Communications of the ACM
Imaging Sciences R&D Laboratories in Argentina
Credit: Imaging Sciences Laboratory
We use the term imaging sciences to refer to the overarching spectrum of scientific and technological contexts which involve images in digital format including, among others, image and video processing, scientific visualization, computer graphics, animations in games and simulators, remote sensing imagery, and also the wide set of associated application areas that have become ubiquitous during the last decade in science, art, human-computer interaction, entertainment, social networks, and many others. As an area that combines mathematics, engineering, and computer science, this discipline arose in a few universities in Argentina mostly in the form of elective classes and small research projects in electrical engineering or computer science departments. Only in the mid-2000s did some initiatives aiming to generate joint activities and to provide identity and visibility to the discipline start to appear. In this short paper, we present a brief history of the three laboratories with the most relevant research and development (R&D) activities in the discipline in Argentina, namely the Imaging Sciences Laboratory of the Universidad Nacional del Sur, the PLADEMA Institute at the Universidad Nacional del Centro de la Provincia de Buenos Aires, and the Image Processing Laboratory at the Universidad Nacional de Mar del Plata.
The Imaging Sciences Laboratorya of the Electrical and Computer Engineering Department of the Universidad Nacional del Sur Bahía Blanca began its activities in the 1990s as a pioneer in Argentina and Latin America in research and teaching in computer graphics, and in visualization. The facility currently is staffed by six National Research and Technology Council (CONICET) fellows (Claudio Delrieux, Alejandro Vitale, Felix Thomsen, Natalia Revollo, Marina Cipolletti, and Noelia Revollo), plus three postdocs and 13 Ph.D. candidates, who are actively researching novel image analysis methods including multifractality, complexity theory, and deep learning in the hope of generating breakthroughs in research contexts including three-dimensional (3D) medical images,1 3D shape analysis,2 biometrics,3 biomedical signal analysis,4 microscopy,5 remote sensing,6 satellite imagery,7 and environmental monitoring,8 while maintaining research and development activities in scientific visualization and computer graphics.
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