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

被引：20
|
作者：
Maliatski, B [1 ]
Yadid-Pecht, O [1 ]
机构：
[1] Ben Gurion Univ Negev, VLSI Syst Ctr, IL-84105 Beer Sheva, Israel
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY | 2005年 / 15卷 / 01期
关键词：
clustering; image processing; VLSI;
D O I：
10.1109/TCSVT.2004.839977
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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 Vs.
引用
收藏
页码：164 / 166
页数：3
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