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Computational Symmetry in Computer Vision and Computer Graphics

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TLDR
Recognizing the fundamental relevance and group theory of symmetry has the potential to play an important role in computational sciences.
Abstract
In the arts and sciences, as well as in our daily lives, symmetry has made a profound and lasting impact. Likewise, a computational treatment of symmetry and group theory (the ultimate mathematical formalization of symmetry) has the potential to play an important role in computational sciences. Though the term Computational Symmetry was formally defined a decade ago by the first author, referring to algorithmic treatment of symmetries, seeking symmetry from digital data has been attempted for over four decades. Computational symmetry on real world data turns out to be challenging enough that, after decades of effort, a fully automated symmetry-savvy system remains elusive for real world applications. The recent resurging interests in computational symmetry for computer vision and computer graphics applications have shown promising results. Recognizing the fundamental relevance and potential power that computational symmetry affords, we offer this survey to the computer vision and computer graphics communities. This survey provides a succinct summary of the relevant mathematical theory, a historic perspective of some important symmetry-related ideas, a partial yet timely report on the state of the arts symmetry detection algorithms along with its first quantitative benchmark, a diverse set of real world applications, suggestions for future directions and a comprehensive reference list.

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The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

TL;DR: In this paper, the authors offer a new book that enPDFd the perception of the visual world to read, which they call "Let's Read". But they do not discuss how to read it.
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Image completion using planar structure guidance

TL;DR: This work proposes a method for automatically guiding patch-based image completion using mid-level structural cues by first estimates planar projection parameters, softly segments the known region into planes, and discovers translational regularity within these planes.
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TILT: Transform Invariant Low-Rank Textures

TL;DR: This method can accurately recover both the intrinsic low-rank texture and the unknown transformation, and hence both the geometry and appearance of the associated planar region in 3D in the case of planar regions with significant affine or projective deformation.
References
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Proceedings ArticleDOI

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