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The 2007 World Congress in Computer Science,
Computer Engineering, & Applied Computing
Las Vegas, Nevada, USA (June 25-28, 2007)
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WORLDCOMP'07 Tutorial

Last modified 2008-03-31 07:14


Real-Time Image and Video Processing: From Research to Reality
Prof. Nasser Kehtarnavaz, Dept. of Electrical Engineering, University of Texas at Dallas, USA

Date: June 26, 2007
Time: 6:00 - 9:30 PM
Location: TBA

Abstract

    This tutorial provides a much-needed treatment on the implementation aspects of real-time image and video processing systems. It brings together in one place the guidelines, strategies and methodologies for taking an image or video-processing algorithm from a research environment to a working, real-time implementation on a resource constrained hardware platform. Carefully selected, relevant examples from the literature will be presented to illustrate the concepts. The participants will be introduced to a wide variety of strategies and tools, which they can then employ in designing a real-time image or video processing system of interest.


Topics

    The tutorial is based on a new lecture series book, authored by the presenter, entitled Real-Time Image and Video Processing: From Research to Reality. Its content consists of the following five parts:

  • Introductory concepts to real-time image and video processing
  • Algorithmic approaches (algorithm simplification strategies)
  • Hardware platforms (hardware architectures for image and video processing)
  • Software tools (software optimization strategies)
  • Roadmap

Objectives

  • Understand the issues involved in the process of transitioning an algorithm from a research development environment to a real-time constrained hardware platform.
  • Understand the concept of real-time as it pertains to image or video processing systems.
  • Appreciate the diversity of operations in an image/video processing system.
  • Identify and apply proven algorithmic simplification strategies to help streamline an image/video-processing algorithm to meet real-time constraints in an application of interest.
  • Select a hardware platform for the real-time implementation, which best suits the types of image/video processing operations in a given application.
  • Apply proven memory and software optimization strategies to maximize performance of the chosen hardware platform for real-time implementation.

Intended Audience

  • Engineers and scientists interested in taking an image or video processing algorithm from a development environment to an actual real-time implementation on a resource constrained hardware platform
  • Those who wish to pursue a career in image or video processing
  • Designers of image/video processing systems and products

Prerequisites

  • Basic knowledge of image processing
  • Background in electrical engineering or computer science

Biography of Instructor

    Nasser Kehtarnavaz received the PhD degree in Electrical and Computer Engineering from Rice University in 1987. He is currently a Professor of Electrical Engineering at the University of Texas at Dallas (UTD). His research interests include signal and image processing, real-time image processing, pattern recognition, and biomedical image analysis. He has authored or co-authored 5 books and more than 140 journal and conference papers in these areas. He is currently managing a Texas Instruments sponsored research program at UTD involving the development and implementation of real-time image processing algorithms for digital and cell-phone cameras. Dr. Kehtarnavaz is a Fellow of SPIE, Chair of the Dallas Chapter of the IEEE Signal Processing Society and Editor-in-Chief of Journal of Real-Time Image Processing.

Corporate Sponsors


Academic Co-Sponsors

MIT Media Laboratory
(Cambridge, Massachusetts)
The Computational Biology and Functional Genomics Laboratory Harvard University
(Cambridge, Massachusetts)
Statistical Genomics and Computational Biology Laboratory, Department of Statistics, Harvard University
(Cambridge, Massachusetts)
Biomedical Cybernetics Laboratory, Harvard University
(Cambridge, Massachusetts)

Texas Advanced Computing Center, The University of Texas at Austin
(Austin, Texas)
Statistical and Computational Intelligence Laboratory of Purdue University
(West Lafayette, Indiana)

Center for Computational Biology & Bioinformatics
(Indianapolis, Indiana)

University of Iowa's Medical Imaging HPC Lab
(Iowa City, Iowa)
BioMedical Informatics & Bio-Imaging Laboratory
(Georgia Institute of Technology & Emory University, Georgia)
Intelligent Data Exploration and Analysis Laboratory University of Texas at Austin, USA
Columbia University, Statistical Genetics Research Group
Institute for Informatics Problems of the Russian Academy of Sciences
(Moscow, Russia)

University of North Dakota
(Grand Forks, North Dakota)

Other Co-Sponsors


HPCwire

GRIDtoday
STEM Education Society

HPC Software Inc.

International Technology Institute (ITI)
Hodges' Health

 

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