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INS 2014 : Special Issue on View-Based 3D Representation, Learning, and Understanding

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Link: http://www.journals.elsevier.com/information-sciences/call-for-papers/view-based-3d-representation-learning-and-understanding/
 
When N/A
Where N/A
Submission Deadline Jun 15, 2014
Notification Due Nov 15, 2014
 

Call For Papers

INFORMATION SCIENCES

Editor in Chief
Witold Pedrycz
University of Alberta

Guest Editors
Yue Gao
Mingli Song
Bulent Sankur


Important dates
Submission deadline: June 15, 2014
Acceptance deadline: November 15, 2014
Publication: Spring, 2015

The advances of computing techniques, graphics hardware, and networks have witnessed the wide applications of 3D data in various domains, such as 3D graphics, entertainment, medical industry and 3D model design. The proliferation of such applications lead to large scale 3D data, while effective 3D processing tools to manipulate these data are still at their infancy. The widespread use of digital still and video cameras as well as mobile devices with cameras has changed the visual information acquisition style. Under these circumstances, capturing a set of images or a short video of real objects becomes feasible. This is a new and emerging topic cross several research areas, such as computer vision, multimedia computing, pattern recognition, image processing and computing graphics. This situation encourages the view-based 3D data analysis, and the mature technologies in image processing further prompt this research. In recent years, extensive research efforts have been dedicated to view-based 3D techniques. For instance, view-based 3D object retrieval and recognition have been deeply investigated and applied in automatic control and remote navigation. View-based 3D data analysis has attracted much attention in both the academe and industry. However, there is still a long way towards effective view-based 3D semantic understanding. The primary objective of this special issue fosters focused attention on the latest research progress in the view-based 3D processing area, especially how 3D content analysis can benefit from view-based learning technology. The special issue seeks original contribution of works which addresses the challenges from view-based 3D representation, learning, and understanding. In particular, the topic of interest includes but is not limited to:

Learning method for view-based 3D representation
2D view data acquisition for 3D objects
Multiple view registration and calibration for 3D objects
Semantic-oriented feature extraction for multiple views of 3D objects
3D scene reconstruction by a few images
View-based 3D learning and understanding
View-based 3D object retrieval and recognition
View-based shape analysis and morphology
Learning techniques in 3D semantic analysis
Human-Computer-Interaction with view depth information
View-based 3D applications
3D Tracking with multiple views
Medical applications with multiple cameras, such as telemedicine
3D TV and free-viewpoint video techniques
(Multiple-)view-based mobile search
All submitted papers must be clearly written in excellent English and contain only original work, which has not been published by or is currently under review for any other journal or conference. Papers must not exceed 25 pages (one-column, at least 11pt fonts) including figures, tables, and references. A detailed submission guideline is available as “Guide to Authors” at: http://www.journals.elsevier.com/information-sciences/.

All manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EES). The authors must select as “SI:View3D” when they reach the “Article Type” step in the submission process. The EES website is located at: http://ees.elsevier.com/ins/. All papers will be peer-reviewed by three independent reviewers. Requests for additional information should be addressed to the guest editors.

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