SBM11: Sketch Based Interfaces and Modeling 2011
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Browsing SBM11: Sketch Based Interfaces and Modeling 2011 by Subject "Categories and Subject Descriptors (according to ACM CCS): I.4.6 [Computer Graphics]: Image Processing andComputer Vision Segmentation - Edge and feature detection"
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Item ClassySeg: A Machine Learning Approach to AutomaticStroke Segmentation(The Eurographics Association, 2011) Herold, J.; Stahovich, T. F.; Tracy Hammond and Andy NealenWe present ClassySeg, a technique for segmenting hand-drawn pen strokes into lines and arcs. ClassySeg employs machine learning techniques to infer the segmentation intended by the drawer. The technique begins by identifying a set of candidate segment points, consisting of all curvature maxima. Features are computed for each candidate point based on speed, curvature, and other geometric properties. These features are adapted from numerous prior segmentation approaches, effectively combining their strengths. These features are used to train a statistical classifier to identify which candidate points are true segment points. A beam search is used to approximate the optimal subset of features to use as input to the classifier. ClassySeg is more accurate than previous techniques foruser-independent training conditions, and is as good as the current state-of-the-art algorithm for user-optimized conditions. More importantly, ClassySeg represents a movement away from prior heuristic-based approaches towards a more general and extensible approach.