MVA Special Issue
Techniques for Industrial Inspection

Imaging- and vision-based techniques play an important role in
industrial inspection. The sophistication of the techniques assures
high-quality performance of the manufacturing pro- cess through precise
positioning, online monitoring, and real-time classification. Advanced
systems incorporating multiple imaging and/or vision modalities provide
robust solutions to complex situations and problems in industrial
applications. A diverse range of industries, in- cluding aerospace,
automotive, electronics, pharmaceutical, biomedical, semiconductor, and
food/beverage, etc., have benefited from recent advances in multi-modal
imaging, data fu- sion, and computer vision technologies. The purpose of
this special issue is to highlight such advances and demonstrate the
successful applications of multi-modal imaging and vision technologies
in industrial inspection.

Papers that advance the theories of multi-modal imaging, data fusion,
and vision tech- niques or tackle challenges in practical applications
are invited. In addition to conventional vision technologies, imaging
modalities of interest include X-ray , Terahertz imaging, and ultrasonic
testing. The contributions should be original and must not have been
presented and/or published (or currently under consideration) in any
other form.

Topics include (but are not limited to) the following:
* Automated defect identification and classification with multi-modal imaging techniques;
* Multi-sensor image fusion for inspection;
* Multi-modal vision system design and implementation;
* Precise measurements with 3D vision and multi-modal geometry reconstruction;
* Registration of multi-modal inspection data;
* Multi-camera system and array for inspection;
* Multi-spectrum imaging and analysis;
* Visualization of multi-modal nondestructive inspection data;
* 3D volumetric image processing;
* Other applications

Machine Vision and Applications accepts high-quality technical
contributions which are within its aims and scope in both long and short
paper formats. Long papers may not be over 30 manuscript pages in length
(12 point type, double-spaced, 5 cm margins (2 inch) on one side of the
paper only) including figures, references, acknowledgements, footnotes,
tables, and captions. All papers should be written in English. Further
guidelines can be viewed at http://www.springerlink.com/content/100522/.
Deadline for submission: August 31, 2008

GUEST EDITORS
Dr. Zheng Liu, Institute for Research in Construction,
National Research Council Canada
Dr. Hiroyuki Ukida, Department of Mechanical Engineering
Tokushima University
Dr. Pradeep Ramuhalli, Department of Electrical and Computer Engineering
Michigan State University
Dr. David S. Forsyth, NDE Division
Texas Research International Inc./Austin

Submitting Your Manuscript

Machine Vision and Applications employs a completely automated
submission and review process. To submit a manuscript, please visit
http://mc.manuscriptcentral.com/mva . If you are new to Manuscript
Central, please use the Create Account link in the top right corner of
the page to create a new account. Once you have created an account you
will have access to your Author Dashboard. More information can be found
regarding use of Manuscript Central in the Help section of the website.

Machine Vision and Applications publishes high-quality technical
contributions in machine vision research and development. Specifically,
the editors encourage submittals in all applications and engineering
aspects of image-related computing. In particular, original
contributions dealing with scientific, commercial, industrial, military,
and biomedical applications of machine vision, are all within the scope
of the journal. Particular emphasis is placed on engineering and
technology aspects of image processing and computer vision.

For further information regarding Machine Vision and Applications,
please contact: Sheli Carr, Editorial Coordinator mva_ec@bellsouth.net