Computer vision is the science and technology of teaching computers to interpret images and videos as well as humans do. For its study, a computer vision system can be decomposed into 3 large blocks: image capture and basic processing (low level), analysis and feature extraction (medium level) and automatic recognition and decision making (high level). The image modeling corresponds to the medium level of analysis, with which it is possible to obtain an alternative representation for the image and its contained objects. The objectives in this line are devoted to the design and implementation of innovative methods and algorithms for image representation, analysis and understanding. Here, the high dimensional modeling is addressed using natural image statistics and bioinspired sparse representations. Novel algorithms and methodologies for object recognition and scene understanding are developed for applications in different areas such as medical imaging, multimodal biometrics, man-machine interfaces, biomechanics for rehabilitation, precision agriculture, video analytics and the development of libraries for specific purposes.
CAID 2016 UNL
PICT 2015 ANPCyT
SAT 2016 Fundación Sadosky – UNL
PPCP 2011 SPU-Mercosur
CAID 2009 UNL