Abstract
Symmetry is a crucial dimension which aids the visual system, human as well as artificial, to organize its environment and to recognize forms and objects. In humans, detection of symmetry, especially bilateral and rotational, is considered to be a primary factor for discovering and interacting with the surrounding environment. We report an enhanced version of the Kondra and Petrosino symmetry detection algorithm, already reported at the "Symmetry Detection from Real World Images" competition at IEEE CVPR2011. The paper includes experimental results achieved by the reflection and rotation symmetry detection algorithm on the datasets made available for the 2013 Symmetry Detection from Real World Images competition.
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