Team 9 - Document Analysis and ICRA
Synchromedia Lab is involved in the project in two main areas: i) data mining and analysis, and ii) online collaborative tools for scientific research.
DATA MINING AND ANALYSIS
The team will employ automatic data extraction to process digitized data for data enhancement, analysis and mining, and use multi-spectral imaging to digitize original manuscripts into rich multi-spectral digital formats. Synchromedia Lab is equipped with a high-definition, 8-band multi-spectral camera that is able to perform reflectance and UV-florescence spectroscopy on manuscripts. New filters for the camera will be designed using differential spectroscopy methods to improve imaging. The camera will be used to:
(i) Create a dataset of multi-spectral images of historical documents on the IOW provided by the project partners.
(ii) Retrieve true text from multi-spectral images using (a) spatial-frequency analysis based on wavelet transforms or statistical methods such as Bayesian methods; and (b) level set framework and multi-level classifiers.
(iii) Perform visualization of multi-spectral images by considering non-linear mappings from the multi-spectral cube to single-spectral planes using a priori information obtained in restoration and enhancement stages. Synchromedia will develop a generalized nonlinear mapping to reduce sparsity of the multi-spectral cube.
Synchromedia Lab also develops knowledge mining systems to discover patterns and infer new knowledge from data and objects distributed over a number of nodes using multilayer hierocratic interactions independent of the scale and complexity of the system.
ONLINE COLLABORATIVE TOOLS FOR SCIENTIFIC RESEARCH
Synchromedia Lab will provide a highly innovative online, service-based virtual environment to provide real-time state-of-the-art telepresence and collaboration tools. This remote collaborative environment aims to reduce time and space constraints by exploiting existing computer and network infrastructures. Such environments have become popular in business, education and research institutions. Several integrated environments for supporting collaborative work exist, but there are very few solutions that consider the hardware as well as the software sharing aspect of collaborative work spaces. Research conducted at Synchromedia Lab ultimately resulted in a highly integrated platform supporting remote collaborations. Elements considered in this platform are:
(i) Application software offering collaborative services (instant messaging, shared whiteboard, collaborative networking, document and application sharing, videoconferencing, etc.).
(ii) A high-performance network linking a set of specialized devices for processing multi-sensor data.
(iii) Ability to work collaboratively in real-time on a document, image or other digital artifacts using enhancement and extraction tools developed in the document analysis and data mining project.
Mohamed Cheriet, École de technologie supérieure
Mohamed Cheriet received his B.Eng. from USTHB University (Algiers) in 1984 and his M.Sc. and Ph.D. degrees in Computer Science from the University of Pierre et Marie Curie (Paris VI) in 1985 and 1988, respectively. Since 1992, he has been a professor in the Automation Engineering Department at the École de Technologie Supérieure (University of Quebec), Montreal, and was appointed full professor there in 1998. He co-founded the Laboratory for Imagery, Vision and Artificial Intelligence (LIVIA) at the University of Quebec, and was its director from 2000 to 2006. He also founded the Synchromedia Consortium and Laboratory for multimedia communication in telepresence at ETS, and has been its director since 1998. His interests include document image analysis, OCR, mathematical models for image processing, pattern classification models and learning algorithms, as well as perception in computer vision. Dr. Cheriet has published more than 250 technical papers in the field and has served as chair or co-chair of the following international conferences: VI'1998, VI'2000, IWFHR'2002, ICFHR'2008, ISSPA’2013. He currently serves on the editorial board and is associate editor of several international journals: Pattern Recognition, IJPRAI, and IJDAR. He is a co-author of the 2007 John Wiley and Sons textbook, Character Recognition Systems: A guide for Students and Practitioners, and co-editor of the 1999 World Scientific Series in Machine Perception & Artificial Intelligence volume, Vision Interface: Real World Applications of Computer Vision. Dr. Cheriet has been a senior member of the IEEE since 2000 and the founder and former chair of the Montreal IEEE CIS (Computational Intelligent Systems) chapter.
Raja Sengupta, McGill University
Dr. Sengupta received his MSc and PhD from Southern Illinois University at Carbondale. Since 2003, he has been a member of the Department of Geography and the School of Environment at McGill University. His research interests include theory and practice of geographic information science (spatial decision support systems and agent-based modelling) and applications of GIS to water resource management, conservation payments, payments for environmental services (PES)and spatial edpidemiology.