prikaz prve stranice dokumenta A deep learning model for estimation of human body measurements from images
  Download
PDF 55.72 MB
doctoral thesis
A deep learning model for estimation of human body measurements from images
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:965085
Bartol, Kristijan
University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Electronic Systems and Information Processing

Cite this document

Bartol, K. (2023). A deep learning model for estimation of human body measurements from images (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:965085

Bartol, Kristijan. "A deep learning model for estimation of human body measurements from images." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:965085

Bartol, Kristijan. "A deep learning model for estimation of human body measurements from images." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:965085

Bartol, K. (2023). 'A deep learning model for estimation of human body measurements from images', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 September 2023, https://urn.nsk.hr/urn:nbn:hr:168:965085

Bartol K. A deep learning model for estimation of human body measurements from images [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2023 September 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:965085

K. Bartol, "A deep learning model for estimation of human body measurements from images", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:965085

Please login to the repository to save this object to your list.
Metadata
TitleA deep learning model for estimation of human body measurements from images
Title (croatian)Model dubokog učenja za procjenu mjera ljudskog tijela iz slika
AuthorKristijan Bartol
MBZ: 387096
MentorTomislav Pribanić (mentor)
Committee memberTomislav Petković (predsjednik povjerenstva)
Committee memberMarko Subašić (član povjerenstva)
Committee memberDubravko Gajski (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
(Department of Electronic Systems and Information Processing)
Zagreb
Defense date and country2023-03-24, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Data Processing
Universal decimal classification
(UDC)		004 - Computer science and technology. Computing. Data processing
Abstract
The understanding of body measurements in and between populations is important and has many applications in medicine surveying, the fashion industry, fitness, and entertainment. Recent advances in human body measurement and shape estimation have been significantly driven by statistical models and deep learning, enabling methods that estimate 3D human meshes from 3D point clouds and 2D images - so called mesh regression methods. This thesis builds upon the state-of-the-art mesh regression approaches from multiple images. The first step is to propose the simplest method and use it as a baseline. The baseline is a linear regression models that takes only person's self-estimated height and weight and estimates the corresponding mesh. The baseline performs surprisingly well compared to the state-of-the-art methods. The second contribution is a 3D human pose estimation model from multiple camera views. The novelty of the model is in the fact that it can take any set of camera views as input, regardless of their relative arrangement and the number of cameras. The third contribution is a model for estimating the parameters of human pose, shape, and clothes from a single image. The estimated parameters are interpretable and, thus, controllable, which is a significant advantage compared to the previous approaches and important for many anthropometric applications. The three proposed models are evaluated in details and compared to the state-of-the-art methods.
Abstract (croatian)
Razumijevanje tjelesnih mjera unutar i između populacija važno je u brojnim primjenama u medicini, anketiranju, modnoj industriji, fitnessu i zabavnoj industriji. Nedavni napretci u mjerenju ljudskog tijela i procjeni njenog oblika su značajnim dijelom vođeni statističkim modelima i dubokim učenjem, omogućujući postupke koji procjenjuju 3D mreže tijela (engl. mesh) iz 3D oblaka točaka (engl. point clouds) i 2D slika, tzv. regresijske metode za 3D mreže. Ovaj doktorski rad nadogradnja je na najsuvremenije regresijske metode za 3D mreže iz više slika. Prvi korak je prijedlog najjednostavnije metode i njeno korištenje kao osnovice. Osnovica je model linearne regresije koja uzima jedino samoprocijenjenu visinu i težinu osobe i procjenjuje pripadnu 3D mrežu. Predložena osnovica radi iznenađujuće dobro u usporedbi s najsuvremenijim metodama. Drugi doprinos je model za procjenu 3D poze čovjeka iz više pogleda. Novost modela je u tome što na ulazu može primiti bilo koji skup kamera, neovisno o njihovom relativnom prostornom rasporedu i broju maera. Treći doprinos je model za procjenu parametara ljudskog položaja, oblika i odjeće iz jednog pogleda. Procijenjeni parametri su intepretabilni i, stoga, upravljivi, što je značajna prednost u odnosu na prijašnje pristupe i važna za mnoge primjene u antropometriji. Tri predložena modela detaljno su ocijenjena i uspoređena s najsuvremeniijim pristupima.
Keywords
Keywords (croatian)
Languageenglish
URN:NBNurn:nbn:hr:168:965085
Promotion2023
Study programmeTitle: Computer Science
Study programme type: university
Study level: postgraduate
Academic / professional title: Doktor znanosti (Doktor znanosti)
Type of resourceText
Extentvii, 133 str. : graf. prikazi ; 31 cm.
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
RepositoryFER Repository
Created on2023-09-19 16:03:44

(©) Nacionalna i sveučilišna knjižnica u Zagrebu 2015. Sva prava pridržana. | Hrvatske bratske zajednice 4 p.p. 550, 10000 Zagreb, Hrvatska Tel. ++ 385 1 616-4040
OIB: 84838770814 | IBAN: HR66 2340 0091 1000 1008 1, Porezni broj (PDV identifikacijski broj) - VAT Number: HR84838770814
Prijava u repozitorij