By Topic

Parallel Clustering Algorithms for Image Processing on Multi-core CPUs

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
$33 $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

4 Author(s)
Honggang Wang ; Coll. of Phys. & Electron. Eng., Ludong Univ., Yantai ; Jide Zhao ; Hongguang Li ; Jianguo Wang

Scaling the number of cores on processor chips has become the trend for current semiconduction industry (i.e. Intel/AMD many-core CPU, Nvida GPU etc). Current software development should take advantage of those multi-core platforms to achieve high performance. But it is a challenging task to develop parallel software on multiple processor because of the well known problems such as deadlock, load balancing, cache conflicts etc. In this paper, we demonstrate the underlying principles for parallel software development for image processing on multicore CPUs. We study and parallelize two popular clustering algorithms: i) k-means and ii) mean-shift. The experimental results show that good parallel implementations of those algorithms is able to achieve nearly linear speedups on multicore processors.

Published in:

Computer Science and Software Engineering, 2008 International Conference on  (Volume:3 )

Date of Conference:

12-14 Dec. 2008