WO2018163786A3 - Target subject analysis apparatus, target subject analysis method, learning apparatus, and learning method - Google Patents

Target subject analysis apparatus, target subject analysis method, learning apparatus, and learning method Download PDF

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Publication number
WO2018163786A3
WO2018163786A3 PCT/JP2018/005819 JP2018005819W WO2018163786A3 WO 2018163786 A3 WO2018163786 A3 WO 2018163786A3 JP 2018005819 W JP2018005819 W JP 2018005819W WO 2018163786 A3 WO2018163786 A3 WO 2018163786A3
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WO
WIPO (PCT)
Prior art keywords
target subject
neural network
learning
subject analysis
unit configured
Prior art date
Application number
PCT/JP2018/005819
Other languages
French (fr)
Other versions
WO2018163786A2 (en
Inventor
Tanichi Ando
Original Assignee
Omron Corporation
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Publication date
Application filed by Omron Corporation filed Critical Omron Corporation
Publication of WO2018163786A2 publication Critical patent/WO2018163786A2/en
Publication of WO2018163786A3 publication Critical patent/WO2018163786A3/en

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/64—Three-dimensional objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • 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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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Molecular Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computing Systems (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Image Analysis (AREA)
  • Traffic Control Systems (AREA)

Abstract

A technique is provided that is able to increase recognition accuracy with respect to attributes of a target subject with a simple configuration. A target subject analysis apparatus according to an aspect of the present invention includes: a data acquisition unit configured to acquire image data that represents an image including a figure of a target subject, and range data that represents a value of a distance at each pixel constituting the image; a neural network computing unit configured to obtain an output value from a trained neural network for determining an attribute of the target subject, by performing computation processing with the neural network using the image data and the range data that are acquired as input to the neural network; and an attribute specifying unit configured to specify the attribute of the target subject based on the output value obtained from the neural network.
PCT/JP2018/005819 2017-03-07 2018-02-20 Target subject analysis apparatus, target subject analysis method, learning apparatus, and learning method WO2018163786A2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2017-042661 2017-03-07
JP2017042661A JP2018147286A (en) 2017-03-07 2017-03-07 Object analyzing apparatus, object analyzing method, learning apparatus, and learning method

Publications (2)

Publication Number Publication Date
WO2018163786A2 WO2018163786A2 (en) 2018-09-13
WO2018163786A3 true WO2018163786A3 (en) 2018-11-01

Family

ID=61656281

Family Applications (1)

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PCT/JP2018/005819 WO2018163786A2 (en) 2017-03-07 2018-02-20 Target subject analysis apparatus, target subject analysis method, learning apparatus, and learning method

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JP (1) JP2018147286A (en)
WO (1) WO2018163786A2 (en)

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Publication number Priority date Publication date Assignee Title
JP7160606B2 (en) * 2018-09-10 2022-10-25 株式会社小松製作所 Working machine control system and method
WO2020115866A1 (en) * 2018-12-06 2020-06-11 株式会社ＤｅｅｐＸ Depth processing system, depth processing program, and depth processing method
CN111381663A (en) * 2018-12-28 2020-07-07 技嘉科技股份有限公司 Efficiency optimization method of processor and mainboard using same
WO2021128343A1 (en) * 2019-12-27 2021-07-01 Siemens Aktiengesellschaft Method and apparatus for product quality inspection

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US7840342B1 (en) * 1997-10-22 2010-11-23 Intelligent Technologies International, Inc. Road physical condition monitoring techniques

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JPH02287860A (en) * 1989-04-28 1990-11-27 Omron Corp Information processor
JP3236361B2 (en) 1992-10-14 2001-12-10 株式会社リコー Motion control device
JP4532171B2 (en) 2004-06-01 2010-08-25 富士重工業株式会社 3D object recognition device
JP4799104B2 (en) * 2005-09-26 2011-10-26 キヤノン株式会社 Information processing apparatus and control method therefor, computer program, and storage medium
JP2011204195A (en) * 2010-03-26 2011-10-13 Panasonic Electric Works Co Ltd Device and method for inspection of irregularity
JP5642049B2 (en) * 2011-11-16 2014-12-17 クラリオン株式会社 Vehicle external recognition device and vehicle system using the same
JP6754619B2 (en) * 2015-06-24 2020-09-16 三星電子株式会社Ｓａｍｓｕｎｇ Ｅｌｅｃｔｒｏｎｉｃｓ Ｃｏ．，Ｌｔｄ． Face recognition method and device

Patent Citations (1)

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Publication number Priority date Publication date Assignee Title
US7840342B1 (en) * 1997-10-22 2010-11-23 Intelligent Technologies International, Inc. Road physical condition monitoring techniques

Non-Patent Citations (4)

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Title
CRISTIANO PREMEBIDA ET AL: "LIDAR and vision-based pedestrian detection system", JOURNAL OF FIELD ROBOTICS, vol. 26, no. 9, 1 September 2009 (2009-09-01), US, pages 696 - 711, XP055263851, ISSN: 1556-4959, DOI: 10.1002/rob.20312 *
EITEL ANDREAS ET AL: "Multimodal deep learning for robust RGB-D object recognition", 2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), IEEE, 28 September 2015 (2015-09-28), pages 681 - 687, XP032831667, DOI: 10.1109/IROS.2015.7353446 *
KIM HANSUNG ET AL: "Room Layout Estimation with Object and Material Attributes Information Using a Spherical Camera", 2016 FOURTH INTERNATIONAL CONFERENCE ON 3D VISION (3DV), IEEE, 25 October 2016 (2016-10-25), pages 519 - 527, XP033027660, DOI: 10.1109/3DV.2016.83 *
RAZAVIAN ALI SHARIF ET AL: "CNN Features Off-the-Shelf: An Astounding Baseline for Recognition", 2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, IEEE, 23 June 2014 (2014-06-23), pages 512 - 519, XP032649707, DOI: 10.1109/CVPRW.2014.131 *

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WO2018163786A2 (en) 2018-09-13
JP2018147286A (en) 2018-09-20

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