JP6615062B2 - 画像を処理する方法及びシステム - Google Patents

画像を処理する方法及びシステム Download PDF

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JP6615062B2
JP6615062B2 JP2016147213A JP2016147213A JP6615062B2 JP 6615062 B2 JP6615062 B2 JP 6615062B2 JP 2016147213 A JP2016147213 A JP 2016147213A JP 2016147213 A JP2016147213 A JP 2016147213A JP 6615062 B2 JP6615062 B2 JP 6615062B2
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JP2017059218A5 (https=
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デホン・リウ
ゼミン・ザン
アンソニー・ヴェトロ
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Mitsubishi Electric Corp
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/30—Noise filtering
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/7715—Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/13—Satellite images

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  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Astronomy & Astrophysics (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Computing Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Remote Sensing (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Evolutionary Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)
JP2016147213A 2015-09-15 2016-07-27 画像を処理する方法及びシステム Active JP6615062B2 (ja)

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US14/854,634 US10217018B2 (en) 2015-09-15 2015-09-15 System and method for processing images using online tensor robust principal component analysis
US14/854,634 2015-09-15

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JP2017059218A JP2017059218A (ja) 2017-03-23
JP2017059218A5 JP2017059218A5 (https=) 2019-06-20
JP6615062B2 true JP6615062B2 (ja) 2019-12-04

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US10217018B2 (en) * 2015-09-15 2019-02-26 Mitsubishi Electric Research Laboratories, Inc. System and method for processing images using online tensor robust principal component analysis
WO2017136070A1 (en) * 2016-02-03 2017-08-10 Google Inc. Compressed recurrent neural network models
CN107292852B (zh) * 2017-07-19 2020-05-05 南京邮电大学 一种基于低秩理论的图像去噪算法
CN108537252B (zh) * 2018-03-21 2022-04-08 温州大学苍南研究院 一种基于新范数的图像噪声去除方法
CN108985161B (zh) * 2018-06-08 2021-08-03 广东工业大学 一种基于拉普拉斯正则化的低秩稀疏表征图像特征学习方法
CN108510013B (zh) * 2018-07-02 2020-05-12 电子科技大学 基于低秩核心矩阵的改进稳健张量主成分分析的背景建模方法
CN113396312B (zh) * 2018-10-12 2024-03-01 电力研究所有限公司 用于在光学失真介质中测量表面特性的方法
CN109658362B (zh) * 2018-12-30 2023-09-05 东北大学秦皇岛分校 基于Capped核范数的数据恢复方法
CN109919200B (zh) * 2019-02-15 2022-08-19 河海大学 一种基于张量分解和域适应的图像分类方法
CN110059291A (zh) * 2019-03-15 2019-07-26 上海大学 一种基于gpu的三阶低秩张量补全方法
CN109934178A (zh) * 2019-03-18 2019-06-25 电子科技大学 一种基于Kronecker基稀疏表示的红外弱小目标检测方法
CN110232705B (zh) * 2019-05-17 2023-05-12 沈阳大学 一种融合分数阶变分调整的反向低秩稀疏学习目标跟踪方法
CN110516557B (zh) * 2019-08-01 2022-07-29 电子科技大学 基于低秩张量分解的多样本人脸表情识别方法
CN111598798B (zh) * 2020-04-27 2023-09-05 浙江工业大学 一种基于低秩张量链分解的图像修复方法
CN112308884B (zh) * 2020-11-06 2023-06-16 桂林电子科技大学 一种基于张量核范数的视频背景减除方法
CN112767261B (zh) * 2021-01-06 2024-02-13 温州大学 一种基于广义非凸张量鲁棒主成分分析模型的针对彩色图像和视频的非局部去噪框架
CN113702439A (zh) * 2021-08-20 2021-11-26 浙江科技学院 一种基于迭代生成稀疏主成分模型的红外无损检测方法
CN113538296A (zh) * 2021-08-26 2021-10-22 北京环境特性研究所 一种红外图像目标检测方法、装置、计算设备及存储介质
CN115482168B (zh) * 2022-09-26 2025-07-25 电子科技大学 基于全变分低秩逼近的三维张量随机加足迹噪声去噪方法
CN116402716B (zh) * 2023-04-06 2025-07-15 重庆大学 一种基于结构组张量奇异值估计的mri图像重构方法
CN116482692B (zh) * 2023-04-11 2025-07-11 浙江大学 基于在线张量鲁棒主成分分析的雷达移动目标检测方法
CN119805397B (zh) * 2024-12-25 2025-10-21 西北工业大学 一种基于张量分解的海面慢速小目标检测方法
CN119478324B (zh) * 2025-01-10 2025-04-15 国网湖南省电力有限公司电力科学研究院 一种电力无人机巡检图像去雨雾方法及装置
CN121049906B (zh) * 2025-11-06 2026-02-24 东南大学 一种基于非局域张量分解的sar三维增强成像方法
CN121482617B (zh) * 2026-01-07 2026-04-14 绍兴文理学院 一种基于张量多子空间学习的高光谱异常检测方法及系统

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AU2015337052A1 (en) * 2014-10-24 2017-05-04 Landmark Graphics Corporation Structure tensor constrained tomographic velocity analysis
US10217018B2 (en) * 2015-09-15 2019-02-26 Mitsubishi Electric Research Laboratories, Inc. System and method for processing images using online tensor robust principal component analysis

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