Invited speakers:

Professor Xiaoyi Jiang
Department of Computer Science
University of Münster, Germany

Tutorial: Depth Image Analysis and Interpretation

3D depth images have turned out to be a key information source for solving a large number of challenging applications. Substantial advances have been demonstrated to acquire, process, analyze, and interpret depth images. Meanwhile, depth data plays a vital role in a variety of application areas including human gesture and pose estimation, object recognition, biometrics, cultural heritage applications, and 3DTV (e.g. Depth Image Based Rendering). Through the recent development in consumer depth cameras, in particular the low-cost Kinect, a new era of depth data analysis emerged. Affordable depth cameras are changing the landscape of computer vision and related research fields, with profound impact far beyond consumer electronics. The objective of this tutorial is to present an overview of techniques for depth acquisition, processing, analysis and interpretation, and applications. 

Professor Daniel P. Lopresti

Department of Computer Science and Engineering
Lehigh University, USA

Tutorial: Challenging Pattern Recognition Problems from the Field of Document Analysis

The field of document analysis has developed over the past 50 years, starting with what was once considered to be a simple task – recognizing clean, typeset text – to a wide range of much more difficult problems driven by attempts to build systems that replicate – or even exceed – human levels of perception and understanding.  As a target application for pattern recognition research, documents provide a uniquely rich set of challenges.  In this tutorial, we will survey fundamental problems from the field of document analysis, examine pattern recognition techniques that have proven to be effective, and identify some open problems that are still the subject of active research today.

School of Electrical and Computer Engineering
Oklahoma State University, USA

Tutorial:Using Neural Networks for Pattern Recognition, Function Approximation, Clustering and Prediction

This tutorial will begin with an introduction to the basic types of neural networks. This will be followed by a discussion of how neural networks can be used to solve practical problems in pattern recognition, function approximation, clustering and prediction. Case studies of each type of application will be presented in some detail, and students will have the opportunity for hands-on experiences in training and testing a variety of neural networks.