CN106133755A - 使用尖峰发放神经网络的图像的不变对象表示 - Google Patents

使用尖峰发放神经网络的图像的不变对象表示 Download PDF

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Publication number
CN106133755A
CN106133755A CN201580016091.0A CN201580016091A CN106133755A CN 106133755 A CN106133755 A CN 106133755A CN 201580016091 A CN201580016091 A CN 201580016091A CN 106133755 A CN106133755 A CN 106133755A
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spike
neuron
reference feature
fixed reference
counting
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Chinese (zh)
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P·阿格拉沃尔
S·马宗达
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Qualcomm Inc
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Qualcomm Inc
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06N3/042—Knowledge-based neural networks; Logical representations of neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G06F18/24137—Distances to cluster centroïds
    • G06F18/2414—Smoothing the distance, e.g. radial basis function networks [RBFN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/10—Interfaces, programming languages or software development kits, e.g. for simulating neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
    • G06V10/507—Summing image-intensity values; Histogram projection 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
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • 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
    • G06V10/82—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)
  • Software Systems (AREA)
  • Computing Systems (AREA)
  • Evolutionary Computation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Image Analysis (AREA)
CN201580016091.0A 2014-03-27 2015-03-23 使用尖峰发放神经网络的图像的不变对象表示 Pending CN106133755A (zh)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US14/228,065 US20150278641A1 (en) 2014-03-27 2014-03-27 Invariant object representation of images using spiking neural networks
US14/228,065 2014-03-27
PCT/US2015/021991 WO2015148369A2 (en) 2014-03-27 2015-03-23 Invariant object representation of images using spiking neural networks

Publications (1)

Publication Number Publication Date
CN106133755A true CN106133755A (zh) 2016-11-16

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CN201580016091.0A Pending CN106133755A (zh) 2014-03-27 2015-03-23 使用尖峰发放神经网络的图像的不变对象表示

Country Status (6)

Country Link
US (1) US20150278641A1 (enExample)
EP (1) EP3123403A2 (enExample)
JP (1) JP2017514215A (enExample)
KR (1) KR20160138042A (enExample)
CN (1) CN106133755A (enExample)
WO (1) WO2015148369A2 (enExample)

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US9195903B2 (en) * 2014-04-29 2015-11-24 International Business Machines Corporation Extracting salient features from video using a neurosynaptic system
US9373058B2 (en) 2014-05-29 2016-06-21 International Business Machines Corporation Scene understanding using a neurosynaptic system
US10115054B2 (en) 2014-07-02 2018-10-30 International Business Machines Corporation Classifying features using a neurosynaptic system
US9798972B2 (en) 2014-07-02 2017-10-24 International Business Machines Corporation Feature extraction using a neurosynaptic system for object classification
KR102565273B1 (ko) * 2016-01-26 2023-08-09 삼성전자주식회사 뉴럴 네트워크에 기초한 인식 장치 및 뉴럴 네트워크의 학습 방법
US11157798B2 (en) 2016-02-12 2021-10-26 Brainchip, Inc. Intelligent autonomous feature extraction system using two hardware spiking neutral networks with spike timing dependent plasticity
US20170236027A1 (en) * 2016-02-16 2017-08-17 Brainchip Inc. Intelligent biomorphic system for pattern recognition with autonomous visual feature extraction
US11151441B2 (en) 2017-02-08 2021-10-19 Brainchip, Inc. System and method for spontaneous machine learning and feature extraction
KR102607864B1 (ko) * 2018-07-06 2023-11-29 삼성전자주식회사 뉴로모픽 시스템 및 그것의 동작 방법
WO2020092691A1 (en) * 2018-11-01 2020-05-07 Peter Aj Van Der Made An improved spiking neural network
CN109978019B (zh) * 2019-03-07 2023-05-23 东北师范大学 图像模式识别模拟与数字混合忆阻设备及制备，实现stdp学习规则和图像模式识别方法
EP3944489B1 (en) * 2019-03-19 2024-07-03 Panasonic Intellectual Property Management Co., Ltd. Method, system and program for conversion of motor control model
KR102416924B1 (ko) 2020-01-28 2022-07-04 인하대학교 산학협력단 영상 영역 분할 방법, 영상 영역 분할 장치 및 영상 영역 분할 프로그램
US11282221B1 (en) * 2020-09-22 2022-03-22 Varian Medical Systems, Inc. Image contouring using spiking neural networks
KR102615194B1 (ko) * 2021-01-21 2023-12-19 한국과학기술연구원 시간 임베디드 부동 소수점 산술을 이용한 개선된 뉴런 코어

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US7606777B2 (en) * 2006-09-01 2009-10-20 Massachusetts Institute Of Technology High-performance vision system exploiting key features of visual cortex
US20120308136A1 (en) * 2010-03-26 2012-12-06 Izhikevich Eugene M Apparatus and methods for pulse-code invariant object recognition
US20120308076A1 (en) * 2010-03-26 2012-12-06 Filip Lukasz Piekniewski Apparatus and methods for temporally proximate object recognition
US8346692B2 (en) * 2005-12-23 2013-01-01 Societe De Commercialisation Des Produits De La Recherche Appliquee-Socpra-Sciences Et Genie S.E.C. Spatio-temporal pattern recognition using a spiking neural network and processing thereof on a portable and/or distributed computer
US20130325766A1 (en) * 2012-06-04 2013-12-05 Csaba Petre Spiking neuron network apparatus and methods

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US9412064B2 (en) * 2011-08-17 2016-08-09 Qualcomm Technologies Inc. Event-based communication in spiking neuron networks communicating a neural activity payload with an efficacy update
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US8346692B2 (en) * 2005-12-23 2013-01-01 Societe De Commercialisation Des Produits De La Recherche Appliquee-Socpra-Sciences Et Genie S.E.C. Spatio-temporal pattern recognition using a spiking neural network and processing thereof on a portable and/or distributed computer
US7606777B2 (en) * 2006-09-01 2009-10-20 Massachusetts Institute Of Technology High-performance vision system exploiting key features of visual cortex
US20120308136A1 (en) * 2010-03-26 2012-12-06 Izhikevich Eugene M Apparatus and methods for pulse-code invariant object recognition
US20120308076A1 (en) * 2010-03-26 2012-12-06 Filip Lukasz Piekniewski Apparatus and methods for temporally proximate object recognition
US20130325766A1 (en) * 2012-06-04 2013-12-05 Csaba Petre Spiking neuron network apparatus and methods

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T.MASQUELIER 等: "Unsupervised learning of visual features through spike timing deppendment plasticity", 《PLOS COMPUTATIONAL BIOLOGY》 *

Also Published As

Publication number Publication date
WO2015148369A3 (en) 2015-12-10
EP3123403A2 (en) 2017-02-01
KR20160138042A (ko) 2016-12-02
JP2017514215A (ja) 2017-06-01
US20150278641A1 (en) 2015-10-01
WO2015148369A2 (en) 2015-10-01

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