Detection of grapes in natural environment using HOG features in low resolution images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F17%3A39910985" target="_blank" >RIV/00216275:25530/17:39910985 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1088/1742-6596/870/1/012004" target="_blank" >http://dx.doi.org/10.1088/1742-6596/870/1/012004</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1742-6596/870/1/012004" target="_blank" >10.1088/1742-6596/870/1/012004</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Detection of grapes in natural environment using HOG features in low resolution images

Popis výsledku v původním jazyce

Detection of grapes in real-life images has importance in various viticulture applications. A grape detector based on an SVM classifier, in combination with a HOG descriptor, has proven to be very efficient in detection of white varieties in high-resolution images. Nevertheless, the high time complexity of such utilization was not suitable for its real-time applications, even when a detector of a simplified structure was used. Thus, we examined possibilities of the simplified version application on images of lower resolutions. For this purpose, we designed a method aimed at search for a detector’s setting which gives the best time complexity vs. performance ratio. In order to provide precise evaluation results, we formed new extended datasets. We discovered that even applied on low-resolution images, the simplified detector, with an appropriate setting of all tuneable parameters, was competitive with other state of the art solutions. We concluded that the detector is qualified for real-time detection of grapes in real-life images.

Název v anglickém jazyce

Detection of grapes in natural environment using HOG features in low resolution images

Popis výsledku anglicky

Detection of grapes in real-life images has importance in various viticulture applications. A grape detector based on an SVM classifier, in combination with a HOG descriptor, has proven to be very efficient in detection of white varieties in high-resolution images. Nevertheless, the high time complexity of such utilization was not suitable for its real-time applications, even when a detector of a simplified structure was used. Thus, we examined possibilities of the simplified version application on images of lower resolutions. For this purpose, we designed a method aimed at search for a detector’s setting which gives the best time complexity vs. performance ratio. In order to provide precise evaluation results, we formed new extended datasets. We discovered that even applied on low-resolution images, the simplified detector, with an appropriate setting of all tuneable parameters, was competitive with other state of the art solutions. We concluded that the detector is qualified for real-time detection of grapes in real-life images.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Journal of Physics: Conference Series

ISBN

—

ISSN

1742-6588

e-ISSN

neuvedeno

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

Institute of Physics Publishing Ltd

Místo vydání

Bristol

Místo konání akce

Praha

Datum konání akce

9. 6. 2017

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000412552100004