Symmetry is a pervasive phenomenon present in all forms and scales in natural and man-made environments. It is not surprising, therefore, that humans, animals and insects have evolved an innate ability to perceive and take advantage of symmetry. What IS surprising is that perception and recognition of symmetry have yet to be fully explored in machine intelligence, in particular computer vision. Despite an understanding of how the concept of repeated patterns is generalized by the mathematics of group theory, and despite attempts over four decades to design algorithms that seek symmetry from digital data, there are very few effective computational tools for automated symmetry analysis available today. The goal of this competition is to benchmark state of the art symmetry detection algorithms (previously published and new algorithms) on real images.
0. Keynote Speaker:
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Parallel model for
the salience of mirror symmetry in natural patterns Mirror symmetry detection is considered
extremely rapid, based on experiments with random noise. Symmetry
detection in natural settings, however, is accomplished against structured
backgrounds. We measured temporal thresholds for detecting the mirror
symmetry axis in patterns assembled from 101 natural textures. Thresholds
ranged from 0.028 to 0.568 sec indicating a wide range of salience
(1/Threshold). For estimating symmetric energy, we used pairs of
mirror-symmetric cortex-like filters, connected with AND junctions, in a
filter-prune-filter-select model. The model easily identified the axis of
symmetry for all patterns, but symmetry magnitude quantified at this axis
correlated weakly with salience... |
1. Co-Chairs:
2. Organizing Committee:
3. Advisory Committee:
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Schedule
(06/23/2013)
time |
topic |
speaker |
type |
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10:15-10:45 |
CVPR break time |
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10:45-10:55 |
An INTRODUCTION (from a historical perspective) of the symmetry
detection competition |
Dr. Y.
Liu |
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10:55-11:10 |
A summary of the Reflection Symmetry Detection (submission and
evaluation) |
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11:10-11:25 |
Recognition of Symmetry Structure by Use of Gestalt Algebra |
E.
Michaelsen |
reflection |
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11:25-11:40 |
Detection of Mirror-Symmetric Image Patches |
V.
Patraucean |
reflection |
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11:40-11:55 |
Multi-Scale Kernel Operators for Reflection and Rotation
Symmetry |
A.
Petrosino |
reflection |
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12:00-13:30 |
CVPR lunch |
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13:30-13:40 |
Summary on Rotation Symmetry Detection (submission and
evaluation) |
Dr. S.
Lee |
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13:40-13:50 |
Multi-Scale Kernel Operators for Reflection and Rotation
Symmetry |
A.
Petrosino |
rotation |
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13:55-14:10 |
Recognition of Symmetry Structure by Use of Gestalt Algebra |
E.
Michaelsen |
translation |
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14:10-14:25 |
Translation Symmetry Detection: A Repetitive Pattern Analysis
Approach |
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translation |
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14:25-15:15 |
Keynote Speech:
Parallel model for the salience of mirror symmetry in natural
patterns |
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15:35-15:55 |
CVPR break time |
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15:55-16:00 |
Awards for the winners |
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16:00-17:00 |
PANEL discussion: What should be the
Gold-standard for evaluating "Symmetry Detection from Real Images"
algorithms in computer vision: human perception or mathematics?
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4. Results and Paper Submissions:
We
are releasing the training datasets containing images and hand-labeled ground
truth, representative of the types of symmetry that will be found in the test
set. After the release of the testing dataset (mid April), competitors are
required to submit results (with a same format as our ground truth) for
evaluation, along with an associated paper describing the algorithm(s) used. We
encourage new symmetry detection algorithm or evaluating published algorithms
on our benchmark dataset. A special issue for the competition results in the
Journal of Machine Vision and Applications is planned.
5. Important Dates:
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Feb.01 |
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Mar.01 |
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Apr.24 |
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May01 |
May07 |
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Workshop |
Jun.23 |
6. Important Links:
Call
for Real World Symmetry Images Contribution
First Symmetry
Detection Competition (CVPR 2011)
PSU Near-Regular Texture Database
Survey
paper:
--
Yanxi Liu and Hagit
Hel-Or and Craig S. Kaplan and Luc Van Gool, Computational
Symmetry in Computer Vision and Computer Graphics, Foundations and Trends
in Computer Graphics and Vision, Vol.5, Num.1-2, Pages 199, 2010. [pdf]
7. Data Download:
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groundtruth |
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8. Submission:
We invite all participants to
submit a 4 page workshop paper using the CVPR12 template to describe their algorithms
as well as their detection results on the testing dataset in the same format of
the groundtruth label we release for the training data.
W also need a normalized confidence
score (between 0 and 1) to be associated with each detected potential symmetry
patterns. We recommend participants to include detection results (>=3 per
image) even with low confidence scores so that we can get a more complete
precision/recall curve for evaluation.
Sponsors:
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