By Topic

Hardware-driven adaptive k-means clustering for real-time video imaging

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

2 Author(s)
Maliatski, B. ; VLSI Syst. Center, Ben-Gurion Univ., Beer-Sheva, Israel ; Yadid-Pecht, O.

A new adaptive k-means clustering algorithm for real-time video imaging is presented. In the proposed solution, a weighted contribution of both pixel intensity and distance between the pixels is used for cluster identification. The weight adaptation of each parameter reduces the computation complexity and makes it possible to implement the algorithm in hardware. The algorithm is designed for real-time video imaging in a VLSI implementation. It was implemented with 15 kgates and maximum clock rate of 80 MHz. Simulation results prove that a QCIF image could be handled in 15 f/s.

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:15 ,  Issue: 1 )