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Title:Primerjava zaznave in segmentacije neznanih objektov v 6 prostostnih stopnjah : magistrsko delo
Authors:ID Herženjak, Nejc (Author)
ID Gotlih, Karl (Mentor) More about this mentor... New window
ID Hace, Aleš (Mentor) More about this mentor... New window
Files:.pdf MAG_Herzenjak_Nejc_2023.pdf (7,57 MB)
MD5: 3740C8ADF51B9EAFFD9F7043039D74D5
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FS - Faculty of Mechanical Engineering
Abstract:Cilj diplomskega dela je analizirati različne pristope odkrivanja in segmentacije neznanih predmetov ter primerjati podobnosti in razlike algoritmov. Raziskava temelji na ‟No Free Lunch Theorem‟ in je osredotočena na iskanje najustreznejših pristopov strojnega vida v robotiki in primerjavo njihove učinkovitosti. Na začetku je narejen pregled stanja raziskav z delitvijo na regresijske in klasifikacijske metode, zatem sledi segmentacija in prepoznava objektov ter se zaključi z opisom klasičnih in metod z nevronskim mreženjem. V nadaljevanju je opredeljena primerjava nabora podatkov in že narejenih meritev. Zadnji dve poglavji sta namenjeni evalvaciji lastnih poskusov in zaključek s priložnostmi nadaljnjih raziskav na tem področju.
Keywords:6-DoF, strojni vid, evalvacija pozicije, segmentacija slike, zaznavanje objektov
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[N. Herženjak]
Year of publishing:2023
Number of pages:1 spletni vir (1 datoteka PDF (VII, 77 f.))
PID:20.500.12556/DKUM-84091 New window
UDC:004.932(043.2)
COBISS.SI-ID:161956355 New window
Publication date in DKUM:08.06.2023
Views:640
Downloads:85
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FS
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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:10.04.2023

Secondary language

Language:English
Title:Comparison of detection and segmentation of unknown objects for 6 dof pose estimation
Abstract:The thesis work aims to analyse different approaches of detection and segmentation of unknown objects and to compare the similarities and differences of algorithms. Research is based on No Free Lunch Theorem on finding the most appropriate approaches of machine vision in robotics and comparing their efficiency. At the beginning, an overview of the state of the art is made, dividing it into regression and classification methods, followed by segmentation and object recognition, and ends with a description of classical and neural network methods. In the following, a comparison of the data set and already made measurements is defined. The last two chapters are devoted to the evaluation of own experiments and the conclusion with opportunities for further research in this area.
Keywords:6-DoF, machine vision, pose evaluation, image segmentation, object detection


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