Robust Grape Detector Based on SVMs and HOG Features
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F17%3A39902713" target="_blank" >RIV/00216275:25530/17:39902713 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1155/2017/3478602" target="_blank" >http://dx.doi.org/10.1155/2017/3478602</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1155/2017/3478602" target="_blank" >10.1155/2017/3478602</a>
Alternative languages
Result language
angličtina
Original language name
Robust Grape Detector Based on SVMs and HOG Features
Original language description
Detection of grapes in real-life images is a serious task solved by researchers dealing with precision viticulture. In the case of white wine varieties, grape detectors based on SVMs classifiers, in combination with a HOG descriptor, have proven to be very efficient. Simplified versions of the detectors seem to be the best solution for practical applications. They offer the best known performance vs. time-complexity ratio. As our research showed, a conversion of RGB images to grayscale format, which is implemented at an image pre-processing level, is ideal means for further improvement of performance of the detectors. In order to enhance the ratio, we explored relevance of the conversion in a context of a detector potential sensitivity to a rotation of berries. For this purpose, we proposed a modification of the conversion, and we designed an appropriate method for a tuning of such modified detectors. To evaluate the effect of the new parameter space on their performance, we developed a specialized visualization method. In order to provide accurate results, we formed new datasets both for tuning and evaluation of the detectors. Our effort resulted in a robust grape detector which is less sensitive to image distortion.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computational Intelligence and Neuroscience
ISSN
1687-5265
e-ISSN
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Volume of the periodical
2017
Issue of the periodical within the volume
18 May 2017
Country of publishing house
GB - UNITED KINGDOM
Number of pages
17
Pages from-to
1-17
UT code for WoS article
000402326000001
EID of the result in the Scopus database
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