Call for Papers Special issue on Background Modeling for Foreground Detection in real-world dynamic scenes Background modeling and foreground detection are important steps in the video processing field such as video-surveillance, optical motion capture, multimedia applications, teleconferencing, video editing, human-computer interface, etc. Conventional foreground detection exploits change detection in video sequences. Some algorithms have used frame difference, but the most common approach is background subtraction that detects moving objects or abandoned objects in video sequences taken from a fixed camera. The last decade witnessed very significant publications on background subtraction but recently new applications in which background is not static, such as recordings taken from mobile devices or Internet videos, need new developments to detect robustly moving objects in challenging environments. Thus, effective methods for robustness to deal both with dynamic backgrounds, illumination changes in real scene with fixed cameras or mobile devices are needed and so different strategies may be used such as automatic feature selection, model selection or hierarchical model. Another feature of background modeling methods is that the use of advanced models has to be computed in real-time and low memory requirements. Algorithms may need to be redesigned to meet these requirements. The goals of this special issue are threefold: 1) proposing new mathematical tools in background modeling and foreground detection, 2) developing new strategies to improve foreground detection algorithms to tackle critical situations such as dynamic backgrounds and illumination changes and 3) developing new adaptive and incremental algorithms to achieve real-time applications. Manuscripts are solicited to address a wide range of mathematical tools, strategies, sensors and real-tim implementation including but not limited to the following: Statistical models for background modeling: Extensions of well-known statistical models, reconstructive and discriminative subspace learning models, support vectors models Clustering models for background modeling Fuzzy models for background modeling Geometric approaches for foreground detection Model-based and Physics-based approaches to foreground detection Foreground detection in moving backgrounds (Internet videos) Speeding-up foreground detectors (GPUs) Real-time methods applied to mobile platforms Multiresolution processing Sensor fusion for foreground detection Feature selection The authors are invited to submit fundamental papers in this field, and they can use the SABS1 dataset, the ChangeDetection.net2 dataset and, BMC3 dataset with their validation scheme. Before submission, authors should carefully read over the journal's Author Guidelines, which are located at: http://www.springer.com/computer/image+processing/journal/138 Prospective authors should submit quality and original manuscripts that have not appeared, nor are under consideration, in any other journals. The electronic copy of their manuscript (10-15 pages in the Machine Vision and Applications publication format) should be submitted through the journal manuscript tracking system at the web site: http://www.editorialmanager.com/mvap/ indicating that their contribution is for the special issue "Background Modeling for Foreground Detection in Challenging Conditions". All papers will be reviewed by at least three expert reviewers in relevant fields. Decision will be made based on the novel scientific and technical contribution and their suitability to the scope of this MVA special issue. Timeline for Submission, Review, and Publication: (One year) Submission deadline: Dec. 30, 2012 Notification of acceptance: April 30, 2013 Final manuscript due: Sept. 30, 2013 Tentative publication date: Dec. 30, 2013 Guest Editors (Alphabetical order) Thierry Bouwmans, PhD, Associate Professor at the MIA Lab, Univ. La Rochelle, France E-mail: tbouwman@univ-lr.fr Larry Davis, PhD, Professor at Computer Vision Lab, University of Maryland, USA. E-mail: lsd@umiacs.umd.edu Jordi Gonzalez, PhD, Associate Professor at the Univ. Autonoma de Barcelona and Computer Vision Center, Spain. E-mail: poal@cvc.uab.es Massimo Piccardi, PhD, Professor at FEIT, University of Technology Sydney, Australia. E-mail: Massimo@it.uts.edu.au Caifang Shan, Ph.D. Senior Scientist at the Philips Research, Netherlands. E-mail: caifeng.shan@gmail.com ______________ 1 http://www.vis.uni-stuttgart.de/index.php?id=sabs 2 http://www.changedetection.net/ 3 http://bmc.univ-bpclermont.fr ________________________________________________________ About MVA For applications oriented contributions it is expected they will deal with innovative applications of machine vision and will contain in depth experimental analysis on real world data sets. Specifically, the editors encourage submittals in all applications of image and video related computing including but not limited to Biometric analysis, Medical image analysis, Robot navigation, Surveillance system and Visual inspection. For theoretical contributions it is expected they will make significant contribution to the state of art. All submissions including but not limited to Image registration, Image retrieval, Action recognition, Object tracking, Target detection, Video retrieval are encouraged.