Interpreting Sloppy Stick Figures by Graph Rectification and Constraint-based Matching

James V. Mahoney and Markus P.J. Fromherz

Abstract

Machine systems for understanding hand-drawn sketches and diagrams must reliably interpret curvilinear configurations that are sloppily drawn and highly variable in form. We propose a two-stage subgraph matching framework for sketch recognition that can accommodate great variability in form and yet provide efficient matching and easy extensibility to new configurations. First, a rectification stage corrects the initial data graph for the common deviations of each kind of constituent local configuration from its ideal form. Matching is then accomplished by a straightforward constraint-based subgraph matching scheme. We explore the approach in the domain of human stick figures in arbitrary poses.

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