scholarly journals Implementation of Watershed Based Image Segmentation Algorithm using Distance Transform for Grid Computing

2013 ◽  
Vol 10 (6) ◽  
pp. 1715-1719
Author(s):  
Megha Sharma ◽  
Seema Verma
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Grid Computing ◽  
Segmentation Algorithm ◽  
New Method ◽  
Distance Transform ◽  
Image Information ◽  
Watershed Transform ◽  
Image Segmentation Algorithm

A new method for image segmentation using watershed transform algorithm is presented in this paper. It takes advantage of the fact that the proposed algorithm produced good results even if the same parameters are used for the standard segmentation algorithm. The proposed segmentation algorithm will be very effective for grid computing as it seems to possess specific tasks of image information and detection in order to obtain a detailed and accurate image analysis.

Improved Watershed Algorithm for Cell Image Segmentation

2012 ◽  
Vol 546-547 ◽  
pp. 464-468
Author(s):  
Yu Zhang ◽  
Duan Quan Xu
Keyword(s):  
Image Segmentation ◽  
Mathematical Morphology ◽  
Segmentation Algorithm ◽  
Watershed Algorithm ◽  
Distance Transform ◽  
Cell Image ◽  
Image Segmentation Algorithm

Watershed is an image segmentation algorithm based on mathematical morphology, which can determine the boundary of connected section efficiently and effectively. But the traditional watershed algorithm is sensitive to noise. To overcome the weakness of classical watershed, this paper presents an improved watershed algorithm based on gradient transform, open-close reconstruction and distance transform. The experiment result shows that application of this improved watershed algorithm in cell image segmentation has a good performance.

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.

scholarly journals A smoke image segmentation algorithm based on rough set and region growing

2019 ◽  
Vol 65 (No. 8) ◽  
pp. 321-329
Author(s):  
Haitao Wang ◽  
Yanli Chen
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Region Growing ◽  
Recall Rate ◽  
Segmentation Algorithm ◽  
Colour Space ◽  
Seed Point ◽  
Black Smoke ◽  
Growth Method ◽  
Image Segmentation Algorithm

Because the image fire smoke segmentation algorithm can not extract white, gray and black smoke at the same time, a smoke image segmentation algorithm is proposed by combining rough set and region growth method. The R component of the image is extracted in the RGB colour space, the roughness histogram is constructed according to the statistical histogram of the R component, and the appropriate valley value in the roughness histogram is selected as the segmentation threshold, the image is roughly segmented. Relative to the background image, the smoke belongs to the motion information, and the motion region is extracted by the interframe difference method to eliminate static interference. Smoke has a unique colour feature, a smoke colour model is created in the RGB colour space, the motion disturbances of similar colour are removed and the suspected smoke areas are obtained. The seed point is selected in the region, and the region is grown on the result of rough segmentation, the smoke region is extracted. The experimental results show that the algorithm can segment white, gray and black smoke at the same time, and the irregular information of smoke edges is relatively complete. Compared with the existing algorithms, the average segmentation accuracy, recall rate and F-value are increased by 19%, 21.5% and 20%, respectively.<br /><br />

Sign in / Sign up

Export Citation Format

Share Document