2017 IEEE International Conference on Computer Vision (ICCV) 2017
DOI: 10.1109/iccv.2017.92
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Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild

Abstract: Humans take advantage of real world symmetries for various tasks, yet capturing their superb symmetry perception mechanism with a computational model remains elusive. Motivated by a new study demonstrating the extremely high inter-person accuracy of human perceived symmetries in the wild, we have constructed the first deeplearning neural network for reflection and rotation symmetry detection (Sym-NET), trained on photos from MS-COCO (Microsoft-Common Object in COntext) dataset with nearly 11K consistent symmet… Show more

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Cited by 33 publications
(61 citation statements)
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References 69 publications
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“…In this regard, borrowing from other fields such as natural scene statistics and cognitive vision can be rewarding. For example, understanding how humans perceive symmetry [134,135] or image clutter [136] in generated images versus natural scenes can give clues regarding the plausibility of the generated images.…”
Section: Summary and Future Workmentioning
confidence: 99%
“…In this regard, borrowing from other fields such as natural scene statistics and cognitive vision can be rewarding. For example, understanding how humans perceive symmetry [134,135] or image clutter [136] in generated images versus natural scenes can give clues regarding the plausibility of the generated images.…”
Section: Summary and Future Workmentioning
confidence: 99%
“…The world under discussion is the world of pictures. In contrast to many machine-vision approaches from Grenander's Pattern Theory [6] to modern convolutional deep learning models [1] where the input is a pixel matrix, this work prefers a continuous domain, and accordingly sparse representations. Among the possible two-dimensional manifolds, the Cartesian vector space over the field of real numbers is the preferred choice.…”
Section: The Gestalt Domain and Some Operations On Itmentioning
confidence: 99%
“…The objects themselves are not represented at all. Funk and Liu use such rationale for their approach [1], which sets the current best performance in recognizing reflection and rotational symmetry. In their discussion, they use the dichotomy dense-sparse coding, where dense refers to approaches that cover the complete domain by sampling it with activation cells, or "neural layers".…”
Section: Introductionmentioning
confidence: 99%
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