Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Image segmentation using temporal-spatial information in dynamic scenes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Wen-Qing Huang ; Coll. of Biosystem Eng. & Food Sci., Zhejiang Univ., Hangzhou, China ; Ya-Ming Wang ; Yun Zhao

The goal of image segmentation is to identify homogeneous regions in images. A common method for segmentation of moving regions in image sequences involves "background subtraction", namely, thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. One possible approach to this problem is to model the attributes of the pixels. In order to segment a larger image in reasonable time and obtain better results, an algorithm of segmenting background based on temporal spatial information of pixels is proposed in this paper. Firstly, adaptive pixel models are modeled to describe the recent history of color at each observed pixel. Then each pixel is classified as background or foreground according to pixel models and the spatial relation between pixels. Finally, parameters of pixel models are updated using on-line EM algorithm. Experimental results show that our approach is suitable for segmenting foreground from background in dynamic environments.

Published in:

Machine Learning and Cybernetics, 2003 International Conference on  (Volume:5 )

Date of Conference:

2-5 Nov. 2003