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

Boris Maliatski, Orly Yadid-Pecht

Research output: Contribution to journalArticlepeer-review

21 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 languageEnglish
Pages (from-to)164-166
Number of pages3
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume15
Issue number1
DOIs
StatePublished - 1 Jan 2005

Keywords

  • Clustering
  • Image processing
  • VLSI

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