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Title:Razpoznavanje kovancev v digitalnih slikah s pomočjo računalniškega vida in strojnega učenja
Authors:Vračko, Tjaž (Author)
Potočnik, Božidar (Mentor) More about this mentor... New window
Files:.pdf UN_Vracko_Tjaz_2018.pdf (3,01 MB)
 
Language:Slovenian
Work type:Bachelor thesis/paper (mb11)
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomskem delu smo raziskali metode računalniškega vida za detekcijo in razpoznavanje evrskih kovancev v digitalnih slikah. Preučili in analizirali smo obstoječe metode za detekcijo kovancev ter predstavili njihove prednosti in slabosti. V delu predstavimo lasten algoritem za detekcijo in razpoznavo evrskih kovancev, ki temelji na Houghovi transformaciji, barvnih in teksturnih značilnicah, postopku na osnovi vreče besed in strojih podpornih vektorjev; za popravljanje rezultatov razpoznave uporablja informacije o velikostnih razmerjih kovancev. Algoritem smo implementirali in testirali na štirih testnih množicah slik. Ugotovili smo, da naš algoritem deluje najbolje na slikah z belim ozadjem, na katerih se nahaja veliko različnih tipov kovancev. Na takšnih slikah smo v povprečju dosegli 81,53-odstotno uspešnost pravilnega razpoznavanja kovancev. Izkazalo se je tudi, da je 20 cm tista oddaljenost kamere od kovancev, pri kateri dosežemo najvišjo uspešnost razpoznave in ustreza razdalji, ki bi jo tudi sicer izbrali za slikanje od 10 do 30 kovancev, položenih na mizo.
Keywords:razpoznavanje kovancev, HOG, SIFT, Houghova transformacija, stroji podpornih vektorjev
Year of publishing:2018
Publisher:T. Vračko
Source:[Maribor
UDC:004.921(043.2)
COBISS_ID:21412630 Link is opened in a new window
License:CC BY-NC-ND 4.0
This work is available under this license: Creative Commons Attribution Non-Commercial No Derivatives 4.0 International
Views:270
Downloads:42
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:Recognition of coins in digital images by using computer vision and machine learning
Abstract:In this paper we present our research on computer vision methods pertaining to the recognition of euro coins in digital images. We studied and analysed existing coin recognition methods and present their strengths and weaknesses. We introduce our own algorithm for recognising euro coins, which is based on the Hough transform, different colour and texture descriptors, as well as the bag of words principle and support vector machines. It uses knowledge about coin size ratios to later correct the results of the classification process. We have implemented the proposed algorithm and tested it on four testing sets of images. Our algorithm works best on images with a white background, on which multiple kinds of coins are located. On such images we have achieved an average correct coin recognition ratio of 81.53%. The results also show that a camera distance of 20 cm from the coins produces the highest recognition rate. This distance matches one naturally chosen when taking a picture of roughly 10 to 30 coins, laid flat onto a table.
Keywords:coin recognition, HOG, SIFT, Hough transform, support vector machines


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