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YXLON Copenhagen
YXLON Copenhagen is a highly specialized, award winning Danish company with 60 years of experience in portable X-ray solutions for industrial NDT.

185 views
00:53 Feb-05-2008
Zheng Liu
Call for Paper

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Special Issue - Integrated Imaging and Vision
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 process 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 fusion, 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 techniques 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(at)bellsouth.net
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