Classification of Food Powders with Open Set using Portable VIS-NIR Spectrometer

You, Hanjong; Kim, Hyungjik; Joo, Dong-Kyun; Lee, Seung Min; Kim, Jeongung; Choi, Sunwoong

doi:10.1109/icaiic.2019.8668992

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B Open-set Recognition Methods In Computer Vision Field1

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2024

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Cited by 7 publications

(2 citation statements)

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“…Both ranges are widely used in the field of food, for issues related to classification and regression, with good performance ( Wang, L. et al, 2017 ; Yu, H. et al, 2021 ; Liu, F., and He, Y. 2008 ; You, H. et al, 2019 ; Kays, S.E. et al, 2000 ).…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral imaging as a non-destructive technique for estimating the nutritional value of food

Marín-Méndez,

Luri Esplandiú,

Alonso-Santamaría

et al. 2024

Current Research in Food Science

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Section: Discussionmentioning

confidence: 99%

Hyperspectral imaging as a non-destructive technique for estimating the nutritional value of food

Marín-Méndez,

Luri Esplandiú,

Alonso-Santamaría

et al. 2024

Current Research in Food Science

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“…A study has demonstrated the use of this technique to realize automatic target recognition (ATR) of synthetic aperture radar (SAR) images [35]. In this study, researchers employed this technique to classify open food powders and achieved an accuracy of 91.2% [36].…”

Section: B Open-set Recognition Methods In Computer Vision Fieldmentioning

confidence: 99%

A Research of Gas Open-Set Identification Based on Data Augmentation Algorithm

Zhu

Wang

2023

IEEE Access

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Significant progress has been made in convolutional neural networks (CNN) based gas recognition. However, existing electronic nose (e-Nose) algorithms all use the closed-set assumption that the test and training samples are in the same label space and can only detect objects of known classes. However, in realistic scenarios, collecting data and training for every possible gas would waste much resource. Openset identification aims to actively reject samples from unknown classes by reducing the intra-class spacing and, thus, not misclassifying them as known classes. In this study, we propose a data preprocessing method to enhance the performance of closed-set recognition by augmenting the eigenvalues of each gas. We then implement the open-set recognition task for gases using an open-set recognition model. These methods contribute to improved recognition accuracy for gases and provide an effective means of handling unknown class samples. Experimental results show that our approach can identify unknown samples well while maintaining accuracy for available classes.

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Dielectric Spectral Profiles For Andean Tubers Classification: A Machine Learning Techniques Application

Chuquizuta

Oblitas

Arteaga

et al. 2021

2021 International Conference on Electromagnetics in Advanced Applications (ICEAA)

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Classification of Food Powders with Open Set using Portable VIS-NIR Spectrometer

Cited by 7 publications

References 7 publications

Hyperspectral imaging as a non-destructive technique for estimating the nutritional value of food

Hyperspectral imaging as a non-destructive technique for estimating the nutritional value of food

A Research of Gas Open-Set Identification Based on Data Augmentation Algorithm

Dielectric Spectral Profiles For Andean Tubers Classification: A Machine Learning Techniques Application

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