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Detection of grapes in natural environment using HOG features in low resolution images
Authors: Škrabánek Pavel | Majerík Filip
Year: 2017
Type of publication: článek ve sborníku
Name of source: Journal of Physics: Conference Series
Publisher name: Institute of Physics Publishing Ltd
Place: Bristol
Page from-to: 1-8
Titles:
Language Name Abstract Keywords
cze Detekce hroznů v obrazu s nízkým rozlišením s využitím HOG deskriptoru Detekce hroznů v reálném obraze má velký význam v zemědělských aplikacích. Detektor hroznů založený na SVM klasifikátoru a HOG deskriptoru se v minulosti ukázal jak velice efektivní nástroj pro detekci hroznů v obrazech s vysokým rozlišením. V tomto příspěvku je představena jeho modifikovaná verze, která dokáže detekovat hrozny i v obrazech s malým rozlišením.
eng Detection of grapes in natural environment using HOG features in low resolution images 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. grape detection; precision viticulture; real scene images; image processing; histogram of oriented gradients; support vector machine

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