Modeling of Large Droplets Impingement Using a Hybrid Taylor-Galerkin Variational Multi-Scale Stabilized Level Set Method

2016 ◽  
Author(s):  
Ahmed Bakkar ◽  
Wagdi G. Habashi ◽  
Marco Fossati
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Multi Scale

An efficient level set method based on multi-scale image segmentation and hermite differential operator

2016 ◽  
Vol 188 ◽  
pp. 90-101 ◽  
Author(s):  
Xiao-Feng Wang ◽  
Hai Min ◽  
Le Zou ◽  
Yi-Gang Zhang ◽  
Yuan-Yan Tang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Differential Operator ◽  
Level Set Method ◽  
Level Set ◽  
Multi Scale ◽  
Efficient Level

A novel multi-scale level set method for SAR image segmentation based on a statistical model

2012 ◽  
Vol 33 (17) ◽  
pp. 5600-5614 ◽  
Author(s):  
Haigang Sui ◽  
Chuan Xu ◽  
Junyi Liu ◽  
Kaimin Sun ◽  
Chengfeng Wen
Keyword(s):  
Image Segmentation ◽  
Statistical Model ◽  
Level Set Method ◽  
Level Set ◽  
Sar Image ◽  
Scale Level ◽  
Multi Scale ◽  
Sar Image Segmentation

scholarly journals An Improved Level Set Method on the Multiscale Edges

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1650
Author(s):  
Yao Su ◽  
Kun He ◽  
Dan Wang ◽  
Tong Peng
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Image Features ◽  
Superior Performance ◽  
Total Variation Regularization ◽  
Multiscale Decomposition ◽  
Multi Scale ◽  
Segmentation Methods ◽  
Edge Based ◽  
Feature Scale

The level set method can segment symmetrical or asymmetrical objects in real images according to image features. However, the segmentation performance varies with feature scale. In order to improve the segmentation effect, we propose an improved level set method on the multiscale edges, which combines the level set method with image multi-scale decomposition to form a unified model. In this model, the segmentation relies on multiscale edges, and the multiscale edges depend on multiscale decomposition. A novel total variation regularization is proposed in multiscale decomposition to preserve edges. The multiscale edges obtained by the multiscale decomposition are integrated into the segmentation process, and the object can be easily extracted from a proper scale. Experimental results indicate that this method has superior performance in precision, recall and F-measure, compared with relative edge-based segmentation methods, and is insensitive to noise and inhomogeneous sub-regions.

A multi-scale level set method based on local features for segmentation of images with intensity inhomogeneity

2019 ◽  
Vol 91 ◽  
pp. 69-85 ◽  
Author(s):  
Hai Min ◽  
Li Xia ◽  
Junwei Han ◽  
Xiaofeng Wang ◽  
Qianqian Pan ◽  
...  
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Local Features ◽  
Intensity Inhomogeneity ◽  
Scale Level ◽  
Multi Scale ◽  
Segmentation Of Images
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