Vision can be considered as a feature mining problem. Visually meaningful features are often geometrical, e.g., boundaries (or edges), corners, T-junctions, and symmetries. Mirror symmetry or near mirror symmetry is very common and useful in image and vision analysis. The current paper proposes several different approaches to extract the symmetry mirrors of 2-dimensional (2-D) mirror symmetric shapes. Proper mirror symmetry metrics are introduced based on Lebesgue measures, Hausdorff distance, and lower-dimensional feature sets. Theory and computation of these approaches and measures are studied.
Institute for Mathematics and Its Applications>IMA Preprints Series
On the foundations of vision modeling. II. Mining of mirror symmetry of 2-D shapes.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.