Robust Grape Detector Based on SVMs and HOG Features
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 detec...
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Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2017/3478602 |
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doaj-460480e1bd24439fa6e765cd2191cb362020-11-24T23:00:29ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732017-01-01201710.1155/2017/34786023478602Robust Grape Detector Based on SVMs and HOG FeaturesPavel Škrabánek0Petr Doležel1Department of Process Control, University of Pardubice, Pardubice, Czech RepublicDepartment of Process Control, University of Pardubice, Pardubice, Czech RepublicDetection 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.http://dx.doi.org/10.1155/2017/3478602 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pavel Škrabánek Petr Doležel |
spellingShingle |
Pavel Škrabánek Petr Doležel Robust Grape Detector Based on SVMs and HOG Features Computational Intelligence and Neuroscience |
author_facet |
Pavel Škrabánek Petr Doležel |
author_sort |
Pavel Škrabánek |
title |
Robust Grape Detector Based on SVMs and HOG Features |
title_short |
Robust Grape Detector Based on SVMs and HOG Features |
title_full |
Robust Grape Detector Based on SVMs and HOG Features |
title_fullStr |
Robust Grape Detector Based on SVMs and HOG Features |
title_full_unstemmed |
Robust Grape Detector Based on SVMs and HOG Features |
title_sort |
robust grape detector based on svms and hog features |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5265 1687-5273 |
publishDate |
2017-01-01 |
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 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. |
url |
http://dx.doi.org/10.1155/2017/3478602 |
work_keys_str_mv |
AT pavelskrabanek robustgrapedetectorbasedonsvmsandhogfeatures AT petrdolezel robustgrapedetectorbasedonsvmsandhogfeatures |
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