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Expanding Primitive Recognition for Hand-sketched Military Course of Action Diagrams

Expanding Primitive Recognition for Hand-sketched Military Course of Action Diagrams Brandon Paulson & Tracy Hammond (advisor). Application. Abstract.

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Expanding Primitive Recognition for Hand-sketched Military Course of Action Diagrams

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  1. Expanding Primitive Recognition for Hand-sketched Military Course of Action Diagrams Brandon Paulson & Tracy Hammond (advisor) Application Abstract Course of action diagrams (COAs) are used by commanders and other military personnel to describe complex battle plans and scenarios. Traditional COA applications use toolbar-based interfaces that are unintuitive and difficult to use, considering there are thousands of unique symbols that make up the domain. Sketching is much more natural to users, but the size of the domain makes recognition challenging. In this work, we introduce a method that is capable of recognizing a large number of primitive shapes, 13 in all, that compose the symbols of the course of action domain. We accomplish this by combining the features of existing recognizers with a new set of features that can help distinguish the less geometrical shapes found in the domain. With this approach, we achieve primitive recognition rates that exceed 98% on a subset of 31 COA symbols drawn by 10 different users. Furthermore, we also have found that training only on a small subset of 31 symbols could allow us to recognize the primitives of over 379 symbols, drawn by a single user, with an accuracy that exceeds 99%. Recognition Overview • COA symbols recognized: 485 (order of magnitude greater than any previous sketch system) • Data samples collected: 6,000 for training and 5,900 (drawn by 8 users) for testing • Accuracy: 89.9% (correct interpretation in top 3); 84.4% (correct interpretation is top) • Primitive Recognition Steps: • Feature computation • PaleoSketch features [1] • CALI features [2] • HHReco features [3] • Long/Rubine features [4,5] • New shape features • Classification • Neural network (multi-layer perceptron) • Sigmoid activation functions • Trained with backpropagation References: [1] B. Paulson and T. Hammond. PaleoSketch: Accurate Primitive Sketch Recognition and Beautification. In IUI, p.1-10, 2008. [2] M. J. Fonseca, C. Pimentel, and J. A. Jorge. CALI: An Online Scribble Recognizer for Calligraphic Interfaces. In AAAI Spring Symposium– Sketch Understanding, p. 51-58, 2002. [3] H. H. Hse and A. R. Newton. Recognition and Beautification of Multi-stroke Symbols in Digital Ink. Computers & Graphics 29(4), p. 533-546, 2005. [4] A. Long Jr., J. Landay, L. Rowe, and J. Michiels. Visual Similarity of Pen Gestures. In CHI, p. 360-367, 2000. [5] D. Rubine. Specifying Gestures by Example. In SIGGRAPH, p. 329-337, 1991. Results Experiment Recognition time: 11 milliseconds/primitive * Difference is statistically significant (t = 5.9878, p = 1.0) † Dataset B consisted of more lines than dataset A, because it included “anticipated” unit symbols, which are drawn as dashed rectangles or diamonds. • Two data sets collected: • Ten users drew 31 COA symbols (above) two times each • One user drew 379 COA symbols between 10 and 20 times each • Experiments conducted: • 10-fold cross validation on data set A with: HHReco, Long/Rubine, CALI, and our system • Train with data set A and test with data set B with our system

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