Application of an Improved Grab Cut Method in Tongue Image Segmentation

2018 ◽  
pp. 484-495
Author(s):  
Bin Liu ◽  
Guangqin Hu ◽  
Xinfeng Zhang ◽  
Yiheng Cai
Keyword(s):  
Image Segmentation ◽  
Cut Method

scholarly journals Optimized Graph cut Color Image Segmentation using Genetic Algorithm with Weighted Constraints (OGcut)

2019 ◽  
Vol 8 (4S2) ◽  
pp. 583-587
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Color Image ◽  
Graph Cut ◽  
Input Image ◽  
Color Image Segmentation ◽  
Weighted Constraints ◽  
Mutation Operators ◽  
Cut Method ◽  
Crossover And Mutation

This paper proposes a Novel color image segmentation using Graph cut method by minimizing the weighted energy function. This method is applying a pair of optimal constraints namely: color constraint and gradient constraint. In the state-of-the-art methods, the background and foreground details are manually initialized and used for verifying the smoothness of the region. But in this proposed method, they are dynamically calculated from the input image. This feature of the proposed method can be used in color image segmentation where more number of unique segments exists in a single image. The genetic algorithm is applied to the graph obtained from the graph cut method. The crossover and mutation operators are applied on various subgraphs to populate the different segments.

A New Normalized-Cut Image Segmentation Algorithm Based on Watershed Transform

2012 ◽  
Vol 235 ◽  
pp. 45-48 ◽  
Author(s):  
Hai Tao Liu ◽  
Yin Long Wang ◽  
Hui Fen Yao
Keyword(s):  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Normalized Cut ◽  
Watershed Transform ◽  
Cut Method ◽  
Weight Value ◽  
Area And Perimeter ◽  
The Common ◽  
Main Formula ◽  
Image Segmentation Algorithm

In this paper an improved image segmentation algorithm based on watershed transform is presented. Firstly the normalized cut method and watershed transform are explained and analyzed. Secondly the idea of the improved algorithm and the main formula are explained. We consider the area and perimeter when we merge adjacent regions. We define a new weight value and discuss the value of the parameterαandβ. Finally the experiment result is presented. The new algorithm reduces the nodes and the computational demand of the common normalized cut technique.

An Image Segmentation Algorithm Based on Watershed Transform

2015 ◽  
Vol 740 ◽  
pp. 608-611
Author(s):  
Yin Long Wang ◽  
Qian Jin Li ◽  
Zhi Xiang Li
Keyword(s):  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Normalized Cut ◽  
Watershed Transform ◽  
Cut Method ◽  
Weight Value ◽  
Area And Perimeter ◽  
The Common ◽  
Main Formula ◽  
Image Segmentation Algorithm

An improved image segmentation algorithm based on watershed transform is presented In this paper. Firstly the normalized cut method and watershed transform are explained and analyzed. Secondly the idea of the improved algorithm and the main formula are explained. We consider the area and perimeter when we merge adjacent regions. We define a new weight value and discuss the value of the parameter α and β. Finally the experiment result is presented. The new algorithm reduces the nodes and the computational demand of the common normalized cut technique.

Image Segmentation Based on Superpixels and Saliency

2014 ◽  
Vol 701-702 ◽  
pp. 312-315 ◽  
Author(s):  
Qi Chen ◽  
Xing Ben Yang ◽  
Yun Hong Chen ◽  
Dan Dan Li
Keyword(s):  
Image Segmentation ◽  
Visual Inspection ◽  
Clustering Algorithm ◽  
Segmentation Method ◽  
Practical Applications ◽  
Saliency Model ◽  
Affinity Propagation Clustering ◽  
Cut Method ◽  
Image Separation ◽  
High Level

Image segmentation plays an important role in computer vision and image processing to interpret and analyze an acquired image. Separation of objects or image regions is usually required for high-level image comprehension in practical applications involving visual inspection. In this paper, a novel automatic image segmentation method is proposed. To extract the foreground of the image automatically, we combine saliency model based on superpixels with the affinity propagation clustering algorithm in an unsupervised manner, and use graph cut method to obtain the segmentation results.

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