Fast algorithm based on superpixel‐level conditional triplet Markov field for successive‐approximation resistor image segmentation

2015 ◽  
Vol 9 (8) ◽  
pp. 1097-1105 ◽  
Author(s):  
Yan Wu ◽  
Fan Wang ◽  
Qingjun Zhang ◽  
Fanglong Niu ◽  
Ming Li
2018 ◽  
Vol 7 (4.33) ◽  
pp. 41
Author(s):  
Abdul K Jumaat ◽  
Ke Chen

Selective image segmentation model aims to separate a specific object from its surroundings. To solve the model, the common practice to deal with its non-differentiable term is to approximate the original functional. While this approach yields to successful segmentation result, however the segmentation process can be slow. In this paper, we showed how to solve the model without approximation using Chambolle’s projection algorithm. Numerical tests show that good visual quality of segmentation is obtained in a fast-computational time.  


Author(s):  
Hanane DALIMI ◽  
Mohamed AFIFI ◽  
Said AMAR

In this article we propose to place our work in a Markovian framework for unsupervised image segmentation. We give one of the procedures for estimating the parameters of a Markov field, we limit the work to the EM estimation method and the Posterior Marginal Maximization (MPM) segmentation method. Estimating the number of regions who compones the image is relatively difficult, we try to solve this problem by the K-means Histogram method.


Author(s):  
Le Zou ◽  
Liang-Tu Song ◽  
Xiao-Feng Wang ◽  
Yan-Ping Chen ◽  
Qiong Zhou ◽  
...  

2011 ◽  
Vol 37 (4) ◽  
pp. 521-542 ◽  
Author(s):  
Fangfang Dong ◽  
Chunxiao Liu ◽  
De-Xing Kong

2015 ◽  
Author(s):  
Jiajing Wang ◽  
Shuhong Jiao ◽  
Zhenyu Sun

2001 ◽  
Author(s):  
Jianzhong Su ◽  
Jinwen Tian ◽  
Jianguo Liu ◽  
Zailong Sun

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