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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • 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

  • 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