We define in this paper two energy terms, symmetric energy term and asymmetric energy term, which respectively correspond to the symmetric and asymmetric components of an object. The asymmetric energy term must be zero, if the studied object is invariant under a reflection about the x-axis. Accordingly, we formulate the problem of detecting reflectional symmetries as a problem of minimizing the asymmetric energy term. From the local minima of the asymmetric energy term, we can detect all the symmetric axes of any object. Since the asymmetric energy term is expressed as a summation of a set of generalized complex (GC) moments computed for an object, the proposed symmetry detection method is robust against both noise and slight deformation. We use the steepest descent technique to calculate the local minima of the asymmetric energy term, whose initialization is calculated from the most dominant GC moment. Experiments on typical logo images and human brain image have shown the effectiveness and the robustness of the proposed method. To our knowledge, this is the first theory on energy functions that describe the symmetric and asymmetric components of a 2D pattern.
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition