A robust method for scale independent detection of curvature-based criticalities and intersections in line drawings

• The method detects curvature based criticalities and intersections efficiently.
• The method is effective for the noisy and disconnected line drawings.
• The method is effective for line drawings with a variety of brush characteristics.
• The method is utilized to represent shapes in a design grammar interpreter.
• Features in varying scales can be detected in digital drawings.

A novel two-phase iterative method is proposed to identify curvature based criticalities and intersections in line drawings. The method is based on evaluating a field obtained from the image via diffusion using adaptively changing ellipse-shaped analysis windows. The deviation of the average field strength values within the analysis windows from that of an external reference field is used as a metric to evaluate the criticality of a region. The external reference field is computed from an image of a straight line. The experimental results depict that the method is effective in detecting curvature based criticalities and intersections, even for noisy and disconnected drawings as well as drawings drawn with a variety of brush characteristics, such as glass, ocean, ripple, scatter, stamp, strokes effects. Our method can be employed as a part of a design grammar interpreter.