).
A novel image analysis method for identifying nutrient deficiencies in plant based on its leaf is proposed. First, the proposed method divides an input leaf image into small blocks. Second, each block of leaf pixels is fed to a set of convolutional n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::13588074938550212cc386f735349d1b
https://doi.org/10.1109/ieecon.2018.8712217
https://doi.org/10.1109/ieecon.2018.8712217
Publikováno v:
IWCIA
This paper presents an automatic segmentation method used to detect cotton wool spots in the retinal images for diabetic retinopathy disease. An early detection of cotton wool is important to prevent the dangerous damage which may cause blindness and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f8430f86043b4ff9c12e502e2efa441
https://doi.org/10.1109/iwcia.2017.8203585
https://doi.org/10.1109/iwcia.2017.8203585