| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2026-01-20 |
| タイトル |
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タイトル |
Material Segmentation Using 1-D Convolutional Neural Network With Transient Histogram |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
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主題 |
Material classification |
| キーワード |
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主題Scheme |
Other |
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主題 |
single photon avalanche diode |
| キーワード |
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主題Scheme |
Other |
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主題 |
surface segmentation |
| キーワード |
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主題Scheme |
Other |
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主題 |
temporal resolution |
| キーワード |
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主題Scheme |
Other |
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主題 |
transient histogram |
| キーワード |
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主題Scheme |
Other |
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主題 |
1-D convolutional neural network |
| キーワード |
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主題Scheme |
Other |
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主題 |
Photonics |
| キーワード |
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主題Scheme |
Other |
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主題 |
Single-photon avalanche diodes |
| キーワード |
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主題Scheme |
Other |
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主題 |
Transient analysis |
| キーワード |
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主題Scheme |
Other |
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主題 |
Histograms |
| キーワード |
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主題Scheme |
Other |
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主題 |
Robot sensing systems |
| キーワード |
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主題Scheme |
Other |
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主題 |
Imaging |
| キーワード |
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主題Scheme |
Other |
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主題 |
Convolutional neural networks |
| キーワード |
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主題Scheme |
Other |
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主題 |
Lighting |
| キーワード |
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主題Scheme |
Other |
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主題 |
Image resolution |
| キーワード |
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主題Scheme |
Other |
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主題 |
Cameras |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Pratama, Yohanssen
北野, 和哉
Kushida, Takahiro
藤村, 友貴
舩冨, 卓哉
向川, 康博
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
This study introduces a method for material classification using transient histograms obtained via a single-photon avalanche diode (SPAD) sensor. Temporal resolution in optical sensing plays a crucial role in material classification and surface segmentation, particularly for distinguishing materials with similar visual properties. In this study, SPAD sensors were utilized to capture transient histograms with temporal resolutions ranging from 13 picoseconds to 208 picoseconds, enabling precise extraction of temporal signatures for various materials. A comparative evaluation of classification techniques, including one-dimensional convolutional neural networks (1-D CNN), random forest (RF), support vector classifier (SVC), and k-nearest neighbors (KNN), was conducted to assess the impact of temporal resolution and exposure time on classification accuracy. 1-D CNN achieved the highest classification accuracy of 99.25% at a temporal resolution of 13 ps and an exposure time of 0.09 s, significantly outperforming other methods. Additionally, the proposed SPAD-based system was evaluated for material segmentation on non-planar surfaces. In a real-world experiment, 1-D CNN achieved an overall accuracy of 87.5% in differentiating visually similar materials, demonstrating the effectiveness of transient histograms for material classification where conventional RGB-based methods fail. These findings highlight the potential of SPAD sensors combined with advanced classification techniques to enhance material classification and segmentation, providing a versatile framework for applications in robotics, computer vision, and optical sensing. |
| 書誌情報 |
en : IEEE Access
巻 13,
p. 59295-59308,
ページ数 14,
発行日 2025-03-26
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| 出版者 |
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出版者 |
IEEE |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2169-3536 |
| 出版者版DOI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1109/ACCESS.2025.3554776 |
| 出版者版URI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
URI |
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関連識別子 |
https://ieeexplore.ieee.org/abstract/document/10942358 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
© 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
| 著者版フラグ |
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出版タイプ |
VoR |
| 助成情報 |
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助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
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研究課題番号 |
20K20629 |
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研究課題番号URI |
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20K20629/ |
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研究課題名 |
光伝播の直接計測に基づくコンピュータビジョン |
fulltext (3.2 MB)
