WO2023143702 - TERAHERTZ RADIATION BASED METHOD AND SYSTEM FOR IDENTIFICATION OF OBJECTS AND PREDICTION OF OBJECT CLASS

National phase entry:
Publication Number WO/2023/143702
Publication Date 03.08.2023
International Application No. PCT/EP2022/051677
International Filing Date 26.01.2022
Title **
[English] TERAHERTZ RADIATION BASED METHOD AND SYSTEM FOR IDENTIFICATION OF OBJECTS AND PREDICTION OF OBJECT CLASS
[French] PROCÉDÉ ET SYSTÈME BASÉS SUR UN RAYONNEMENT TÉRAHERTZ POUR L'IDENTIFICATION D'OBJETS ET LA PRÉDICTION DE CLASSE D'OBJETS
Applicants **
ROBERT BOSCH GMBH
Inventors
YAN, Wai
KANUPRIYA, Malhotra
NGO, Chi Trung
TAN, Tian
MOOKHERJEE, Debosmit
MIYAJIMA, Masafumi
ANDALAM, Sidharta
JEON, Jin Han
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Quotation for National Phase entry

Country StagesTotal
China Filing, Examination, Granting2024
EPO Filing, Examination, Granting9788
Japan Filing, Examination, Granting2277
South Korea Filing, Examination, Granting2246
USA Filing, Examination, Granting4740
MasterCard Visa
Total: 21,075

The term for entry into the National Phase has expired. This quotation is for informational purposes only

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Abstract[English] Aspects concern a method and system for classifying an object. The method includes receiving a power intensity heatmap image associated with the object, identifying at least one region of interest (ROI) on the power intensity heatmap image; extracting important features from the ROI used to differentiate between objects, and classifying the object based on the extracted features. Deep machine learning and/or artificial intelligence modules may be utilized for the identification of ROI, extracting of features, and/or the classification.[French] Certains aspects de la présente invention concernent un procédé et un système pour la classification d'un objet. Le procédé comprend la réception d'une image de carte de densité de clics de puissance associée à l'objet, l'identification d'au moins une région d'intérêt (ROI) sur l'image de carte de densité de clics de puissance; l'extraction des caractéristiques importantes à partir de la région ROI utilisées pour différencier des objets, et la classification de l'objet sur la base des caractéristiques extraites. Des modules d'apprentissage automatique profond et/ou d'intelligence artificielle peuvent être utilisés pour l'identification de la région ROI, l'extraction de caractéristiques et/ou la classification.