scholarly journals Multiple-Model Fully Convolutional Neural Networks for Single Object Tracking on Thermal Infrared Video

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 42790-42799 ◽  
Author(s):  
Mohd Asyraf Zulkifley ◽  
Niki Trigoni
Keyword(s):  
Neural Networks ◽  
Object Tracking ◽  
Convolutional Neural Networks ◽  
Thermal Infrared ◽  
Multiple Model ◽  
Single Object ◽  
Fully Convolutional Neural Networks ◽  
Infrared Video

Deep convolutional neural networks for thermal infrared object tracking

2017 ◽  
Vol 134 ◽  
pp. 189-198 ◽  
Author(s):  
Qiao Liu ◽  
Xiaohuan Lu ◽  
Zhenyu He ◽  
Chunkai Zhang ◽  
Wen-Sheng Chen
Keyword(s):  
Neural Networks ◽  
Object Tracking ◽  
Convolutional Neural Networks ◽  
Thermal Infrared ◽  
Deep Convolutional Neural Networks

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.

scholarly journals Fully convolutional neural networks for polyp segmentation in colonoscopy

2017 ◽  
Author(s):  
Patrick Brandao ◽  
Evangelos Mazomenos ◽  
Gastone Ciuti ◽  
Renato Caliò ◽  
Federico Bianchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fully Convolutional Neural Networks
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