SG11201903022UA - Predictive models for visually classifying insects - Google Patents

Predictive models for visually classifying insects

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Publication number
SG11201903022UA
SG11201903022UA SG11201903022UA SG11201903022UA SG11201903022UA SG 11201903022U A SG11201903022U A SG 11201903022UA SG 11201903022U A SG11201903022U A SG 11201903022UA SG 11201903022U A SG11201903022U A SG 11201903022UA SG 11201903022U A SG11201903022U A SG 11201903022UA
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SG
Singapore
Prior art keywords
verily
south san
california
insect
international
Prior art date
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SG11201903022UA
Inventor
Tiantian Zha
Yaniv Ovadia
Daniel Newburger
Dilip Krishnan
Josh Livni
Mark Desnoyer
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Verily Life Sciences Llc
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Publication of SG11201903022UA publication Critical patent/SG11201903022UA/en

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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
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/255—Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
    • 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
    • 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
    • G06N3/045—Combinations of 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
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
    • 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
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/06—Recognition of objects for industrial automation

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
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  • Multimedia (AREA)
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  • General Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • Quality & Reliability (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Image Analysis (AREA)
  • Catching Or Destruction (AREA)
  • Image Processing (AREA)

Abstract

INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property C.--.` MD HIM 011101011M 01 11010101111111111011110011101101110111 OEN Organization International Bureau (10) International Publication Number 03 (43) International Publication Date ......P' WO 2018/081640 Al 03 May 2018 (03.05.2018) WIP0 I PCT (51) International Patent Classification: Sciences LLC, 269 East Grand Ave., South San Francis- GOOF 19/00 (2011.01) GOOK 9/62 (2006.01) co, California 94080 (US). KRISHNAN, Dilip; c/o Verily GOOK 9 / 4 6 (2006.01) G06T 7/00 (2017.01) Life Sciences LLC, 269 East Grand Ave., South San Fran- GOOK 9 / 5 2 (2006.01) cisco, California 94080 (US). L1VNI, Josh; c/o Verily Life Sciences LLC, 269 East Grand Ave., South San Francis- (21) International Application Number: PCT/US2017/058872 co, California 94080 (US). DESNOYER, Mark; c/o Verily Life Sciences LLC, 269 East Grand Ave., South San Fran- (22) International Filing Date: cisco, California 94080 (US). 27 October 2017 (27.10.2017) (74) Agent: MCALLISTER, Tyler et al.; Kilpatrick Townsend (25) Filing Language: English & Stockton LLP, Mailstop: IP Docketing - 22, 1100 Peachtree Street, N.E., Suite 2800, Atlanta, Georgia 30309 (26) Publication Language: English (US). (30) Priority Data: (81) Designated States (unless otherwise indicated, for every 62/414,597 28 October 2016 (28.10.2016) US kind of national protection available): AE, AG, AL, AM, (71) Applicant: VERILY LIFE SCIENCES LLC [US/US]; AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, 269 East Grand Ave., South San Francisco, California CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, 94080 (US). DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, (72) Inventors: ZHA, Tiantian; c/o Verily Life Sciences LLC, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, — 269 East Grand Ave., South San Francisco, California MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, 94080 (US). OVADIA, Yaniv; c/o Verily Life Sciences OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, = LLC, 269 East Grand Ave., South San Francisco, Califor- nia 94080 (US). NEWBURGER, Daniel; c/o Verily Life = = (54) Title: PREDICTIVE MODELS FOR VISUALLY CLASSIFYING INSECTS = = = 102 1, 1 . 104 - 1 6 — _.I _ 108 ACCESS 114 AN WAGE DEPICTING PLURALITY OF INSECTS 112 — = = 1208 I-120N 1145 114N .._ _. • IDEN1IFY PATCH FROM MAT DEPICTS INSECT= AN IMAGE THE IMAGE AN = = = 106 = USE A PREILTAIE - i = PREDICTIVE MODE JIQ MOOEL TO EVALUATE IMAGE PATCH TO „)[ACRE INSECT(S). CLASSTY INSECT(S), AND/OR COUNT 106 iNsEcr(s)j22 11 .4 © 71' V; JJ 06 OUTPUT INSECT IN FROM THE PREDICTIVE MODEL CORRESPOIOING 10 THE EVALUATION OF THE IMAGE PATCH 124 11 GC FIG. 1 © . --- - GC (57) : Insects can be localized and classified using a predictive model. To begin, image data is obtained that corresponds to the 1 -1 insects.. Using a predictive model, samples of the image data are evaluated to determine whether the image portions include an insect 0 and, if so, into what category the insect should be classified (e.g., male/female, species A/species B, etc.). ei o [Continued on next page] WO 2018/081640 Al MIDEDIMOMMIDIRERIONEHOHINHOIHMEEN SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG). Published: — with international search report (Art. 21(3))
SG11201903022UA 2016-10-28 2017-10-27 Predictive models for visually classifying insects SG11201903022UA (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201662414597P 2016-10-28 2016-10-28
PCT/US2017/058872 WO2018081640A1 (en) 2016-10-28 2017-10-27 Predictive models for visually classifying insects

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US (1) US11151423B2 (en)
EP (1) EP3532962B1 (en)
CN (1) CN109964235A (en)
AU (1) AU2017350945B2 (en)
ES (1) ES2956102T3 (en)
PT (1) PT3532962T (en)
SG (1) SG11201903022UA (en)
WO (1) WO2018081640A1 (en)

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WO2018081640A1 (en) 2018-05-03
EP3532962A4 (en) 2020-09-09
AU2017350945B2 (en) 2021-11-11
US20180121764A1 (en) 2018-05-03
AU2017350945A1 (en) 2019-05-02
ES2956102T3 (en) 2023-12-13
AU2017350945A9 (en) 2019-05-16
EP3532962A1 (en) 2019-09-04
US11151423B2 (en) 2021-10-19

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