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    <title>Pinboard (Vaguery)</title>
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    <description>recent bookmarks from Vaguery</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://arxiv.org/abs/2307.15584"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1406.7025"/>
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  </channel><item rdf:about="https://arxiv.org/abs/2307.15584">
    <title>[2307.15584] Quasi-Monte Carlo Algorithms (not only) for Graphics Software</title>
    <dc:date>2023-09-09T12:58:09+00:00</dc:date>
    <link>https://arxiv.org/abs/2307.15584</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Quasi-Monte Carlo methods have become the industry standard in computer graphics. For that purpose, efficient algorithms for low discrepancy sequences are discussed. In addition, numerical pitfalls encountered in practice are revealed. We then take a look at massively parallel quasi-Monte Carlo integro-approximation for image synthesis by light transport simulation. Beyond superior uniformity, low discrepancy points may be optimized with respect to additional criteria, such as noise characteristics at low sampling rates or the quality of low-dimensional projections.
]]></description>
<dc:subject>low-discrepancy-numbers algorithms numerical-methods computer-graphics sampling rather-interesting performance-measure to-write-about to-cite</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3865da3252e2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:low-discrepancy-numbers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computer-graphics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sampling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
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<item rdf:about="http://arxiv.org/abs/1406.7025">
    <title>[1406.7025] DASS: Detail Aware Sketch-Based Surface Modeling</title>
    <dc:date>2015-09-23T21:50:16+00:00</dc:date>
    <link>http://arxiv.org/abs/1406.7025</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present a sketch-based modeling system suitable for detail editing, based on a multilevel representation for surfaces. The main advantage of this representation allowing for the control of local (details) and global changes of the model. We used an adaptive mesh (4-8 mesh) and developed a label theory to construct a manifold structure, which is responsible for controlling local editing of the model. The overall shape and global modifications are defined by a variational implicit surface (Hermite RBF). Our system assembles the manifold structures to allow the user to add details without changing the overall shape, as well as edit the overall shape while repositioning details coherently.
]]></description>
<dc:subject>finite-elements optimization modeling modeling-is-not-mathematics computer-graphics performance-measure nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5c88f9716cac/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:finite-elements"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling-is-not-mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computer-graphics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1411.1668">
    <title>[1411.1668] On Chord and Sagitta in ${mathbb Z}^2$: An Analysis towards Fast and Robust Circular Arc Detection</title>
    <dc:date>2014-11-16T11:33:01+00:00</dc:date>
    <link>http://arxiv.org/abs/1411.1668</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Although chord and sagitta, when considered in tandem, may reflect many underlying geometric properties of circles on the Euclidean plane, their implications on the digital plane are not yet well-understood. In this paper, we explore some of their fundamental properties on the digital plane that have a strong bearing on the unsupervised detection of circles and circular arcs in a digital image. We show that although the chord-and-sagitta properties of a real circle do not readily migrate to the digital plane, they can indeed be used for the analysis in the discrete domain based on certain bounds on their deviations, which are derived from the real domain. In particular, we derive an upper bound on the circumferential angular deviation of a point in the context of chord property, and an upper bound on the relative error in radius estimation with regard to the sagitta property. Using these two bounds, we design a novel algorithm for the detection and parameterization of circles and circular arcs, which does not require any heuristic initialization or manual tuning. The chord property is deployed for the detection of circular arcs, whereas the sagitta property is used to estimate their centers and radii. Finally, to improve the accuracy of estimation, the notion of restricted Hough transform is used. Experimental results demonstrate superior efficiency and robustness of the proposed methodology compared to existing techniques.
]]></description>
<dc:subject>computer-graphics approximation geometry rather-interesting image-processing discretization experiment nudge-targets classification consider:learning-what's-meant-not-shown</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:089989813c45/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computer-graphics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:discretization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:learning-what's-meant-not-shown"/>
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<item rdf:about="http://arxiv.org/abs/1006.4327">
    <title>[1006.4327] On computing B\'ezier curves by Pascal matrix methods</title>
    <dc:date>2010-06-28T23:31:21+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.4327</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The main goal of the paper is to introduce methods which compute B\'ezier curves faster than Casteljau's method does. These methods are based on the spectral factorization of a $n\times n$ Bernstein matrix, $B^e_n(s)= P_nG_n(s)P_n^{-1}$, where $P_n$ is the $n\times n$ lower triangular Pascal matrix. So we first calculate the exact optimum positive value $t$ in order to transform $P_n$ in a scaled Toeplitz matrix, which is a problem that was partially solved by X. Wang and J. Zhou (2006). Then fast Pascal matrix-vector multiplications and strategies of polynomial evaluation are put together to compute B\'ezier curves. Nevertheless, when $n$ increases, more precise Pascal matrix-vector multiplications allied to affine transformations of the vectors of coordinates of the control points of the curve are then necessary to stabilize all the computation."
]]></description>
<dc:subject>nudge-targets algorithms numerical-methods computer-graphics Bezier-curves computational-complexity</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fa28a8906050/</dc:identifier>
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