Motion layer extraction in the presence of occlusion using graph cuts

2005 ◽  
Vol 27 (10) ◽  
pp. 1644-1659 ◽  
Author(s):  
Jiangjian Xiao ◽  
M. Shah
Keyword(s):  
Graph Cuts ◽  
Layer Extraction

scholarly journals Attention Multi-Scale Network for Automatic Layer Extraction of Ice Radar Topological Sequences

2021 ◽  
Vol 13 (12) ◽  
pp. 2425
Author(s):  
Yiheng Cai ◽  
Dan Liu ◽  
Jin Xie ◽  
Jingxian Yang ◽  
Xiangbin Cui ◽  
...  
Keyword(s):  
Deep Learning ◽  
Global Climate ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Sheet Thickness ◽  
Learning Methods ◽  
Convolutional Network ◽  
Multi Scale ◽  
Ice Surface ◽  
Layer Extraction

Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly provide quantitative, objective and reliable extraction of information from radargrams. Most traditional handcrafted methods, designed to detect ice-surface and ice-bed layers from ice sheet radargrams, require complex human involvement and are difficult to apply to large datasets, while deep learning methods can obtain better results in a generalized way. In this study, an end-to-end multi-scale attention network (MsANet) is proposed to realize the estimation and reconstruction of layers in sequences of ice sheet radar tomographic images. First, we use an improved 3D convolutional network, C3D-M, whose first full connection layer is replaced by a convolution unit to better maintain the spatial relativity of ice layer features, as the backbone. Then, an adjustable multi-scale module uses different scale filters to learn scale information to enhance the feature extraction capabilities of the network. Finally, an attention module extended to 3D space removes a redundant bottleneck unit to better fuse and refine ice layer features. Radar sequential images collected by the Center of Remote Sensing of Ice Sheets in 2014 are used as training and testing data. Compared with state-of-the-art deep learning methods, the MsANet shows a 10% reduction (2.14 pixels) on the measurement of average mean absolute column-wise error for detecting the ice-surface and ice-bottom layers, runs faster and uses approximately 12 million fewer parameters.

Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts

NeuroImage ◽  
2008 ◽  
Vol 43 (4) ◽  
pp. 708-720 ◽  
Author(s):  
Fedde van der Lijn ◽  
Tom den Heijer ◽  
Monique M.B. Breteler ◽  
Wiro J. Niessen
Keyword(s):  
Graph Cuts ◽  
Mr Images ◽  
Atlas Registration ◽  
Hippocampus Segmentation ◽  
Voxel Classification

Image Segmentation by Pre-Labeling Based on Part-Based Model

2014 ◽  
Vol 556-562 ◽  
pp. 4206-4210
Author(s):  
Wei Liu ◽  
Xue Jun Xu
Keyword(s):  
Image Segmentation ◽  
Accurate Result ◽  
Graph Cuts ◽  
Interactive Segmentation ◽  
Seed Point ◽  
Bounding Box ◽  
Compact Shape

Interactive segmentation with graph cuts has become very popular and many priors have been introduced into graph cuts to improve the results. This paper proposed a method which uses the deformable part-based model to pre-label the seeds. First the deformable part-based model finds out the bounding box, then we can pre-label the seed point based on the assumption of compact shape. Our results show that our method can get more accurate result especially the appearance of the object and background are similar and the shape is compact.

Object tracking via graph cuts

2009 ◽  
Author(s):  
Alexander M. Nelson ◽  
Jeremiah J. Neubert
Keyword(s):  
Object Tracking ◽  
Graph Cuts

Unsupervised color image segmentation using graph cuts with multi-components

2013 ◽  
Author(s):  
Lei Li ◽  
Lianghai Jin ◽  
Enmin Song ◽  
Zhuoli Dong
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Graph Cuts ◽  
Color Image Segmentation
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