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Performance Evaluation of State-of-the-Art Discrete Symmetry Detection Algorithms
Members: Minwoo Park, seungkyu Lee, James Hays, Po-Chun Chen, Somesh Kashyap, Asad Butt, and Yanxi Liu
Project DescriptionSymmetry is one of the important cues for human and machine perception of the world. For over three decades, automatic symmetry detection from images/patterns has been a standing topic in computer vision. We present a timely, systematic, and quantitative performance evaluation of three state of the art discrete symmetry detection algorithms. This evaluation scheme includes a set of carefully chosen synthetic and real images presenting justified, unambiguous single or multiple dominant symmetries, and a pair of well-defined success rates for validation. We make our 176 test images with associated hand-labeled ground truth publicly available with this paper. In addition, we explore the potential contribution of symmetry detection for object recognition by testing the symmetry detection algorithm on three publicly available object recognition image sets (PASCAL VOC'07, MSRC and Caltech-256). Our results indicate that even after several decades of effort, symmetry detection in real-world images remains a challenging, unsolved problem in computer vision. Meanwhile, we illustrate its future potential in object recognition.  Figure 1. Sample images and results from our test image set  Figure 2. The pairwise reflection and rotation symmetry detection algorithms evaluation on our 176 test-images with labeled ground truth Publications
Related LinksExperimental results - reflection symmetryExperimental results - rotation symmetry Base test image and ground truth for Figure 2 above(Figure 4 in the paper) Test image set |
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