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Item ShortStraw: A Simple and Effective Corner Finder for Polylines(The Eurographics Association, 2008) Wolin, Aaron; Eoff, Brian; Hammond, Tracy; Christine Alvarado and Marie-Paule CaniIn this paper we introduce ShortStraw, a simple and highly accurate polyline corner finder. ShortStraw uses a bottom-up approach to find corners by: (1) resampling the points of the stroke, (2) calculating the straw distance between the endpoints of a window around each resampled point, and (3) taking the points with the minimum straw distance to be corners. Using an all-or-nothing accuracy measure, ShortStraw achieves an accuracy more than twice that of the current best benchmark.Item SOUSA: Sketch-based Online User Study Applet(The Eurographics Association, 2008) Paulson, Brandon; Wolin, Aaron; Johnston, Joshua; Hammond, Tracy; Christine Alvarado and Marie-Paule CaniAlthough existing domain-specific datasets are readily available, most sketch recognition researchers are forced to collect new data for their particular domain. Creating tools to collect and label sketched data can take time, and, if every researcher creates their own toolset, much time is wasted that could be better suited toward advanced research. Additionally, it is often the case that other researchers have performed collection studies and collected the same types of sketch data, resulting in large duplications of effort. We propose, and have built, a generalpurpose sketch collection and verification tool that allows researchers to design custom user studies through an online applet residing on our group's web page. By hosting such a tool through our site, we hope to provide researchers with a quick and easy way of collecting data. Additionally, our tool serves to create a universal repository of sketch data that can be made readily available to other sketch recognition researchers.Item From Paper to Machine: Extracting Strokes from Images for use in Sketch Recognition(The Eurographics Association, 2008) Rajan, Pankaj; Hammond, Tracy; Christine Alvarado and Marie-Paule CaniSketching is a way of conveying ideas to people of diverse backgrounds and culture without any linguistic medium. With the advent of inexpensive tablet PCs, online sketches have become more common, allowing for stroke-based sketch recognition techniques, more powerful editing techniques, and automatic simulation of recognized diagrams. Online sketches provide significantly more information than paper sketches, but they still do not provide the flexibility, naturalness, and simplicity of a simple piece of paper. Recognition methods exist for paper sketches, but they tend to be domain specific and don't benefit from the advances of stroke-based sketch recognition. Our goal is to combine the power of stroke-based sketch recognition with the flexibility and ease of use of a piece of paper. In this paper we will present a stroke-tracing algorithm that can be used to extract stroke data from the pixilated image of the sketch drawn on paper. The presented method handles overlapping strokes and also attempts to capture sequencing information, which is helpful in many sketch recognition techniques. We present preliminary results of our algorithm on several paper-drawn, hand-sketched, scanned-in pixilated images.