Project summary

The objective is to build a Europe-wide Distributed Institute which will pioneer principled methods of pattern analysis, statistical modelling and computational learning as core enabling technologies for multimodal interfaces that are capable of natural and seamless interaction with and among individual human users.

At each stage in the process, machine learning has a crucial role to play. It is proving an increasingly important tool in Machine Vision, Speech, Haptics, Brain Computer Interfaces, Information Extraction and Natural Language Processing; it provides a uniform methodology for multimodal integration; it is an invaluable tool in information extraction; while on-line learning provides the techniques needed for adaptively modelling the requirements of individual users. Though machine learning has such potential to improve the quality of multimodal interfaces, significant advances are needed, in both the fundamental techniques and their tailoring to the various aspects of the applications, before this vision can become a reality.

The institute will foster interaction between groups working on fundamental analysis including statisticians and learning theorists; algorithms groups including members of the non-linear programming community; and groups in machine vision, speech, haptics, brain-computer interfaces, natural language processing, information-retrieval, textual information processing and user modelling for computer human interaction, groups that will act as bridges to the application domains and end-users.

Recently Funded PASCAL Events PASCAL Symposium, Bled, 26-30 Jan 2008 Complex systems 2008 Sheffield Mathematics for Data Modelling Winter School 4th Workshop on Ensemble Methods and Boosting Visual Object Classes Challenge 2008 (VOC2008) Human-machine comparisons of consonant recognition in noise

Latest Publications A Tiered Move-making Algorithm for General Pairwise MRF’s Vibhav Vineet, Jonathan Warrell and Philip Torr Conference or Workshop Item (June 2012) Fast Human Pose Detection Using Randomized Hierarchical Cascades of Rejectors Grégory Rogez, Jonathan Rihan, Carlos Orrite-Uruñuela and Philip Torr Article (2012) Three things everyone should know to improve object retrieval Relja Arandjelovi´c and Andrew Zisserman Conference or Workshop Item (June 2012)