Deep Learning Applications in Thermal IR Image Processing
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6547-2_10
Reference14 articles.
1. Mishra P et al (2022) Deep learning for near-infrared spectral data modelling: hypes and benefits. TrAC Trends Anal Chem 116804
2. Guo S et al (2020) Deep learning for artefact removal in infrared spectroscopy. Analyst 145(15):5213–5220
3. Mwanga EP et al (2023) Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra. BMC Bioinform 24(1):1–15
4. Ribeiro DCSZ et al (2023) Determination of the lactose content in low-lactose milk using Fourier-transform infrared spectroscopy (FTIR) and convolutional neural network. Heliyon e12898
5. Munyendo L et al. Novel method for the detection of adulterants in coffee and the determination of a coffee’s geographical origin using near infrared spectroscopy complemented by an autoencoder. Int J Food Sci Technol
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