diplomsko delo
Matic Švab (Author), Matej Kristan (Mentor)

Abstract

V diplomski nalogi je predstavljen postopek razpoznavanja dreves s pomočjo računalniškega vida, ki analizira drevesno lubje. Iz posameznih slik lubja postopek izlušči značilnice LBP, ki jih SVM uporabi za učenje in testiranje. Ker prosto dostopna zbirka slik drevesnega lubja ne obstaja, je bilo potrebno zajeti večjo anotirano zbirko slik lubja, ki je tudi prva javno dostopna zbirka. Pri razpoznavanju se pojavi še problem določitev skale, saj različne naprave zajamejo slike različnih velikosti, različnih razmerjih širine/višine slike predvsem pa ljudje ne slikajo enako oddaljeni od dreves. V diplomski nalogi je tudi predlagan postopek, ki s pomočjo značilnic, pridobljenih s detektorjem DoG, samodejno določi skalo slike, s katero se vhodna slika pred izračunom LBP-ja vedno preskalira v referenčno velikost in s tem teži k normalizirani velikosti pomembnih struktur v slikah. Končni eksperiment je na zbirki 12 dreves dosegel 84.62 % natančnost.

Keywords

lokalni binarni vzorci;metoda podpornih vektorjev;klasifikacija dreves;samodejno določanje skale;računalništvo;računalništvo in informatika;univerzitetni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [M. Švab]
UDC: 004.93:582.091(043.2)
COBISS: 1536123331 Link will open in a new window
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Other data

Secondary language: English
Secondary title: Computer-vision-based tree trunk recognition
Secondary abstract: This thesis presents a process of a tree recognition by means of the computer vision, which analyses the tree bark. The procedure extracts LBP features from individual pictures of bark, which are used for training and testing by SVM. Since freely accessible collection of tree bark pictures does not exist, it was necessary to create a larger annotated collection which is also the first database publicly available. In recognition there is also a problem with scale or picture size, because different devices take pictures of different sizes, in different width/height proportions and mostly people do not take photographs from the same distance. The thesis also proposes a procedure that by means of the features gained by DoG detector, automatically determines the picture scale, by means of which the input picture is always rescaled in the reference size before the calculation of LBP. In the final experiment the 84.62 % accuracy was achieved on the collection of 12 trees.
Secondary keywords: local binary patterns;support vector machine;tree classification;automatic scale determination;computer science;computer and information science;diploma;
File type: application/pdf
Type (COBISS): Bachelor thesis/paper
Study programme: 1000468
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 46 str.
ID: 8739470