EP3516592A4 - Method for object detection in digital image and video using spiking neural networks - Google Patents

Method for object detection in digital image and video using spiking neural networks Download PDF

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Publication number
EP3516592A4
EP3516592A4 EP17851211.7A EP17851211A EP3516592A4 EP 3516592 A4 EP3516592 A4 EP 3516592A4 EP 17851211 A EP17851211 A EP 17851211A EP 3516592 A4 EP3516592 A4 EP 3516592A4
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EP
European Patent Office
Prior art keywords
video
digital image
object detection
neural networks
spiking neural
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP17851211.7A
Other languages
German (de)
French (fr)
Other versions
EP3516592A1 (en
Inventor
Yongqiang CAO
Qin Jiang
Yang Chen
Deepak Khosla
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
HRL Laboratories LLC
Original Assignee
HRL Laboratories LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US15/269,777 external-priority patent/US10198689B2/en
Application filed by HRL Laboratories LLC filed Critical HRL Laboratories LLC
Publication of EP3516592A1 publication Critical patent/EP3516592A1/en
Publication of EP3516592A4 publication Critical patent/EP3516592A4/en
Pending legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/047—Probabilistic or stochastic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
    • 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)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Data Mining & Analysis (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Computational Linguistics (AREA)
  • Mathematical Physics (AREA)
  • Neurology (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Probability & Statistics with Applications (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Image Analysis (AREA)
EP17851211.7A 2016-09-19 2017-05-23 Method for object detection in digital image and video using spiking neural networks Pending EP3516592A4 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US15/269,777 US10198689B2 (en) 2014-01-30 2016-09-19 Method for object detection in digital image and video using spiking neural networks
PCT/US2017/034093 WO2018052496A1 (en) 2016-09-19 2017-05-23 Method for object detection in digital image and video using spiking neural networks

Publications (2)

Publication Number Publication Date
EP3516592A1 EP3516592A1 (en) 2019-07-31
EP3516592A4 true EP3516592A4 (en) 2020-05-20

Family

ID=61618861

Family Applications (1)

Application Number Title Priority Date Filing Date
EP17851211.7A Pending EP3516592A4 (en) 2016-09-19 2017-05-23 Method for object detection in digital image and video using spiking neural networks

Country Status (3)

Country Link
EP (1) EP3516592A4 (en)
CN (1) CN109643390B (en)
WO (1) WO2018052496A1 (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110555523B (en) * 2019-07-23 2022-03-29 中建三局智能技术有限公司 Short-range tracking method and system based on impulse neural network
CN112465746B (en) * 2020-11-02 2024-03-05 新疆天维无损检测有限公司 Method for detecting small defects in ray film

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150347870A1 (en) * 2014-05-29 2015-12-03 International Business Machines Corporation Scene understanding using a neurosynaptic system

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050136509A1 (en) * 2003-09-10 2005-06-23 Bioimagene, Inc. Method and system for quantitatively analyzing biological samples
US8369652B1 (en) * 2008-06-16 2013-02-05 Hrl Laboratories, Llc Visual attention system for salient regions in imagery
AU2010201740B2 (en) * 2010-04-30 2013-03-07 Canon Kabushiki Kaisha Method, apparatus and system for performing a zoom operation
US9460387B2 (en) * 2011-09-21 2016-10-04 Qualcomm Technologies Inc. Apparatus and methods for implementing event-based updates in neuron networks
US9224090B2 (en) * 2012-05-07 2015-12-29 Brain Corporation Sensory input processing apparatus in a spiking neural network
US8977582B2 (en) * 2012-07-12 2015-03-10 Brain Corporation Spiking neuron network sensory processing apparatus and methods
US9123127B2 (en) * 2012-12-10 2015-09-01 Brain Corporation Contrast enhancement spiking neuron network sensory processing apparatus and methods
US9111355B1 (en) * 2013-03-13 2015-08-18 Hrl Laboratories, Llc Selective color processing for vision systems that enables optimal detection and recognition
US9195903B2 (en) * 2014-04-29 2015-11-24 International Business Machines Corporation Extracting salient features from video using a neurosynaptic system
US10055850B2 (en) * 2014-09-19 2018-08-21 Brain Corporation Salient features tracking apparatus and methods using visual initialization

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150347870A1 (en) * 2014-05-29 2015-12-03 International Business Machines Corporation Scene understanding using a neurosynaptic system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
FRINTROP SIMONE ET AL: "Traditional saliency reloaded: A good old model in new shape", 2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), IEEE, 7 June 2015 (2015-06-07), pages 82 - 90, XP032793433, DOI: 10.1109/CVPR.2015.7298603 *
See also references of WO2018052496A1 *

Also Published As

Publication number Publication date
WO2018052496A1 (en) 2018-03-22
CN109643390B (en) 2023-04-18
CN109643390A (en) 2019-04-16
EP3516592A1 (en) 2019-07-31

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