On the boundary map K3(Λ/I) → K2(Λ,I)

1981 ◽  
pp. 262-268 ◽  
Author(s):  
Jean-Louis Loday
Keyword(s):  
Boundary Map

THE BAUM–CONNES ASSEMBLY MAP AS A BOUNDARY MAP

2006 ◽  
Vol 38 (04) ◽  
pp. 575-586
Author(s):  
PAUL D. MITCHENER
Keyword(s):  
Boundary Map ◽  
Assembly Map

Reversible data hiding with context modeling, generalized expansion and boundary map

2010 ◽  
Vol 57 (3) ◽  
pp. 477-499 ◽  
Author(s):  
Wei Fan ◽  
Zhenyong Chen ◽  
Ming Chen ◽  
Lixin Luo ◽  
Zhang Xiong
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Context Modeling ◽  
Boundary Map

scholarly journals Development of CAD/CAM System Based on Boundary-Map Data Structure.

2000 ◽  
Vol 66 (7) ◽  
pp. 1048-1052 ◽  
Author(s):  
Mikio FUJIO ◽  
Hukuzo YAGISHITA ◽  
Hiroshi SUZUKI
Keyword(s):  
Data Structure ◽  
Boundary Map ◽  
Cad Cam ◽  
Map Data

scholarly journals An Adversarial and Densely Dilated Network for Connectomes Segmentation

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 467 ◽  
Author(s):  
Ke Chen ◽  
Dandan Zhu ◽  
Jianwei Lu ◽  
Ye Luo
Keyword(s):  
State Of The Art ◽  
Piriform Cortex ◽  
Receptive Fields ◽  
Ground Truth ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Boundary Map ◽  
Segmentation Task ◽  
The Brain

Automatic reconstructing of neural circuits in the brain is one of the most crucial studies in neuroscience. Connectomes segmentation plays an important role in reconstruction from electron microscopy (EM) images; however, it is rather challenging due to highly anisotropic shapes with inferior quality and various thickness. In our paper, we propose a novel connectomes segmentation framework called adversarial and densely dilated network (ADDN) to address these issues. ADDN is based on the conditional Generative Adversarial Network (cGAN) structure which is the latest advance in machine learning with power to generate images similar to the ground truth especially when the training data is limited. Specifically, we design densely dilated network (DDN) as the segmentor to allow a deeper architecture and larger receptive fields for more accurate segmentation. Discriminator is trained to distinguish generated segmentation from manual segmentation. During training, such adversarial loss function is optimized together with dice loss. Extensive experimental results demonstrate that our ADDN is effective for such connectomes segmentation task, helping to retrieve more accurate segmentation and attenuate the blurry effects of generated boundary map. Our method obtains state-of-the-art performance while requiring less computation on ISBI 2012 EM dataset and mouse piriform cortex dataset.

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