Low-discrepancy blue noise sampling

Ahmed, Abdalla G. M.; Perrier, Hélène; Cœurjolly, David; Ostromoukhov, Victor; Guo, Jianwei; Yan, Dong‐Ming; Huang, Hui; Deußen, Oliver

doi:10.1145/2980179.2980218

Cited by 38 publications

(21 citation statements)

References 57 publications

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“…A coarse-fine search matching algorithm, namely lowdifference sequence sampling image matching method was selected in the matching process, whose details can be found in reference [29]. Once subset area was matched by maximizing the correlation coefficient in the searching area (defined by the step size and the subset size), the displacement field was calculated according to the difference of the location, which can be described as:…”

Section: Image Processing and Strain Field Calculationmentioning

confidence: 99%

A method of crack detection based on digital image correlation for simulated cracked tooth

Zhang

Guo

et al. 2021

BMC Oral Health

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Background Early clinical cracked tooth can be a perplexing disorder to diagnose and manage. One of the key problems for the diagnosis of the cracked tooth is the detection of the location of the surface crack. Methods This paper proposes an image-based method for the detection of the micro-crack in the simulated cracked tooth. A homemade three-axis motion platform mounted with a telecentric lens was built as an image acquisition system to observe the surface of the simulated cracked tooth, which was under compression with a magnitude of the masticatory force. By using digital image correlation (DIC), the deformation map for the crown surface of the cracked tooth was calculated. Through image analysis, the micro-crack was quantitatively visualized and characterized. Results The skeleton of the crack path was successfully extracted from the image of the principal strain field, which was further verified by the image from micro-CT. Based on crack kinematics, the crack opening displacement was quantitatively calculated to be 2–10 µm under the normal mastication stress, which was in good agreement with the value reported in the literature. Conclusions The crack on the surface of the simulated cracked tooth could be detected based on the proposed DIC-based method. The proposed method may provide a new solution for the rapid clinical diagnosis of cracked teeth and the calculated crack information would be helpful for the subsequent clinical treatment of cracked teeth.

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Section: Image Processing and Strain Field Calculationmentioning

confidence: 99%

A method of crack detection based on digital image correlation for simulated cracked tooth

Zhang

Guo

et al. 2021

BMC Oral Health

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“…A large variety of optimization-based approaches has been proposed since then. Two main optimization-based approaches have been developed and presented in numerous papers: (1) on-line optimization [20, 27, 24, (2) off-line optimization [44][45][46][47][48][49], where the near-optimal solution is prepared in form of lookup tables, used in runtime. The present work uses the approach developed by de Goes et al [39], called Blue Noise Through Optimal Transport (BNOT), as reference.…”

Section: Blue Noise Distributionsmentioning

confidence: 99%

Blue-noise sampling for human retinal cone spatial distribution modeling

Lanaro¹,

Perrier²,

Cœurjolly³

et al. 2020

J. Phys. Commun.

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This paper proposes a novel method for modeling retinal cone distribution in humans. It is based on Blue-noise sampling algorithms being strongly related with the mosaic sampling performed by cone photoreceptors in the human retina. Here we present the method together with a series of examples of various real retinal patches. The same samples have also been created with alternative algorithms and compared with plots of the center of the inner segments of cone photoreceptors from imaged retinas. Results are evaluated with different distance measure used in the field, like nearest-neighbor analysis and pair correlation function. The proposed method can effectively describe features of a human retinal cone distribution by allowing to create samples similar to the available data. For this reason, we believe that the proposed algorithm may be a promising solution when modeling local patches of retina.

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“…Indeed, PBRT, the celebrated rendering textbook, eventually dropped its Poisson-disk "best-candidate" sampler [Mitchell 1991] in favor of the more efficient low-discrepancy samplers. There were also recent attempts to combine the blue-noise frequency spectrum with the low-discrepancy distribution [Ahmed et al 2016;Perrier et al 2018].…”

Section: Low-discrepancy Samplingmentioning

confidence: 99%

Screen-space blue-noise diffusion of monte carlo sampling error via hierarchical ordering of pixels

Ahmed

Wonka

2020

ACM Trans. Graph.

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We present a novel technique for diffusing Monte Carlo sampling error as a blue noise in screen space. We show that automatic diffusion of sampling error can be achieved by ordering the pixels in a way that preserves locality, such as Morton's Z-ordering, and assigning the samples to the pixels from successive sub-sequences of a single low-discrepancy sequence, thus securing well-distributed samples for each pixel, local neighborhoods, and the whole image. We further show that a blue-noise distribution of the error is attainable by scrambling the Z-ordering to induce isotropy. We present an efficient technique to implement this hierarchical scrambling by defining a context-free grammar that describes infinite self-similar lookup trees. Our concept is scalable to arbitrary image resolutions, sample dimensions, and sample count, and supports progressive and adaptive sampling.

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Low-discrepancy blue noise sampling

Cited by 38 publications

References 57 publications

A method of crack detection based on digital image correlation for simulated cracked tooth

A method of crack detection based on digital image correlation for simulated cracked tooth

Blue-noise sampling for human retinal cone spatial distribution modeling

Screen-space blue-noise diffusion of monte carlo sampling error via hierarchical ordering of pixels

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