MX2021015602A - Sistemas y metodos para determinar acciones realizadas por objetos dentro de imagenes. - Google Patents
Sistemas y metodos para determinar acciones realizadas por objetos dentro de imagenes.Info
- Publication number
- MX2021015602A MX2021015602A MX2021015602A MX2021015602A MX2021015602A MX 2021015602 A MX2021015602 A MX 2021015602A MX 2021015602 A MX2021015602 A MX 2021015602A MX 2021015602 A MX2021015602 A MX 2021015602A MX 2021015602 A MX2021015602 A MX 2021015602A
- Authority
- MX
- Mexico
- Prior art keywords
- layer
- memory
- images
- objects
- systems
- Prior art date
Links
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06F18/211—Selection of the most significant subset of features
- G06F18/2111—Selection of the most significant subset of features by using evolutionary computational techniques, e.g. genetic algorithms
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2431—Multiple classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06V10/771—Feature selection, e.g. selecting representative features from a multi-dimensional feature space
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Psychiatry (AREA)
- Social Psychology (AREA)
- Human Computer Interaction (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- General Engineering & Computer Science (AREA)
- Physiology (AREA)
- Image Analysis (AREA)
Abstract
Un sistema para determinar una acción realizada dentro de una imagen de entrada incluye una memoria para almacenar una o más instrucciones, y un procesador comunicativamente acoplado a la memoria, y configurado para ejecutar la una o más instrucciones en la memoria. El procesador emplea una red neuronal convolucional (CNN) que incluye un número predefinido de etapas iniciales para extraer una o más características significantes correspondientes a la imagen de entrada, en donde cada etapa inicial incluye una primera capa, y un bloque residual, y en donde la primera capa se selecciona de un grupo que consiste de una capa de convolución, una capa de agrupamiento máximo, y una capa de agrupamiento promedio. La CNN incluye una etapa final para clasificar las características significantes extraídas en una o más clases predefinidas, en donde la etapa final está formada de una capa de agrupamiento promedio global, y una capa densa.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US16/458,286 US11151412B2 (en) | 2019-07-01 | 2019-07-01 | Systems and methods for determining actions performed by objects within images |
PCT/IB2020/054486 WO2021001701A1 (en) | 2019-07-01 | 2020-05-12 | Systems and methods for determining actions performed by objects within images |
Publications (1)
Publication Number | Publication Date |
---|---|
MX2021015602A true MX2021015602A (es) | 2022-01-31 |
Family
ID=70857212
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
MX2021015602A MX2021015602A (es) | 2019-07-01 | 2020-05-12 | Sistemas y metodos para determinar acciones realizadas por objetos dentro de imagenes. |
Country Status (11)
Country | Link |
---|---|
US (1) | US11151412B2 (es) |
EP (1) | EP3994604A1 (es) |
JP (1) | JP7351941B2 (es) |
KR (1) | KR20220010560A (es) |
CN (1) | CN114008692A (es) |
AU (1) | AU2020300066B2 (es) |
BR (1) | BR112021024279A2 (es) |
CA (1) | CA3141695A1 (es) |
CO (1) | CO2021016316A2 (es) |
MX (1) | MX2021015602A (es) |
WO (1) | WO2021001701A1 (es) |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11992322B2 (en) * | 2021-03-30 | 2024-05-28 | Ionetworks Inc. | Heart rhythm detection method and system using radar sensor |
CN113908362B (zh) * | 2021-10-13 | 2022-05-17 | 南方医科大学珠江医院 | 基于大数据的ecmo护理质量控制方法及系统 |
KR102655767B1 (ko) | 2023-01-12 | 2024-04-05 | 국립공주대학교 산학협력단 | 쪽파를 함유하는 닭튀김의 제조방법 |
Family Cites Families (29)
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US10242266B2 (en) * | 2016-03-02 | 2019-03-26 | Mitsubishi Electric Research Laboratories, Inc. | Method and system for detecting actions in videos |
US10559111B2 (en) * | 2016-06-23 | 2020-02-11 | LoomAi, Inc. | Systems and methods for generating computer ready animation models of a human head from captured data images |
US10115040B2 (en) * | 2016-09-14 | 2018-10-30 | Kla-Tencor Corporation | Convolutional neural network-based mode selection and defect classification for image fusion |
JP6725411B2 (ja) * | 2016-12-27 | 2020-07-15 | 積水化学工業株式会社 | 行動評価装置、行動評価方法 |
JP6909657B2 (ja) * | 2017-07-12 | 2021-07-28 | 株式会社日立製作所 | 映像認識システム |
US10706350B1 (en) * | 2017-08-11 | 2020-07-07 | Facebook, Inc. | Video analysis using convolutional networks |
US20200210767A1 (en) * | 2017-09-08 | 2020-07-02 | The General Hospital Corporation | Method and systems for analyzing medical image data using machine learning |
US11734955B2 (en) * | 2017-09-18 | 2023-08-22 | Board Of Trustees Of Michigan State University | Disentangled representation learning generative adversarial network for pose-invariant face recognition |
US10692244B2 (en) * | 2017-10-06 | 2020-06-23 | Nvidia Corporation | Learning based camera pose estimation from images of an environment |
SG11201912745WA (en) * | 2017-10-16 | 2020-01-30 | Illumina Inc | Deep learning-based splice site classification |
KR102416048B1 (ko) * | 2017-10-16 | 2022-07-04 | 일루미나, 인코포레이티드 | 변이체 분류를 위한 심층 컨볼루션 신경망 |
US11263525B2 (en) * | 2017-10-26 | 2022-03-01 | Nvidia Corporation | Progressive modification of neural networks |
JP2019096006A (ja) * | 2017-11-21 | 2019-06-20 | キヤノン株式会社 | 情報処理装置、情報処理方法 |
JP7108395B2 (ja) * | 2017-11-27 | 2022-07-28 | ホーチキ株式会社 | 行動監視システム |
EP3901833A1 (en) * | 2018-01-15 | 2021-10-27 | Illumina, Inc. | Deep learning-based variant classifier |
US20190236440A1 (en) * | 2018-01-31 | 2019-08-01 | Pin-Han Ho | Deep convolutional neural network architecture and system and method for building the deep convolutional neural network architecture |
US11507800B2 (en) * | 2018-03-06 | 2022-11-22 | Adobe Inc. | Semantic class localization digital environment |
EP3547211B1 (en) * | 2018-03-30 | 2021-11-17 | Naver Corporation | Methods for training a cnn and classifying an action performed by a subject in an inputted video using said cnn |
US11315570B2 (en) * | 2018-05-02 | 2022-04-26 | Facebook Technologies, Llc | Machine learning-based speech-to-text transcription cloud intermediary |
CN108776807A (zh) * | 2018-05-18 | 2018-11-09 | 复旦大学 | 一种基于可跳层双支神经网络的图像粗细粒度分类方法 |
CN108921022A (zh) * | 2018-05-30 | 2018-11-30 | 腾讯科技(深圳)有限公司 | 一种人体属性识别方法、装置、设备及介质 |
US11010902B2 (en) * | 2018-06-04 | 2021-05-18 | University Of Central Florida Research Foundation, Inc. | Capsules for image analysis |
CN108830211A (zh) * | 2018-06-11 | 2018-11-16 | 厦门中控智慧信息技术有限公司 | 基于深度学习的人脸识别方法及相关产品 |
US11034357B2 (en) * | 2018-09-14 | 2021-06-15 | Honda Motor Co., Ltd. | Scene classification prediction |
CN114502061B (zh) * | 2018-12-04 | 2024-05-28 | 巴黎欧莱雅 | 使用深度学习的基于图像的自动皮肤诊断 |
US11049310B2 (en) * | 2019-01-18 | 2021-06-29 | Snap Inc. | Photorealistic real-time portrait animation |
US10691980B1 (en) * | 2019-04-18 | 2020-06-23 | Siemens Healthcare Gmbh | Multi-task learning for chest X-ray abnormality classification |
US10873456B1 (en) * | 2019-05-07 | 2020-12-22 | LedgerDomain, LLC | Neural network classifiers for block chain data structures |
WO2020236993A1 (en) * | 2019-05-21 | 2020-11-26 | Magic Leap, Inc. | Hand pose estimation |
-
2019
- 2019-07-01 US US16/458,286 patent/US11151412B2/en active Active
-
2020
- 2020-05-12 WO PCT/IB2020/054486 patent/WO2021001701A1/en unknown
- 2020-05-12 BR BR112021024279A patent/BR112021024279A2/pt not_active Application Discontinuation
- 2020-05-12 MX MX2021015602A patent/MX2021015602A/es unknown
- 2020-05-12 AU AU2020300066A patent/AU2020300066B2/en active Active
- 2020-05-12 CA CA3141695A patent/CA3141695A1/en active Pending
- 2020-05-12 KR KR1020217042120A patent/KR20220010560A/ko not_active Application Discontinuation
- 2020-05-12 JP JP2021578061A patent/JP7351941B2/ja active Active
- 2020-05-12 EP EP20728556.0A patent/EP3994604A1/en active Pending
- 2020-05-12 CN CN202080044111.6A patent/CN114008692A/zh active Pending
-
2021
- 2021-11-30 CO CONC2021/0016316A patent/CO2021016316A2/es unknown
Also Published As
Publication number | Publication date |
---|---|
KR20220010560A (ko) | 2022-01-25 |
AU2020300066A1 (en) | 2021-12-09 |
AU2020300066B2 (en) | 2023-02-02 |
US11151412B2 (en) | 2021-10-19 |
WO2021001701A1 (en) | 2021-01-07 |
BR112021024279A2 (pt) | 2022-01-11 |
JP2022540070A (ja) | 2022-09-14 |
CA3141695A1 (en) | 2021-01-07 |
EP3994604A1 (en) | 2022-05-11 |
CN114008692A (zh) | 2022-02-01 |
CO2021016316A2 (es) | 2022-01-17 |
JP7351941B2 (ja) | 2023-09-27 |
US20210004641A1 (en) | 2021-01-07 |
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