Topics

Machine Vision

Machine vision is a branch of engineering that uses computer vision in the context of manufacturing. While the scope of Machine Vision is broad and a comprehensive definition is difficult to distil, a "generally accepted definition of machine vision is '... the analysis of images to extract data for controlling a process or activity.'" Put another way, Machine Vision processes are targeted at "recognizing the actual objects in an image and assigning properties to those objects--understanding what they mean." The commercial applications of Machine Vision include tracking of people in crowds, numberplate recognition, reconstructing the 3D geometry of an environment.

Multimodal Interaction and Adaptive Learning

Traditional Pattern Recognition (PR) and Machine Learning (ML) have generally focused on full automation; that is, in developing technologies ultimately aiming at fully replacing human beings in tasks that require complex perceptive and/or cognitive skills. However, full automation often proves elusive or unnatural in many applications where technology is expected to assist rather than replace the human agents. This asks for a paradigm shift which should place PR/ML within the framework of human interaction.

Multimodal Interaction and Adaptive Learning deals with the fundamental work needed to address the research challenges and opportunities entailed by this paradigm shift. These include: interaction analysis and modelling, multimodal processing and fusion, interactive performance estimation and measurement, and several emerging forms of machine learning that look especially promising in the interactive framework (online, adaptive, active, semi-supervised, limited feedback, reinforcement, etc.).

Handwritten Text Recognition

Many documents used every day are handwritten documents, as for example, postal addresses, bank cheques, medical prescriptions, a big quantity of historical documents, an important part of the information gathered by forms, etc. In many cases it would be interesting to have these documents in digital form rather than paper based, in order to provide new ways to indexing, consulting and working with these documents.

Handwriting text recognition (HTR) can be defined as the ability of a computer to transform handwritten input represented in its spatial form of graphical marks into equivalent symbolic representation as ASCII text. Usually, this handwritten input comes from sources such as paper documents, photographs or electronic pens and touch-screens.