An Adaptive Approach for Automated Grapevine Phenotyping using VGG-based
Convolutional Neural Networks

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An Adaptive Approach for Automated Grapevine Phenotyping using VGG-based Convolutional Neural Networks

23 November 2018

Volker Steinhage

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Papers citing "An Adaptive Approach for Automated Grapevine Phenotyping using VGG-based Convolutional Neural Networks"

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Title
Efficient identification, localization and quantification of grapevine inflorescences in unprepared field images using Fully Convolutional Networks Robert Rudolph Katja Herzog R. Töpfer Volker Steinhage 33 14 0 10 Jul 2018
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation Adam Paszke Abhishek Chaurasia Sangpil Kim Eugenio Culurciello SSeg 324 2,083 0 07 Jun 2016
Fully Convolutional Networks for Semantic Segmentation Evan Shelhamer Jonathan Long Trevor Darrell VOS SSeg 744 37,890 0 20 May 2016
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan Alex Kendall R. Cipolla SSeg 1.1K 15,819 0 02 Nov 2015
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 1.9K 77,378 0 18 May 2015
Learning Deconvolution Network for Semantic Segmentation Hyeonwoo Noh Seunghoon Hong Bohyung Han SSeg 235 4,180 0 17 May 2015
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.7K 100,508 0 04 Sep 2014