Leaf classification from binary image via artificial intelligence

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F16%3A00305211" target="_blank" >RIV/68407700:21340/16:00305211 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.biosystemseng.2015.12.007" target="_blank" >http://dx.doi.org/10.1016/j.biosystemseng.2015.12.007</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.biosystemseng.2015.12.007" target="_blank" >10.1016/j.biosystemseng.2015.12.007</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Leaf classification from binary image via artificial intelligence

Popis výsledku v původním jazyce

The invariant recognition of 2D binary images is the main subject of the paper. Two methods for invariant pattern recognition based on 2D Fourier power spectrum with guaranteed translation invariance are proposed. First method introduce the features invariant to translation, scaling, rotation and mirroring (TSO invariance). The second method introduces the features invariant to general affine transform (A invariance). The methods are used to obtain TSO/A invariant spectra except of the rotation effect which are analysed on circular paths with fixed radii. Harmonic analysis of power fluctuations around paths generates Fourier coefficients and their square absolute values are used as TSO/A invariant descriptors. The proposed methods were tested on two large sets of 2D digital images of tree leaves. After TSO/A invariant processing of thresholded digital images, kernel Support Vector Machine or self-organizing neural network were used for leaf categorisation.

Název v anglickém jazyce

Leaf classification from binary image via artificial intelligence

Popis výsledku anglicky

The invariant recognition of 2D binary images is the main subject of the paper. Two methods for invariant pattern recognition based on 2D Fourier power spectrum with guaranteed translation invariance are proposed. First method introduce the features invariant to translation, scaling, rotation and mirroring (TSO invariance). The second method introduces the features invariant to general affine transform (A invariance). The methods are used to obtain TSO/A invariant spectra except of the rotation effect which are analysed on circular paths with fixed radii. Harmonic analysis of power fluctuations around paths generates Fourier coefficients and their square absolute values are used as TSO/A invariant descriptors. The proposed methods were tested on two large sets of 2D digital images of tree leaves. After TSO/A invariant processing of thresholded digital images, kernel Support Vector Machine or self-organizing neural network were used for leaf categorisation.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

BIOSYSTEMS ENGINEERING

ISSN

1537-5110

e-ISSN

—

Svazek periodika

142

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

83-100

Kód UT WoS článku

000370100100006

EID výsledku v databázi Scopus

2-s2.0-84953313019