Acivs 2013

Advanced Concepts for Intelligent Vision Systems

Poznan Acivs logo

Oct. 28-31 2013

City Park Hotel, Poznan, Poland

LNCS

Acivs 2013 Invited Speakers' Biographies

Jocelyn Chanussot

Grenoble Institute of Technology

Jocelyn Chanussot received the M.Sc. degree in electrical engineering from the Grenoble Institute of Technology (Grenoble INP), Grenoble, France, in 1995, and the Ph.D. degree from Savoie University, Annecy, France, in 1998.

In 1999, he was with the Geography Imagery Perception Laboratory for the Delegation Generale de l'Armement (DGA - French National Defense Department). Since 1999, he has been with Grenoble INP, where he was an Assistant Professor from 1999 to 2005, an Associate Professor from 2005 to 2007, and is currently a Professor of signal and image processing. He is conducting his research at the Grenoble Images Speech Signals and Automatics Laboratory (GIPSA-Lab).

His research interests include image analysis, multicomponent image processing, nonlinear filtering, and data fusion in remote sensing. Since july 2013, he is an Adjunct Professor of the University of Iceland.

Dr. Chanussot is the founding President of IEEE Geoscience and Remote Sensing French chapter (2007-2010) which received the 2010 IEEE GRS-S Chapter Excellence Award. He was the co-recipient of the NORSIG 2006 Best Student Paper Award, the IEEE GRSS 2011 Symposium Best Paper Award, the IEEE GRSS 2012 Transactions Prize Paper Award and the IEEE GRSS 2013 Highest Impact Paper Award. He was a member of the IEEE Geoscience and Remote Sensing Society AdCom (2009-2010), in charge of membership development. He was the General Chair of the first IEEE GRSS Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote sensing (WHISPERS). He was the Chair (2009-2011) and Cochair of the GRS Data Fusion Technical Committee (2005-2008).

He was a member of the Machine Learning for Signal Processing Technical Committee of the IEEE Signal Processing Society (2006-2008) and the Program Chair of the IEEE International Workshop on Machine Learning for Signal Processing, (2009). He was an Associate Editor for the IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2005-2007) and for Pattern Recognition (2006-2008). Since 2007, he is an Associate Editor for the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. Since 2011, he is the Editor-in-Chief of the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. He is a Fellow of the IEEE (2012).

Presentation: Hierarchical analysis of hyperspectral images

After decades of use of multispectral remote sensing, most of the major space agencies now have new programs to launch hyperspectral sensors, recording the reflectance information of each point on the ground in hundreds of narrow and contiguous spectral bands. The spectral information is instrumental for the accurate analysis of the physical component present in one scene. But, every rose has its thorns : most of the traditional signal and image processing algorithms fail when confronted to such high dimensional data (each pixel is represented by a vector with several hundreds of dimensions). In this talk, we will start by a general presentation of the challenges and opportunities offered by hyperspectral imaging systems in a number of applications. We will then explore these issues with a hierarchical approach, briefly illustrating the problem of spectral unmixing and of super-resolution, then moving on to pixel-wise classification (purely spectral classification and then including contextual features). Eventually, we will focus on the extension to hyperspectral data of a very powerful image processing analysis tool: the Binary Partition Tree (BPT). It provides a generic hierarchical representation of images and consists of the two following steps: - construction of the tree : one starts from the pixel level and merge pixels/regions progressively until the top of the hierarchy (the whole image is considered as one single region) is reached. To proceed, one needs to define a model to represent the regions (for instance: the average spectrum - but this is not a good idea) and one also needs to define a similarity measure between neighbouring regions to decide which ones should be merged first (for instance the euclidean distance between the model of each region - but this is not a good idea either). This step (construction of the tree) is very much related to the data. - the second step is the pruning of the tree: this is very much related to the considered application. The pruning of the tree leads to one segmentation. The resulting segmentation might not be any of the result obtained during the iterative construction of the tree. This is where this representation outperforms the standard approaches. But one may also perform classification, or object detection (assuming an object of interest will appear somewhere as one node of the tree, the game is to define a suitable criterion, related to the application, to find this node). Results are presented on various hyperspectral images.

Marek Domanski

Poznan University of Technology

Marek Domañski received M.S. (1978), Ph.D (1983) and Habilitation (1990) degrees from Poznañ University of Technology, Poland where he is Chair Professor for Multimedia Telecom. and Microelectronics since 1993. His team submitted highly ranked technology proposals to MPEG for scalable video (2004) and 3D video coding (2011), developed one of the very first AVC tv decoders (2004) and various AVC and AAC HE codec implementations and improvements. He is an author or co-author of 3 books and over 200 papers in journals and proceedings of international conferences. The contributions were mostly on image, video and audio compression, image processing, multimedia systems, 3D video and color image technology, digital filters and multidimensional signal processing. He chaired or co-chaired international conferences: IWSSIP 1997 and 2004, ICSES 2004, 73rd MPEG, EUSIPCO 2007, PCS 2012, AVSS 2013. He served as a member of committees of international journals and international conferences, and as Area Editor of Signal Processing: Image Communications journal in 2005-2010.

Presentation: Free-Viewpoint Video

The term "free-viewpoint video" refers to representations of a visual scene that allow a viewer to navigate virtually through a scene. Such a representation mostly consists of a number of views and depth maps, and may be used to synthesize virtual views for arbitrary points on a trajectory of navigation. Therefore, a viewer is able to see the scene from an arbitrary chosen viewpoint, like using a virtual camera. The views might be monoscopic but stereoscopic views provide more realistic navigation through a scene. Significant recent interest in free-viewpoint video is stimulated by potential applications that include not only interactive movies and television coverage of sports, in particular of boxing, sumo, judo or wrestling, but also interactive theatre and circus performances, courses and user manuals. In particular, "free-viewpoint television" is already a subject of extensive research. In this lecture, we will consider acquisition of multiview video and the corresponding preprocessing that includes system calibration, geometrical rectification and color correction. Further, we will proceed to production of depth information for multiview video. After this step, we get the "multiview plus depth" (MVD) representation that needs to be transmitted. The emerging MVD compression technologies will be briefly reported together with the corresponding international standardization projects. At last, the virtual view synthesis will be discussed. The "free-viewpoint video" technology is also closely related to technology of autostereoscopic displays that are used to present 3D video to many viewers who do not need to wear special glasses. Displayed are many different views, possibly between 30 and 170, that are mostly synthesized in a computer connected to the display. There also arise issues of 3D virtual-view-based quality assessment and 3D visual attention. The "free-viewpoint video" is a "hot" research topic and the lecture will be illustrated by the recent research results.

Ales Leonardis

University of Birmingham

Alesš Leonardis is Professor of Robotics at the University of Birmingham and co-Director of the Centre for Computational Neuroscience and Cognitive Robotics. His research interests include robust and adaptive methods for computer vision, object and scene recognition and categorization, statistical visual learning, 3D object modeling, and biologically motivated vision. He has been an associate editor of the IEEE PAMI, an editorial board member of Pattern Recognition, an editor of the Springer book series Computational Imaging and Vision. He was also a Program Co-chair of the European Conference on Computer Vision 2006. In 2002, he coauthored a paper, Multiple Eigenspaces, which won the 29th Annual Pattern Recognition Society award. In 2004 he was awarded a prestigious national award for his research achievements. He is a fellow of the IAPR and a member of the IEEE and the IEEE Computer Society.

Presentation: Hierarchical Compositional Representations for Computer Vision

To be announced


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