Semantic Segmentation of Vineyard Images Using Convolutional Neural Networks

2020 ◽  
pp. 292-303 ◽  
Author(s):  
Theofanis Kalampokas ◽  
Konstantinos Tziridis ◽  
Alexandros Nikolaou ◽  
Eleni Vrochidou ◽  
George A. Papakostas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation

scholarly journals Semantic Segmentation of Cerebellum in 2D Fetal Ultrasound Brain Images using Convolutional Neural Networks

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Vishal Singh ◽  
Pradeeba Sridar ◽  
Jinman Kim ◽  
Ralph Nanan ◽  
N. Poornima ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Fetal Ultrasound ◽  
Brain Images

scholarly journals Convolutional Neural Networks for Semantic Segmentation as a Tool for Multiclass Face Analysis in Thermal Infrared

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
David Müller ◽  
Andreas Ehlen ◽  
Bernd Valeske
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Thermal Infrared ◽  
Relative Temperature ◽  
Face Analysis ◽  
Image Translation ◽  
The Face ◽  
Live Image ◽  
Quantitative Thermography

AbstractConvolutional neural networks were used for multiclass segmentation in thermal infrared face analysis. The principle is based on existing image-to-image translation approaches, where each pixel in an image is assigned to a class label. We show that established networks architectures can be trained for the task of multiclass face analysis in thermal infrared. Created class annotations consisted of pixel-accurate locations of different face classes. Subsequently, the trained network can segment an acquired unknown infrared face image into the defined classes. Furthermore, face classification in live image acquisition is shown, in order to be able to display the relative temperature in real-time from the learned areas. This allows a pixel-accurate temperature face analysis e.g. for infection detection like Covid-19. At the same time our approach offers the advantage of concentrating on the relevant areas of the face. Areas of the face irrelevant for the relative temperature calculation or accessories such as glasses, masks and jewelry are not considered. A custom database was created to train the network. The results were quantitatively evaluated with the intersection over union (IoU) metric. The methodology shown can be transferred to similar problems for more quantitative thermography tasks like in materials characterization or quality control in production.

Semantic Segmentation of Aerial Images With Shuffling Convolutional Neural Networks

2018 ◽  
Vol 15 (2) ◽  
pp. 173-177 ◽  
Author(s):  
Kaiqiang Chen ◽  
Kun Fu ◽  
Menglong Yan ◽  
Xin Gao ◽  
Xian Sun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Aerial Images

scholarly journals Correction to: Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks

2020 ◽  
Author(s):  
Sven Koitka ◽  
Lennard Kroll ◽  
Eugen Malamutmann ◽  
Arzu Oezcelik ◽  
Felix Nensa
Keyword(s):  
Neural Networks ◽  
Body Composition ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Ct Imaging ◽  
Original Version ◽  
Composition Analysis ◽  
Body Composition Analysis

The original version of this article, published on 18 September 2020, unfortunately contained a mistake.

Sign in / Sign up

Export Citation Format

Share Document