Monique Thonnat
INRIA, France
Semantic Activity Recognition
Abstract: Extracting automatically the semantics from visual data is a real challenge. We describe in this paper how recent work in cognitive vision leads to significative results in activity recognition for visualsurveillance and video monitoring. In particular we present work performed in the domain of video understanding in our PULSAR team at INRIA in Sophia Antipolis. Our main objective is to analyse in real-time video streams captured by static video cameras and to recognize their semantic content. We present a cognitive vision approach mixing 4D computer vision techniques and activity recognition based on a priori knowledge. Applications in visualsurveillance and healthcare monitoring are shown. We conclude by current issues in cognitive vision for activity recognition.
Zoubin Ghahramani
University of Cambridge
Bayesian Methods for Artificial Intelligence and Machine Learning
Abstract: Bayesian methods provide a framework for representing and manipulating uncertainty, for learning from noisy data, and for making decisions that maximize expected utility----components which are important to both AI and Machine Learning. However, although Bayesian methods have become more popular in recent years, there remains a good degree of skepticism with respect to taking a fully Bayesian approach. This talk will introduce fundamental topics in Bayesian statistics as they apply to machine learning and AI, and address some misconceptions about Bayesian approaches. I will then discuss some current work on non-parametric Bayesian machine learning, particularly in the area of unsupervised learning.
Pascal Van Hentenryck
Brown University
The Impact of Constraint Programming
Abstract: Constraint programming is a success story for artificial intelligence. It quickly moved from research laboratories to industrial applications and is in daily use to solve complex optimization throughout the world. At the same time, constraint programming continued to evolve, addressing new needs and opportunities. This talk reviews some recent progress in constraint programming, including its hybridization with other optimization approaches, the quest for more autonomous search, and its applications in a variety of nontraditional areas.
Georges Metakides
Patras University, Greece
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