Automatic Image Semantic Segmentation by MRF with Transformation-Invariant Shape Priors

2016 ◽  
pp. 216-226
Author(s):  
Peng Tang ◽  
Weidong Jin
Keyword(s):  
Semantic Segmentation ◽  
Shape Priors ◽  
Invariant Shape

Multiregion Level Set Tracking with Transformation Invariant Shape Priors

2006 ◽  
pp. 674-683 ◽  
Author(s):  
Michael Fussenegger ◽  
Rachid Deriche ◽  
Axel Pinz
Keyword(s):  
Level Set ◽  
Shape Priors ◽  
Invariant Shape

Face Completion Using Semantic Segmentation and Geometric Features

2018 ◽  
Vol 11 (6) ◽  
pp. 304
Author(s):  
Javier Pinzon-Arenas ◽  
Robinson Jimenez-Moreno ◽  
Ruben Hernandez-Beleno
Keyword(s):  
Semantic Segmentation ◽  
Geometric Features

Development of environment design support mixed reality system capable of environment estimation using deep learning

Impact ◽  
2020 ◽  
Vol 2020 (2) ◽  
pp. 9-11
Author(s):  
Tomohiro Fukuda
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Computer Games ◽  
Construction Projects ◽  
Mixed Reality ◽  
Semantic Segmentation ◽  
Environment Design ◽  
Aviation Training ◽  
Architecture And Design ◽  
World Environment

Mixed reality (MR) is rapidly becoming a vital tool, not just in gaming, but also in education, medicine, construction and environmental management. The term refers to systems in which computer-generated content is superimposed over objects in a real-world environment across one or more sensory modalities. Although most of us have heard of the use of MR in computer games, it also has applications in military and aviation training, as well as tourism, healthcare and more. In addition, it has the potential for use in architecture and design, where buildings can be superimposed in existing locations to render 3D generations of plans. However, one major challenge that remains in MR development is the issue of real-time occlusion. This refers to hiding 3D virtual objects behind real articles. Dr Tomohiro Fukuda, who is based at the Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering at Osaka University in Japan, is an expert in this field. Researchers, led by Dr Tomohiro Fukuda, are tackling the issue of occlusion in MR. They are currently developing a MR system that realises real-time occlusion by harnessing deep learning to achieve an outdoor landscape design simulation using a semantic segmentation technique. This methodology can be used to automatically estimate the visual environment prior to and after construction projects.

Utilizing semantic segmentation method with convolutional neural net to model a partial nephrectomy simulator for 3D printing

2019 ◽  
Vol 18 (1) ◽  
pp. e1747-e1748
Author(s):  
Y.S. Kyung ◽  
B. Lim ◽  
S.Y. Choi ◽  
J.H. Han ◽  
W. Lee ◽  
...  
Keyword(s):  
3D Printing ◽  
Partial Nephrectomy ◽  
Semantic Segmentation ◽  
Neural Net ◽  
Segmentation Method
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