Visualizing concave and convex partitioning of 2D contours
A new method based on parsing the concavity code is used to partition a digital contour into concave and convex sections. Innovative features of the technique include: (1) coerced two-state classification of every point into a concavity or a convexity; (2) linguistic explanation for each points classification; (3) curvature validation via the residue (that portion of the boundary remaining after curvature extraction); (4) preservation of original contour shape; (5) symbolic logic methodology (no floating point operations); (6) parameter-free implementation; (7) linear time and space complexity. No other currently available contour partitioning method exhibits all of these features. The parser achieves two-state partitioning by implementing simple notions of cumulative curvature, vertex adjacency, shallow curvature absorption, and residue sharing. Concavities and convexities are color-coded to help disambiguate complex images such as topographic contour maps and radio frequency propagation plots. To gauge product quality, the observer may appeal visually to the residue for validation.
concavity curvature shape contour partitioning dominant points