We are interested in enabling natural human-computer interaction by combining techniques from machine learning, computer vision, computer graphics, human-computer interaction and psychology. Specific areas that we focus on include: multimodal human-computer interfaces, affective computing, pen-based interfaces, sketch-based applications, intelligent user interfaces, applications of computer vision and machine learning to solving real world problems. Browse through the publications and research pages to get a flavor of IUI@Koc.
Dr. Sezgin presented the Lab's work on formative assessment at the The ASC-Inclusion final project review meeting in Luxembourg.
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Dr. Sezgin has been awarded a TUBITAK 1003 Grant. The grant will support research in development and evaluation of pen-based intelligent user interfaces for eLearning in the context of the FATIH initiative. Of hundreds of initial proposals submitted to the 1003 call, only 67 made it past the first round, and
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Sezgin&Sezgin's article on finding portable congruential random number generators has been accepted for publication in the Computer Physics Communications journal.
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Many computer graphics applications must fragment freehand curves into sets of prespecified geometric primitives. For example, sketch recognition typically converts hand-drawn strokes into line and arc segments and then combines these primitives into meaningful symbols for recognizing drawings. However, current fragmentation methods' shortcomings make them impractical. For example, they require
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