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1. (WO2017032775) ACTIVE MACHINE LEARNING FOR TRAINING AN EVENT CLASSIFICATION
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Pub. No.: WO/2017/032775 International Application No.: PCT/EP2016/069914
Publication Date: 02.03.2017 International Filing Date: 23.08.2016
IPC:
G06K 9/62 (2006.01)
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
K
RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9
Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62
Methods or arrangements for recognition using electronic means
Applicants:
CARL ZEISS AG [DE/DE]; Carl-Zeiss-Str. 22 73447 Oberkochen, DE
CARL ZEISS MICROSCOPY GMBH [DE/DE]; Carl-Zeiss-Promenade 10 07745 Jena, DE
Inventors:
KANDEMIR, Melih; TR
HAMPRECHT, Fred; DE
WOJEK, Christian; DE
SCHMIDT, Ute; DE
Agent:
NEUSSER, Sebastian; DE
Priority Data:
10 2015 114 015.224.08.2015DE
Title (EN) ACTIVE MACHINE LEARNING FOR TRAINING AN EVENT CLASSIFICATION
(FR) APPRENTISSAGE MACHINE ACTIF POUR L'ENTRAÎNEMENT D'UN CLASSIFICATEUR D'ÉVÉNEMENTS
(DE) AKTIVES MASCHINELLES LERNEN ZUM TRAINIEREN EINES EREIGNISKLASSIFIKATORS
Abstract:
(EN) An event classification (131) is trained by means of machine learning. To this end, an anomaly detection (121) for detecting events in an image data set (110, 111) is carried out. Based on the performing of the anomaly detection (121), a model assumption (130) of the event classification (131) is determined.
(FR) Une classification d'événements (131) est entraînée en faisant appel à l'apprentissage machine. A cet effet, une détection d'anomalies (121) est exécutée dans le but de reconnaître des événements dans un enregistrement d'images (110, 111). Une hypothèse modèle (130) de la classification d'événéments (131) est déterminée sur la base de l'exécution de la détection d'anomalies (121).
(DE) Eine Ereignisklassifikation (131) wird mittels maschinellem Lernen trainiert. Dabei wird eine Anomaliedetektion (121) zum Erkennen von Ereignissen in einem Bilddatensatz (110, 111) durchgeführt. Basierend auf dem Durchführen der Anomaliedetektion (121) wird eine Modellannahme (130) der Ereignisklassifikation (131) bestimmt.
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Designated States: AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JP, KE, KG, KN, KP, KR, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, 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
African Regional Intellectual Property Organization (ARIPO) (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW)
Eurasian Patent Office (AM, AZ, BY, KG, KZ, RU, TJ, TM)
European Patent Office (EPO) (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)
African Intellectual Property Organization (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG)
Publication Language: German (DE)
Filing Language: German (DE)