Detection and Segmentation of 2D Curved Reflection Symmetric Structures

Ching L. Teo, Cornelia Fermüller, Yiannis Aloimonos

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.

Curved reflection symmetry detection via Structured Random Forest

Symmetry-constrained segmentation

Example results



[1] S. Tsogkas and I. Kokkinos. Learning-based symmetry detection in natural images. In ECCV, pp. 41--54, 2012.


This work was funded by the support of the European Union under the Cognitive Systems program (project POETICON++), the National Science Foundation under INSPIRE grant SMA 1248056, and by DARPA through U.S. Army grant W911NF-14-1-0384.

Questions? Please contact cteo "at" umd dot edu