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1. (WO2019043458) SUPERRESOLUTION METROLOGY METHODS BASED ON SINGULAR DISTRIBUTIONS AND DEEP LEARNING
Latest bibliographic data on file with the International Bureau    Submit observation

Pub. No.: WO/2019/043458 International Application No.: PCT/IB2018/001129
Publication Date: 07.03.2019 International Filing Date: 30.08.2018
IPC:
G06K 9/00 (2006.01) ,G06K 9/20 (2006.01) ,G06K 9/46 (2006.01) ,G06K 9/52 (2006.01) ,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
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
20
Image acquisition
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
36
Image preprocessing, i.e. processing the image information without deciding about the identity of the image
46
Extraction of features or characteristics of the image
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
36
Image preprocessing, i.e. processing the image information without deciding about the identity of the image
46
Extraction of features or characteristics of the image
52
by deriving mathematical or geometrical properties from the whole image
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:
BIOAXIAL SAS [FR/FR]; 29 rue du Faubourg Saint-Jacques 75014 Paris, FR
Inventors:
SIRAT, Gabriel, Y.; FR
Agent:
CHAUVIN, Vincent; Jacobacci Coralis Harle 32, rue de l'Arcade 75008 Paris, FR
Priority Data:
62/551,90630.08.2017US
62/551,91330.08.2017US
Title (EN) SUPERRESOLUTION METROLOGY METHODS BASED ON SINGULAR DISTRIBUTIONS AND DEEP LEARNING
(FR) PROCÉDÉS DE MÉTROLOGIE À SUPER RÉSOLUTION BASÉS SUR DES DISTRIBUTIONS SINGULIÈRES ET UN APPRENTISSAGE PROFOND
Abstract:
(EN) Methods for determining a value of an intrinsic geometrical parameter of a geometrical feature characterizing a physical object, and for classifying a scene into at least one geometrical shape, each geometrical shape modeling a luminous object. A singular light distribution characterized by a first wavelength and a position of singularity is projected onto the physical object. Light excited by the singular light distribution that has interacted with the geometrical feature and that impinges upon a detector is detected and a return energy distribution is identified and quantified at one or more positions. A deep learning or neural network layer may be employed, using the detected light as direct input of the neural network layer, adapted to classify the scene, as a plurality of shapes, static or dynamic, the shapes being part of a set of shapes predetermined or acquired by learning.
(FR) La présente invention concerne des procédés pour déterminer une valeur d’un paramètre géométrique intrinsèque d’une caractéristique géométrique caractérisant un objet physique, et pour classer une scène en au moins une forme géométrique, chaque forme géométrique modélisant un objet lumineux. Une distribution singulière de lumière, caractérisée par une première longueur d’onde et une position de singularité, est projetée sur l’objet physique. Une lumière excitée par la distribution singulière de lumière, qui a interagi avec la caractéristique géométrique et qui est incidente sur un détecteur, est détectée, et une distribution d’énergie renvoyée est identifiée et quantifiée au niveau d'une ou de plusieurs positions. Une couche d’apprentissage profond ou de réseau neuronal peut être employée, en utilisant la lumière détectée comme entrée directe de la couche de réseau neuronal, et adaptée pour classer la scène, en tant que pluralité de formes, statiques ou dynamiques, les formes faisant partie d’un ensemble de formes prédéterminées ou acquises par apprentissage.
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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, DJ, DK, DM, DO, 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, KR, KW, 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: English (EN)
Filing Language: English (EN)