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INS 2014 : Special Issue on View-Based 3D Representation, Learning, and Understanding | |||||||||||
Link: http://www.journals.elsevier.com/information-sciences/call-for-papers/view-based-3d-representation-learning-and-understanding/ | |||||||||||
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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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