Handling Ambiguity in Constraint-based Recognition of Stick Figure Sketches
James V. Mahoney and Markus P.J. Fromherz
Abstract
Even seemingly simple drawings, diagrams, and sketches are hard for
computer programs to interpret, because these inputs can be highly
variable in several respects. This variability corrupts the expected
mapping between a prior model of a configuration and an instance of it
in the scene. We propose a scheme for representing ambiguity
explicitly, within a subgraph matching framework, that limits its
impact on the computational and program complexity of matching.
First, ambiguous alternative structures in the input are represented
explicitly by coupled subgraphs of the data graph, using a class of
segmentation post-processing operations termed graph elaboration.
Second, the matching process enforces mutual exclusion constraints
among these coupled alternatives, and preferences or rankings
associated with them enable better matches to be found early on by a
constrained optimization process. We describe several elaboration
processes, and extend a straightforward constraint-based subgraph
matching scheme to elaborated data graphs. The discussion focuses on
the domain of human stick figures in diverse poses.
PDF file
Back to the top.