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Title:
AVTOMATSKO PREPOZNAVANJE OČI IZ DIGITALNIH POSNETKOV
Authors:
ID
Jerovšek, Tadej
(Author)
ID
Potočnik, Božidar
(Mentor)
More about this mentor...
Files:
UNI_Jerovsek_Tadej_2014.pdf
(2,10 MB)
MD5: 2F57B3D14652143FA67A6FAE490ECE9F
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V tem diplomskem delu se ukvarjamo z detektiranjem oči v barvnih digitalnih posnetkih ter segmentacijo le-teh, saj večina obstoječih metod vrača kot rezultat le oklepajočo škatlo očesa. Pri reševanju tega problema smo za detekcijo beločnice uporabili barvni prostor HSV, za iskanje šarenice smo uporabili prileganje modela, pri določitvi zenice pa smo uporabili nevronsko mrežo. Naš algoritem smo nato validirali na bazi 50 slik, kjer smo ugotavljali uspešnost za posamezne komponente očesa. Pri tem smo ugotovili, da pri večini razpoznamo komponento v dobri meri, vendar pri nekaterih razpoznamo tudi napačni del slike. Kot izhod oblikujemo maske posameznih komponent, ki jih lahko uporabimo za nadaljnja dela z očmi.
Keywords:
računalniški vid
,
prepoznavanje oči
,
razpoznavanje vzorcev
,
digitalna obdelava slik
,
Houghova transformacija
,
nevronske mreže
Place of publishing:
Maribor
Publisher:
[T. Jerovšek]
Year of publishing:
2014
PID:
20.500.12556/DKUM-44277
UDC:
004.932.72(043.2)
COBISS.SI-ID:
17991958
NUK URN:
URN:SI:UM:DK:B4CAVPJ7
Publication date in DKUM:
16.06.2014
Views:
2616
Downloads:
139
Metadata:
Categories:
KTFMB - FERI
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:
JEROVŠEK, Tadej, 2014,
AVTOMATSKO PREPOZNAVANJE OČI IZ DIGITALNIH POSNETKOV
[online]. Bachelor’s thesis. Maribor : T. Jerovšek. [Accessed 19 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=44277
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Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.
Secondary language
Language:
English
Title:
AUTOMATED EYE RECOGNITION FROM DIGITAL IMAGES
Abstract:
In the diploma thesis we deal with detecting eyes in color digital images and their segmentation, because most existing methods return only a bounding box as a result. To solve this problem, we used the HSV color space for detecting the sclera, model fitting for finding the iris and we used a neural network for determining the pupil. We then validated our algorithm on a base of 50 images, where we determined its success rate for individual components of the eye. With this we determined that in most pictures we identify the component in a good measure, but in some we also identify a false part of the image. We form masks of individual components as an output, which can then be used for further work with eyes.
Keywords:
computer vision
,
eye recognition
,
pattern recognition
,
digital image processing
,
Hough transform
,
neural networks
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