Image Processing can be used to solve complex problems in many different areas, such as document analysis and face detection. However, the design of image processing operators is a very challenging task that requires deep knowledge of both image processing techniques and the domain of application. An alternative formulation consists in using machine learning to estimate a local transformation from a set of pairs of images containing an input and its processed version.
The main goal of this research project is to study, develop and validate algorithms and methods for the recognition of handwritten mathematical expressions. This is an important and challenging problem in the field of Pattern Recognition. The variety of symbols to be recognized, the variations in writing style, the need to analyze 2D spatial arrangement of the symbols, different notations, intrinsic ambiguities, among other issues make this a non trivial problem.
A powerful and flexible modular environment for exploration of medical images, both for clinical and scientific purposes. The aim is to provide image conversion, processing, visualization and analysis tools for a number of medical image modalities, such as Magnetic Resonance (MR), Computer Tomography (CT), Positron Emission and Single Photon Emission Tomography (PET and SPECT).