Symmetry is a common geometric invariant that exists in nature and man-made environments. In spite of its ubiquitous nature, the (1) detection of symmetry and the (2) extraction of symmetrical structures that supports the symmetry remains a challenge in Computer Vision. In this work, we present a complete approach that addresses these two processes together, specifically for the case of curved reflection symmetries found in articulated structures. For the detection of curved reflection symmetries, we train a Structured Random Forest (SRF) classifier that associates local symmetrical measures computed via fast histogram comparisons [1] from color, texture, Gabor and spectral features with curved symmetries. Next, we embed the detected curved symmetries into a 5-way Markov Random Field (MRF) representation of the image edges so that symmetry is enforced in the final segmentation using graph-cuts. As symmetry is enforced locally, our approach is able to handle 1) multiple branches and 2) approximate symmetries which are common in real images.
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