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Embedded Computer Vision

  • Book
  • © 2009

Overview

Editors:
  1. Branislav Kisačanin

    1. Texas Instruments, Dallas, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Shuvra S. Bhattacharyya

    1. University of Maryland, College Park, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Sek Chai

    1. Motorola, Schaumburg, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Provides historical perspective, the latest research results and a vision for future developments in this new field of embedded computer vision
  • Contains high-level, state-of-the-art research results
  • Looks ahead, providing a sense of what major applications could be expected in the near future

Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)

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Table of contents (13 chapters)

  1. Front Matter

    Pages I-XXVIII
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Hardware Considerations for Embedded Vision Systems

      • Mathias Kölsch, Steven Butner
      Pages 3-26

    3. Design Methodology for Embedded Computer Vision Systems

      • Sankalita Saha, Shuvra S. Bhattacharyya
      Pages 27-47

    4. We Canwatch It For You Wholesale

      • Alan J. Lipton
      Pages 49-76

  3. Advances in Embedded Computer Vision

    1. Front Matter

      Pages 77-77
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    2. Using Robust Local Features on DSP-Based Embedded Systems

      • Clemens Arth, Christian Leistner, Horst Bischof
      Pages 79-100

    3. Benchmarks of Low-Level Vision Algorithms for DSP, FPGA, and Mobile PC Processors

      • Daniel Baumgartner, Peter Roessler, Wilfried Kubinger, Christian Zinner, Karina Ambrosch
      Pages 101-120

    4. SAD-Based Stereo Matching Using FPGAs

      • Karina Ambrosch, Martin Humenberger, Wilfried Kubinger, Andreas Steininger
      Pages 121-138

    5. Motion History Histograms for Human Action Recognition

      • Hongying Meng, Nick Pears, Michael Freeman, Chris Bailey
      Pages 139-162

    6. Embedded Real-Time Surveillance Using Multimodal Mean Background Modeling

      • Senyo Apewokin, Brian Valentine, Dana Forsthoefel, Linda Wills, Scott Wills, Antonio Gentile
      Pages 163-175

    7. Implementation Considerations for Automotive Vision Systems on a Fixed-Point DSP

      • Zoran Nikolić
      Pages 177-194

    8. Towards OpenVL: Improving Real-Time Performance of Computer Vision Applications

      • Changsong Shen, James J. Little, Sidney Fels
      Pages 195-216

  4. Looking Ahead

    1. Front Matter

      Pages 217-217
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    2. Mobile Challenges for Embedded Computer Vision

      • Sek Chai
      Pages 219-235

    3. Challenges in Video Analytics

      • Nikhil Gagvani
      Pages 237-256

    4. Challenges of Embedded Computer Vision in Automotive Safety Systems

      • Yan Zhang, Arnab S. Dhua, Stephen J. Kiselewich, William A. Bauson
      Pages 257-279

  5. Back Matter

    Pages 281-282
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Keywords

About this book

As a graduate student at Ohio State in the mid-1970s, I inherited a unique c- puter vision laboratory from the doctoral research of previous students. They had designed and built an early frame-grabber to deliver digitized color video from a (very large) electronic video camera on a tripod to a mini-computer (sic) with a (huge!) disk drive—about the size of four washing machines. They had also - signed a binary image array processor and programming language, complete with a user’s guide, to facilitate designing software for this one-of-a-kindprocessor. The overall system enabled programmable real-time image processing at video rate for many operations. I had the whole lab to myself. I designed software that detected an object in the eldofview,trackeditsmovementsinrealtime,anddisplayedarunningdescription of the events in English. For example: “An object has appeared in the upper right corner...Itismovingdownandtotheleft...Nowtheobjectisgettingcloser...The object moved out of sight to the left”—about like that. The algorithms were simple, relying on a suf cient image intensity difference to separate the object from the background (a plain wall). From computer vision papers I had read, I knew that vision in general imaging conditions is much more sophisticated. But it worked, it was great fun, and I was hooked.

Reviews

From the reviews:

“The book is a result of the Embedded Computer Vision Workshop 2007. … provides a very good overview of the current state of the art in embedded computer vision and of the major trends and growing markets. … it is a good start and provides an extensive list of references to look for if one wants to go into more detail. Overall I would recommend this book to anyone interested in getting into this exciting field.” (Marcus E. Hennecke, IAPR Newsletter, Vol. 33 (2), April, 2011)

Editors and Affiliations

  • Texas Instruments, Dallas, USA

    Branislav Kisačanin

  • University of Maryland, College Park, USA

    Shuvra S. Bhattacharyya

  • Motorola, Schaumburg, USA

    Sek Chai

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