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Robust Grape Detector Based on SVMs and HOG Features
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-18
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
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 versus time-complexity ratio.
As our research showed, a conversion of RGB images to grayscale format, which is implemented at an image preprocessing 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 for both tuning and evaluation of the detectors.
Our effort resulted in a robust grape detector which is less sensitive to image distortion.
American Psychological Association (APA)
Škrabánek, Pavel& Doležel, Petr. 2017. Robust Grape Detector Based on SVMs and HOG Features. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-17.
https://search.emarefa.net/detail/BIM-1140900
Modern Language Association (MLA)
Škrabánek, Pavel& Doležel, Petr. Robust Grape Detector Based on SVMs and HOG Features. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-17.
https://search.emarefa.net/detail/BIM-1140900
American Medical Association (AMA)
Škrabánek, Pavel& Doležel, Petr. Robust Grape Detector Based on SVMs and HOG Features. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-17.
https://search.emarefa.net/detail/BIM-1140900
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1140900