A Quantitative Evaluation of Symmetry Detection Algorithms

Po-Chun Chen, James H. Hays, Seungkyu Lee, Minwoo Park, and Yanxi Liu
tech. report CMU-RI-TR-07-36, Robotics Institute, Carnegie Mellon University, October, 2007


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Abstract
Symmetry is one of the most important cues for human and machine perception of the chaotic real world. For over three decades now, automatic symmetry detection from images/patterns has been a standing topic in com- puter vision. We observe a surge of new symmetry detection algorithms that go beyond simple bilateral symmetry detection. This paper presents a sys- tematic, quantitative evaluation of rotation, reflection and translation sym- metry detection algorithms published within the past 1.5 years. We provide a set of carefully chosen synthetic and real images that contain both single and multiple symmetries and a diverse range of computational challenges. We also provide their associated, hand-labeled ground truth. We propose a well-defined quantitative evaluation scheme for an effective validation and comparison of different symmetry detection algorithms. Our results indicate that even after several decades of effort, symmetry detection from real-world images remains a challenging, unsolved problem in computer vision.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Associated Lab(s) / Group(s): Computational Symmetry
Number of pages: 17

Text Reference
Po-Chun Chen, James H. Hays, Seungkyu Lee, Minwoo Park, and Yanxi Liu, "A Quantitative Evaluation of Symmetry Detection Algorithms," tech. report CMU-RI-TR-07-36, Robotics Institute, Carnegie Mellon University, October, 2007

BibTeX Reference
@techreport{Hays_2007_5873,
   author = "Po-Chun Chen and James H. Hays and Seungkyu Lee and Minwoo Park and Yanxi Liu",
   title = "A Quantitative Evaluation of Symmetry Detection Algorithms",
   booktitle = "",
   institution = "Robotics Institute",
   month = "October",
   year = "2007",
   number= "CMU-RI-TR-07-36",
   address= "Pittsburgh, PA",
}