scholarly journals Simultaneously recovering both domain and varying density in inverse gravimetry by efficient level-set methods

2020 ◽  
Vol 0 (0) ◽  
pp. 1-27
Author(s):  
Wenbin Li ◽  
◽  
Jianliang Qian ◽  
2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Mohammed M. Abdelsamea ◽  
Giorgio Gnecco ◽  
Mohamed Medhat Gaber ◽  
Eyad Elyan

Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses.


Author(s):  
Angelo Alessandri ◽  
Patrizia Bagnerini ◽  
Mauro Gaggero ◽  
Alberto Traverso

2018 ◽  
Vol 174 (1-2) ◽  
pp. 359-390 ◽  
Author(s):  
Aleksandr Y. Aravkin ◽  
James V. Burke ◽  
Dmitry Drusvyatskiy ◽  
Michael P. Friedlander ◽  
Scott Roy

2018 ◽  
Vol 54 (3) ◽  
pp. 1-4 ◽  
Author(s):  
Kyung Sik Seo ◽  
Kang Hyouk Lee ◽  
Il Han Park

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