FR3090167B1 - Procédé d’amélioration de la précision d’un ensemble de données d’images d’apprentissage d’un réseau neuronal à convolution pour des applications de prévention des pertes - Google Patents
Procédé d’amélioration de la précision d’un ensemble de données d’images d’apprentissage d’un réseau neuronal à convolution pour des applications de prévention des pertes Download PDFInfo
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- FR3090167B1 FR3090167B1 FR1914458A FR1914458A FR3090167B1 FR 3090167 B1 FR3090167 B1 FR 3090167B1 FR 1914458 A FR1914458 A FR 1914458A FR 1914458 A FR1914458 A FR 1914458A FR 3090167 B1 FR3090167 B1 FR 3090167B1
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- 238000000034 method Methods 0.000 title abstract 3
- 230000002265 prevention Effects 0.000 title abstract 3
- 238000013527 convolutional neural network Methods 0.000 title abstract 2
- 238000013528 artificial neural network Methods 0.000 abstract 1
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K17/00—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations
- G06K17/0022—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisions for transferring data to distant stations, e.g. from a sensing device
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- G06F18/254—Fusion techniques of classification results, e.g. of results related to same input data
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- G06F3/08—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers from or to individual record carriers, e.g. punched card, memory card, integrated circuit [IC] card or smart card
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- G06K7/14—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
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- G06V10/809—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level of classification results, e.g. where the classifiers operate on the same input data
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- G06K7/10821—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation by scanning of the records by radiation in the optical part of the electromagnetic spectrum further details of bar or optical code scanning devices
- G06K7/10861—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation by scanning of the records by radiation in the optical part of the electromagnetic spectrum further details of bar or optical code scanning devices sensing of data fields affixed to objects or articles, e.g. coded labels
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- G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
- G06K7/14—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
- G06K7/1404—Methods for optical code recognition
- G06K7/146—Methods for optical code recognition the method including quality enhancement steps
- G06K7/1482—Methods for optical code recognition the method including quality enhancement steps using fuzzy logic or natural solvers, such as neural networks, genetic algorithms and simulated annealing
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- Engineering & Computer Science (AREA)
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- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
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- Quality & Reliability (AREA)
- Automation & Control Theory (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Human Computer Interaction (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Algebra (AREA)
- Image Analysis (AREA)
Abstract
Procédé d ’ amélioration de la précision d ’ un ensemble de données d ’ images d ’ apprentissage d ’ un réseau neuronal à convolution pour des applications de prévention des pertes Des techniques d’amélioration de la précision d’un réseau neuronal formé pour des applications de prévention des pertes comportent l’identification des caractéristiques physiques d’un objet (108) dans des données de numérisation d’image, le rognage de repères des données de numérisation d’image, et l’examen des caractéristiques physiques dans les données de numérisation d’image à repères supprimés en utilisant un réseau neuronal pour identifier l’objet (108) sur la base d’une comparaison de données d’identification en fonction des caractéristiques physiques et une autre identification, comme en fonction des repères. En réponse à une prédiction de correspondance, une indication est faite d’une correspondance et un signal d’authentification est généré. Figure pour l’abrégé : Fig. 1.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US16/221,816 | 2018-12-17 | ||
US16/221,816 US20200192608A1 (en) | 2018-12-17 | 2018-12-17 | Method for improving the accuracy of a convolution neural network training image data set for loss prevention applications |
Publications (2)
Publication Number | Publication Date |
---|---|
FR3090167A1 FR3090167A1 (fr) | 2020-06-19 |
FR3090167B1 true FR3090167B1 (fr) | 2022-09-09 |
Family
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Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
FR1914458A Active FR3090167B1 (fr) | 2018-12-17 | 2019-12-16 | Procédé d’amélioration de la précision d’un ensemble de données d’images d’apprentissage d’un réseau neuronal à convolution pour des applications de prévention des pertes |
Country Status (2)
Country | Link |
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US (1) | US20200192608A1 (fr) |
FR (1) | FR3090167B1 (fr) |
Families Citing this family (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11062104B2 (en) * | 2019-07-08 | 2021-07-13 | Zebra Technologies Corporation | Object recognition system with invisible or nearly invisible lighting |
US20210334594A1 (en) * | 2020-04-23 | 2021-10-28 | Rehrig Pacific Company | Scalable training data capture system |
US11727678B2 (en) | 2020-10-30 | 2023-08-15 | Tiliter Pty Ltd. | Method and apparatus for image recognition in mobile communication device to identify and weigh items |
CN113486937A (zh) * | 2021-06-28 | 2021-10-08 | 华侨大学 | 一种基于卷积神经网络的固废识别数据集构建系统 |
CN114580588B (zh) * | 2022-05-06 | 2022-08-12 | 江苏省质量和标准化研究院 | 基于概率矩阵模型的uhf rfid群标签选型方法 |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8494909B2 (en) * | 2009-02-09 | 2013-07-23 | Datalogic ADC, Inc. | Automatic learning in a merchandise checkout system with visual recognition |
US9594983B2 (en) * | 2013-08-02 | 2017-03-14 | Digimarc Corporation | Learning systems and methods |
JP7009389B2 (ja) * | 2016-05-09 | 2022-01-25 | グラバンゴ コーポレイション | 環境内のコンピュータビジョン駆動型アプリケーションのためのシステムおよび方法 |
-
2018
- 2018-12-17 US US16/221,816 patent/US20200192608A1/en not_active Abandoned
-
2019
- 2019-12-16 FR FR1914458A patent/FR3090167B1/fr active Active
Also Published As
Publication number | Publication date |
---|---|
FR3090167A1 (fr) | 2020-06-19 |
US20200192608A1 (en) | 2020-06-18 |
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