Identity Issues that Orphans Face Part 1

2019 ◽  
Vol 04 (02) ◽  
pp. 10-16
Author(s):  
M Roy Bright ◽  
Keyword(s):  
Face Part

Identity Issues that Orphans Face Part 2

2019 ◽  
Vol 04 (03) ◽  
pp. 13-17
Author(s):  
M Roy Bright ◽  
Keyword(s):  
Face Part

Historical evolution of surgical approaches to the face—part II: midface

2021 ◽  
Author(s):  
Jose S. Sifuentes-Cervantes ◽  
Francisco Carrillo-Morales ◽  
Jaime Castro-Núñez ◽  
Bhargav Venkata Chivukula ◽  
Larry L. Cunningham ◽  
...  
Keyword(s):  
Surgical Approaches ◽  
Historical Evolution ◽  
The Face ◽  
Face Part

scholarly journals Inferior occipital gyrus is organised along common gradients of spatial and face-part selectivity

2021 ◽  
pp. JN-RM-2415-20
Author(s):  
Benjamin de Haas ◽  
Martin I. Sereno ◽  
D. Samuel Schwarzkopf
Keyword(s):  
Face Part

scholarly journals Semantically Guided Large Deformation Estimation with Deep Networks

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1392
Author(s):  
In Young Ha ◽  
Matthias Wilms ◽  
Mattias Heinrich
Keyword(s):  
Medical Image ◽  
Motion Tracking ◽  
Large Deformations ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Mri Sequences ◽  
Magnetic Resonance Imaging Mri ◽  
Weakly Supervised ◽  
Low Dimensional ◽  
Face Part

Deformable image registration is still a challenge when the considered images have strong variations in appearance and large initial misalignment. A huge performance gap currently remains for fast-moving regions in videos or strong deformations of natural objects. We present a new semantically guided and two-step deep deformation network that is particularly well suited for the estimation of large deformations. We combine a U-Net architecture that is weakly supervised with segmentation information to extract semantically meaningful features with multiple stages of nonrigid spatial transformer networks parameterized with low-dimensional B-spline deformations. Combining alignment loss and semantic loss functions together with a regularization penalty to obtain smooth and plausible deformations, we achieve superior results in terms of alignment quality compared to previous approaches that have only considered a label-driven alignment loss. Our network model advances the state of the art for inter-subject face part alignment and motion tracking in medical cardiac magnetic resonance imaging (MRI) sequences in comparison to the FlowNet and Label-Reg, two recent deep-learning registration frameworks. The models are compact, very fast in inference, and demonstrate clear potential for a variety of challenging tracking and/or alignment tasks in computer vision and medical image analysis.

scholarly journals Spatial organization of face part representations within face-selective areas revealed by 7T fMRI

2019 ◽  
Vol 19 (10) ◽  
pp. 260b
Author(s):  
Jiedong Zhang ◽  
Peng Zhang ◽  
Sheng He
Keyword(s):  
Spatial Organization ◽  
7T Fmri ◽  
Face Part
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