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Title:Prepoznavanje oblik objektov z uporabo 3D kamere SICK Ranger3
Authors:ID Zadravec, Samo (Author)
ID Hace, Aleš (Mentor) More about this mentor... New window
ID Župerl, Uroš (Mentor) More about this mentor... New window
ID Munđar, Goran (Comentor)
Files:.pdf UN_Zadravec_Samo_2024.pdf (10,46 MB)
MD5: 57F620B2F121BC6B629E1D8D9E98CA3F
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomski nalogi je predstavljen razvoj, izgradnja, programiranje in rezultati sistema, ki natančno izdela 3D posnetek obravnavanega objekta in je zmožen prepoznati okrogle in pravokotne oblike s tega 3D posnetka. Prav tako se je sistem zmožen naučiti celotne oblike objekta preko prepoznavanja robov z uporabo 3D kamere SICK Ranger3 in senzorskega integracijskega modula SICK SIM4000. S tem sistemom bi se lahko pospešil postopek pregledovanja izdelkov v proizvodnji, hkrati pa močno zvišala natančnost pregledovanja izdelkov, kar bi zvišalo nivo kvalitete.
Keywords:SICK, 3D kamera, prepoznavanje oblik, učenje oblike, kontrola kakovosti
Place of publishing:Maribor
Publisher:[S. Zadravec]
Year of publishing:2024
PID:20.500.12556/DKUM-89765 New window
UDC:004.932.72'1(043.2)
COBISS.SI-ID:219378179 New window
Publication date in DKUM:19.09.2024
Views:144
Downloads:45
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:10.08.2024

Secondary language

Language:English
Title:Object shape recognition using 3D camera SICK Ranger3
Abstract:The thesis presents the development, construction, programming, and results of a system that accurately creates a 3D image of the object in question and is capable of recognizing circular and rectangular shapes from this 3D image. The system is also able to learn the entire shape of the object through edge recognition using the SICK Ranger3 3D camera and the SICK SIM4000 sensor integration module. This system could speed up the product inspection process in manufacturing while significantly increasing the accuracy of product inspections, thereby raising the quality level.
Keywords:SICK, 3D camera, shape recognition, shape learning, quality control


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