scholarly journals Hierarchical voting experts: An unsupervised algorithm for hierarchical sequence segmentation

2008 ◽  
Author(s):  
Matthew Miller ◽  
Alexander Stoytchev
Keyword(s):  
Sequence Segmentation

Study on Strategy of CT Image Sequence Segmentation for Liver and Tumor Based on U-Net and Bi-ConvLSTM

2021 ◽  
pp. 115008
Author(s):  
Jing Li ◽  
Xue Ou ◽  
Nanyan Shen ◽  
Jie Sun ◽  
Junli Ding ◽  
...  
Keyword(s):  
Image Sequence ◽  
Ct Image ◽  
Sequence Segmentation

scholarly journals Robust Evolution Method of Active Contour Models and Application in Segmentation of Image Sequence

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Guoqi Liu ◽  
Haifeng Li
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Image Sequence ◽  
Nuclear Norm ◽  
Shape Prior ◽  
Object Boundary ◽  
Active Contour Models ◽  
Ideal Object ◽  
Sequence Segmentation ◽  
Contour Models

Active contour models are widely used in image segmentation. In order to obtain ideal object boundary, researchers utilize various information to define new models for image segmentation. However, the models could not meet all scenes of image. In this paper, we propose a block evolution method to improve the robustness of contour evolution. A block matrix is consisted of contours of former iterations and contours of shape prior, and a nuclear norm of the matrix is a measure of the similarity of these shapes. The constraint of the nuclear norm minimization is imposed on the evolution of active contour models, which could avoid large deformation of the adjacent curves and keep the shape conformability of contour in the evolution. The shape prior can be integrated into the block evolution method, which is effective in dealing with missing features of images and noise. The proposed method can be applied to image sequence segmentation. Experiments demonstrate that the proposed method improves the robust performance of active contour models and can increase the flexibility of applications in image sequence segmentation.

GPU parallelization of sequence segmentation using information theoretic models

2018 ◽  
Vol 86 ◽  
pp. 11-24 ◽  
Author(s):  
Luay Alawneh ◽  
Emad Rawashdeh ◽  
Mahmoud Al-Ayyoub ◽  
Yaser Jararweh
Keyword(s):  
Information Theoretic ◽  
Sequence Segmentation
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