SBM17: Sketch Based Interfaces and Modeling 2017

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https://diglib.eg.org/handle/10.2312/2631852

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Browsing SBM17: Sketch Based Interfaces and Modeling 2017 by Subject "based interface"

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    Conquering the Cube: Learning to Sketch Primitives in Perspective with an Intelligent Tutoring System
    (Association for Computing Machinery, Inc (ACM), 2017) Keshavabhotla, Swarna; Williford, Blake; Kumar, Shalini; Hilton, Ethan; Taele, Paul; Li, Wayne; Linsey, Julie; Hammond, Tracy; Holger Winnemoeller and Lyn Bartram
    Design sketching is a powerful tool for expressing ideas from pen and paper e ectively and becoming a more well-rounded communicator. Sketching instructors conventionally employ pen and paper in their classrooms to convey these fundamentals to students. However this traditional approach limits the bandwidth and capability of instructors to give timely and individualized feedback. An intelligent tutoring system can leverage the knowledge of domain expert design sketching instructors so that students can practice and receive real-time feedback outside of classroom hours. Our system leverages consulted instructor insights and observed pedagogical practices of an active university design sketching curriculum, and applies them in a mastery-based progression of exercises that utilize sketch recognition to give real-time feedback. An evaluation of our system's usability in a class of engineering students studying design sketching showed that it performed very well, was seen by the students as a motivating and intuitive practice tool, and allowed the students to improve the accuracy and speed of their sketches.
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    Flow2Code: From Hand-drawn Flowcharts to Code Execution
    (Association for Computing Machinery, Inc (ACM), 2017) Herrera-Camara, Jorge-Ivan; Hammond, Tracy; Holger Winnemoeller and Lyn Bartram
    Flowcharts play an important role when learning to program by conveying algorithms graphically and making them easy to read and understand. Computer-based owchart design requires the user to learn the so ware rst, which o en results in a steep learning curve. Paper-drawn owcharts don't provide feedback. We propose a system that allows users to draw their owcharts directly on paper combined with a mobile phone app that takes a photo of the owchart, interprets it, and generates and executes the resulting code. Flow2Code uses o -line sketch recognition and computer vision algorithms to recognize owcharts drawn on paper. To gain practice and feedback with owcharts, the user needs only a pencil, white paper, and a mobile device. e paper describes a tested system and algorithmic model for recognizing and interpreting o ine owcharts as well as a novel geometric feature, Axis Aligned Score (AAS), that enables fast accurate recognition of various quadrilaterals.