Open Access Paper
8 March 2014 Front Matter: Volume 9024
Proceedings Volume 9024, Image Processing: Machine Vision Applications VII; 902401 (2014) https://doi.org/10.1117/12.2063420
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States
Abstract
This PDF file contains the front matter associated with SPIE Proceedings Volume 9024 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

The papers included in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee. Some conference presentations may not be available for publication. The papers published in these proceedings reflect the work and thoughts of the authors and are published herein as submitted. The publishers are not responsible for the validity of the information or for any outcomes resulting from reliance thereon.

Please use the following format to cite material from this book:

Author(s), “Title of Paper,” in Image Processing: Machine Vision Applications VII, edited by Kurt S. Niel, Philip R. Bingham, Proceedings of SPIE-IS&T Electronic Imaging, SPIE Vol. 9024, Article CID Number (2014)

ISSN: 0277-786X

ISBN: 9780819499417

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Conference Committee

Symposium Chair

  • Sergio R. Goma, Qualcomm Inc. (United States)

Symposium Cochair

  • Sheila S. Hemami, Northeastern University (United States)

Conference Chairs

  • Kurt S. Niel, Upper Austria University of Applied Sciences (Austria)

  • Philip R. Bingham, Oak Ridge National Laboratory (United States)

Conference Program Committee

  • Ewald Fauster, vatron GmbH (Austria)

  • Daniel Fecker, Technische Universität Braunschweig (Germany)

  • Steven P. Floeder, 3M Company (United States)

  • David Fofi, Université de Bourgogne (France)

  • Shaun Gleason, National Oceanography Center (United Kingdom)

  • Keith Jenkins, The University of Southern California (United States)

  • Olivier Laligant, Université de Bourgogne (France)

  • Edmund Y. Lam, The University of Hong Kong (Hong Kong, China)

  • Fabrice Meriaudeau, Université de Bourgogne (France)

  • Hamed Sari-Sarraf, Texas Tech University (United States)

  • Ivan W. Selesnick, Polytechnic Institute of New York University (United States)

  • Ralph Seulin, Université de Bourgogne (France)

  • Christophe Stolz, Université de Bourgogne (France)

  • Vincent C. Paquit, Oak Ridge National Laboratory (United States)

  • Gerald Zauner, FH OÖ Forschungs & Entwicklungs GmbH (Austria)

Session Chairs

  • 1 Industrial Machine Vision Applications

    Philip R. Bingham, Oak Ridge National Laboratory (United States)

  • 2 Computational Vision and Imaging Techniques I

    Gerald Zauner, FH OÖ Forschungs & Entwicklungs GmbH (Austria)

  • 3 Computational Vision and Imaging Techniques II

    Kurt S. Niel, Upper Austria University of Applied Sciences (Austria)

  • 4 Thermal, Color, and/or Spectroscopic Imaging Algorithms and Applications

    Gerald Zauner, FH OÖ Forschungs & Entwicklungs GmbH (Austria)

  • 5 Image-Related Pattern Recognition Techniques and Applications

    Kurt S. Niel, Upper Austria University of Applied Sciences (Austria)

Introduction

In our everyday machine vision work, we are facing two big and very complex challenges. On one hand, we are doing scientific research in exploring the fundamental behavior of imaging systems and methods. On the other hand, we cooperate strongly with industrial needs for reliable quality under constraints of a reasonable budget. There are clear gaps in the goals of these two challenges and even contradictions in their demands.

Our conference, Machine Vision Applications, serves as a forum to discuss efforts that span these demands by bringing scientific research and industrial needs together. On reading the contributions to this conference, one will get a feel for how the different branches of machine vision serve to support industrial needs. You will find papers describing quality control issues at manufacturing processes, agricultural evaluations, face detection for security purposes, 3D topographical modelling by airborne images, hyperspectral detection for biological surveys, and many others. There are also papers concerning a bit more abstract level of machine vision indicating improvements in algorithms—speed, reliability, and memory usage—by keeping the quality of the overall evaluation task.

In this context, our community offers the “Best Paper Award” for contributions that combine three essential aspects: scientific background, industrial reliability, and clarity in writing, presentation, and discussion. The paper, “Depth and all-in-focus images obtained by multi-line-scan light-field approach,” (9024-07), has been selected for this award. Congratulations!

Kurt S. Niel

Philip R. Bingham

© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
"Front Matter: Volume 9024", Proc. SPIE 9024, Image Processing: Machine Vision Applications VII, 902401 (8 March 2014); https://doi.org/10.1117/12.2063420
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KEYWORDS
Machine vision

Applied sciences

Computer vision technology

Current controlled current source

Detection and tracking algorithms

Image processing

Image quality

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