May 27–29 2013, Uppsala, Sweden
Chair for the invited speaker sessions is Christer Kiselman.
Professor Bhabatosh Chanda of the Indian Statistical Institute (Kolkata, India) received a B.E. in 1979 and a PhD in 1988 from the University of Calcutta. His research interest include image and video processing, pattern recognition, computer vision and mathematical morphology.
He has received ‘Young Scientist Medal’ of the Indian National Science Academy in 1989, ‘Computer Engineering Division Medal’ of the Institution of Engineers (India) in 1998, ‘Vikram Sarabhai Research Award’ in 2002, and IETE-Ram Lal Wadhwa Gold medal in 2007. He is a fellow of the Institute of Electronics and Telecommunication Engineers (FIETE), the National Academy of Science, India (FNASc), the Indian National Academy of Engineering (FNAE), and the International Association of Pattern Recognition (FIAPR).
Adaptive Morphologic Regularizations for inverse problems
Regularization is a well-known technique for obtaining stable solution of ill-posed inverse problems. In this paper we establish a key relationship among the regularization methods with edge-preserving noise filtering method which leads to an efficient adaptive regularization methods. We show experimentally the efficiency and superiority of the proposed regularization methods for some inverse problems, e.g. deblurring and super-resolution (SR) image reconstruction.
Ron Kimmel is a Professor of Computer Science at the Technion (Haifa, Israel), where he holds the Montreal Chair in Sciences. He has worked in various areas of image and shape analysis in computer vision, image processing, and computer graphics. Kimmel’s interest in recent years has been non-rigid shape processing and analysis, medical imaging and computational biometry, numerical optimization of problems with a geometric flavor, and applications of metric and differential geometry. Kimmel is an IEEE Fellow for his contributions to image processing and non-rigid shape analysis. He is author of two books, the founder of the Geometric Image Processing Lab, as well as several successful image processing and analysis companies.
The Laplace–Beltrami operator: a ubiquitous tool for image and shape processing
The ubiquity of the Laplace–Beltrami operator in shape analysis can be seen by observing the wide variety of applications where it has been found to be useful. Here we demonstrate a small subset of such uses with their latest developments including a scale-invariant transform for general triangulated meshes, an effective and efficient method for denoising meshes using Beltrami flows via high-dimensional embeddings of 2D manifolds and finally the possibility of viewing the framework of geodesic active contours as a surface minimization having the Laplace–Beltrami operator as its main ingredient.
Christine Voiron-Canicio is Professor of geography (University of Nice Sophia Antipolis, Nice, France) and head of the CNRS Laboratory ESPACE. She specialises in spatial analysis methods and spatial modelling. Her current field and lab work focuses on modelling the urban spread using mathematical morphology, performing simulations and spatialising scenarii of evolution (geoprospective approach). Among various studies, she has a specific interest in land use changes and spatial processes occuring in urban Mediterranean areas, at various scales.
Geography, Mathematics and Mathematical Morphology
Mathematical Morphology (MM) has been introduced in geographical sciences during the years 1970–1980. However it did not find the same echo in the geographer community according the areas of research. Unlike remote sensing where MM tools have been used as early as in the eighties and are nowadays widespread, in the research works resorting to spatial analysis and modelling, MM is much rarer. And yet morphological analyses exactly match the purpose of spatial analysis. This talk aims to demonstrate the relevance of MM in geography and more precisely in spatial analysis. The three applications proposed focus on socio-economic issues: urban zones of influence detection, regional differentiations analysis and spatial modelling. Finally, are highlighted and discussed the major shortcomings which hold up the spread of MM in geography, planning and geomatics.