César F. Pimentel

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CALI is a fast, simple and compact online recognizer that identifies Scribbles (multi-stroke geometric shapes) drawn with a stylus on a digitizing tablet. Our method is able to identify shapes of different sizes and rotated at arbitrary angles , drawn with dashed, continuous strokes or overlapping lines. We use temporal adjacency to allow users to input the(More)
This paper describes a trainable recognizer for hand-drawn sketches using geometric features. We compare three different training algorithms and select the best approach in terms of cost-performance ratio. The algorithms employ classic machine-learning techniques using a clustering approach. Experimental results show competing performance (95.1%) with the(More)
This paper describes a trainable recognizer for hand-drawn sketches using geometric features. We compare three different training algorithms and select the best approach in terms of cost-performance ratio. Experimental results show competing performance (95.1%) with the non-trainable recognizer (95.8%) previously developed, with obvious gains in flexibility(More)
One of the aims of Affective Computing [19] is to design agents that behave according to models of human affective phenomena. Among these phenomena are the roles that emotions play on various cognitive processes, such as attention, reasoning, decision making, and belief selection. In this paper, we focus on an affective phenomenon that concerns belief(More)
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