^August September – 2008 ^October

The heritage of a community defines the character and uniqueness of that community. However, because of the high degree of degradation suffered by very old documents, analyzing them is a difficult task. In this presentation, a degradation model is provided which is based on the physical understanding of degradation. This model can also be used to generate ground truth datasets. Based on this degradation model, various enhancement and restoration methods are proposed in different paradigms such as variational framework, PDE-based methods, and statistical methods. For improving the performance of these methods, several classifiers on different levels ranging from local level to global level are introduced. Based on the latter concept, all preprocessing methods can be modified and improved. The methods are tested on several datasets with promising results.

Short bio:

Mohamed Cheriet was born in Algiers (Algeria) in 1960. He 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 (Multimedia Communication in Telepresence) there, 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 200 technical papers in the field, and has served as chair or co-chair of the following international conferences: VI’1998, VI’2000, IWFHR’2002, and ICFHR’2008. He currently serves on the editorial board and is associate editor of several international journals: IJPRAI, IJDAR, and Pattern Recognition. He co-authored a book entitled,”Character Recognition Systems: A guide for Students and Practitioners,” John Wiley and Sons, 2007. Dr. Cheriet is a senior member of the IEEE and the chapter chair of IEEE Montreal Computational Intelligent Systems (CIS).

The initial sequencing and analysis of the human genome, completed in 2003, was a major biological breakthrough, leading to a better understanding of the evolution and function of many human genes. The key to translating this newly acquired knowledge into medical advances relies on the availability of the genomes of many individuals, and in the study of correlation between genomes and diseases. Because the initial human genome was sequenced over 8 years and at the cost of $3 billion, another technological leap was necessary in order to allow for the economical sequencing of the genomes of many humans. Today this leap has been accomplished: Next-Generation Sequencing (NGS) technologies are able to sequence a human genome in a few weeks, at a cost of $10,000 to $100,000. Using these technologies, scientists are hoping to sequence thousands of human genomes in the next few years, and eventually allow each individual to know his or her personal genome.

Some of the biggest remaining challenges on the path to the personal genome are algorithmic. The NGS technologies are only able to read many small fragments of a genomic sequence, and reconstructing the source genome from these fragments, as well as the analysis of the differences between the sets of fragments from various individuals are difficult computational problems. Furthermore, the challenges of using the NGS datasets are exacerbated by the errors and biases in the underlying sequencing technologies. In this talk I will give an overview of genome sequencing and NGS technologies, and discuss some of the computational methods used to address the challenges posed by NGS datasets.

A new aspect for erasure coding is considered, namely, the possibility that some portion of the arriving packets are corrupted in an undetectable fashion. In practice, the corrupted packets may be attributed to a portion of the communication paths that are leading to the receiver and are controlled by an adversary. Alternatively, in case packets are collected from several sources, the corruption may be attributed to a portion of the sources that are malicious.

Corruption resistant fountain codes are presented; the codes resemble and extend the LT and Raptor codes. To overcome the corrupted packets received, our codes use information theoretic techniques, rather than cryptographic primitives such as homomorphic one-way-(hash) functions. Our schemes overcome adversaries by means of using slightly more packets than the minimal number required for revealing the encoded message, and using a majority over the possible decoded values. We also present a more efficient randomized decoding scheme.

Beyond the obvious use, as a rateless corruption resilient erasure code, our code has several important applications in the realm of distributed computing.

Shape inference is a crucial part of image understanding and indeed many studies have addressed this problem in different ways over the years. However, when the object is specular, existing studies usually allow only spare reconstruction and have strong restrictions such as known or calibrated environment. We suggest a new approach for specular shape reconstruction when the environment is neither calibrated nor known. Furthermore unlike previous work, our approach considers general surfaces and allows dense reconstruction. We consider far-field illumination, where the object-environment distance is relatively large, and we examine the dense specular flow that is induced on the image plane through relative object-environment motion. We show that, under these very practical conditions, the observed specular flow can be related to surface shape through a pair of coupled nonlinear partial-differential equation, which we call the '3D shape from specular flow' equation. We show that by solving this equation one can recover the surface. Importantly, this relationship depends only on the environment's relative motion and not on its content. We present two novel reconstruction algorithms. The first one presents an analytic method for recovery of the shape under special environment rotation. In the second algorithm a specular surface is recovered in the presence of arbitrary environment motion. We show that by a linear combination of the observed specular flows we reduce the problem to the same special case that the first algorithm can solve. Finally, we discuss some numerical issues related to the suggested reconstruction algorithms and validate our results in several experiments.