@inproceedings{1adc850810a54d37b0965696eaf531de,
title = "AdaSpectrum: Lightweight Hyperspectral Fine-Grained Classification of Visually Similar Materials from RGB Images on Mobile Platforms",
abstract = "To solve the classification problem of visually similar materials on mobile devices, this paper proposes AdaSpectrum, a mobile hyperspectral imaging system for visually similar material classification, such as powder material,it processes RGB and near-infrared(NIR) images to generate hyperspectral data and classify materials directly on mobile devices. It focuses on optimizing performance for mobile devices, reducing computational load and improving speed and memory efficiency. Preliminary results show the system effectively balances accuracy and resource constraints, with plans to expand its applications to more material types.",
keywords = "Hyperspectral Image, Mobile Devices, Model Lightweight",
author = "Geyang Song and Bin Guo and Sicong Liu and Lehao Wang and Zhiwen Yu",
note = "Publisher Copyright: {\textcopyright} 2024 ACM.; 1st ACM International Workshop on Resource-efficient Mobile and Embedded LLM System in AIoT, RMELS 2024, Part of SenSys 2024 ; Conference date: 04-11-2024 Through 04-11-2024",
year = "2024",
month = nov,
day = "4",
doi = "10.1145/3698383.3699620",
language = "英语",
series = "RMELS 2024 - Proceedings of the 1st ACM International Workshop on Resource-efficient Mobile and Embedded LLM System in AIoT, Part of: ACM Sensys 2024",
publisher = "Association for Computing Machinery, Inc",
pages = "1--2",
booktitle = "RMELS 2024 - Proceedings of the 1st ACM International Workshop on Resource-efficient Mobile and Embedded LLM System in AIoT, Part of",
}