Vehicle monitoring video image segmentation based on improving Watershed Algorithm

2012 ◽  
Author(s):  
Xinkuan Wei ◽  
Shanlin Zhang ◽  
Yongjun Shen ◽  
Yansheng Xing
Keyword(s):  
Image Segmentation ◽  
Video Image ◽  
Watershed Algorithm ◽  
Vehicle Monitoring

Image of Monolithic Circuit Segmentation Based on Watershed Algorithm

2013 ◽  
Vol 303-306 ◽  
pp. 1109-1113
Author(s):  
Zhu Lin Wang ◽  
Bin Fang ◽  
Xi Wei Guo
Keyword(s):  
Image Segmentation ◽  
Processing Method ◽  
Morphological Processing ◽  
Watershed Algorithm ◽  
Single Object ◽  
Watershed Transform ◽  
Key Technology ◽  
Processing Function ◽  
Important Position

Abstract. Image segmentation is a key technology in image engineering, it occupy an important position. This paper introduces the watershed transform to Image of monolithic circuit processing method, and then introduced the watershed transform to Image of monolithic circuit segmentation and sample. The results show that, by using the watershed algorithm and morphological processing function, which is connected with a plurality of object images are segmented into a plurality of single object, to achieve the image segmentation, and as far as possible to reduce or eliminate the phenomenon of over-segmentation. Finally it points out the further direction of research.

Improved watershed algorithm for color image segmentation

2009 ◽  
Author(s):  
Hong-bo Tan ◽  
Zhi-qiang Hou ◽  
Xiao-chun Li ◽  
Rong Liu ◽  
Wei-wu Guo
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Watershed Algorithm

Video Image Segmentation Using Gaussian Mixture Models Based on the Differential Evolution-Based Parameter Estimation

2011 ◽  
Vol 474-476 ◽  
pp. 442-447
Author(s):  
Zhi Gao Zeng ◽  
Li Xin Ding ◽  
Sheng Qiu Yi ◽  
San You Zeng ◽  
Zi Hua Qiu
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
Mixture Models ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Expectation Maximization Algorithm ◽  
Image Data ◽  
Gaussian Mixture ◽  
Parameters Estimation ◽  
Video Image

In order to improve the accuracy of the image segmentation in video surveillance sequences and to overcome the limits of the traditional clustering algorithms that can not accurately model the image data sets which Contains noise data, the paper presents an automatic and accurate video image segmentation algorithm, according to the spatial properties, which uses the Gaussian mixture models to segment the image. But the expectation-maximization algorithm is very sensitive to initial values, and easy to fall into local optimums, so the paper presents a differential evolution-based parameters estimation for Gaussian mixture models. The experiment result shows that the segmentation accuracy has been improved greatly than by the traditional segmentation algorithms.

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