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

Boris Maliatski; Orly Yadid-Pecht

doi:10.1109/TCSVT.2004.839977

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

Boris Maliatski, Orly Yadid-Pecht

Department of Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

25 Scopus citations

Abstract

A new adaptive κ-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.

Original language	English
Pages (from-to)	164-166
Number of pages	3
Journal	IEEE Transactions on Circuits and Systems for Video Technology
Volume	15
Issue number	1
DOIs	https://doi.org/10.1109/TCSVT.2004.839977
State	Published - 1 Jan 2005

Keywords

Clustering
Image processing
VLSI

ASJC Scopus subject areas

Media Technology
Electrical and Electronic Engineering

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10.1109/TCSVT.2004.839977

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

Abstract

Keywords

ASJC Scopus subject areas

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