Relevant bibliographies by topics / Blue-noise Sampling

Academic literature on the topic 'Blue-noise Sampling'

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Published: 9 March 2023

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Journal articles on the topic "Blue-noise Sampling"

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Kailkhura, Bhavya, Jayaraman J. Thiagarajan, Peer-Timo Bremer, and Pramod K. Varshney. "Stair blue noise sampling." ACM Transactions on Graphics 35, no. 6 (November 11, 2016): 1–10. http://dx.doi.org/10.1145/2980179.2982435.

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Chen, Zhonggui, Zhan Yuan, Yi-King Choi, Ligang Liu, and Wenping Wang. "Variational Blue Noise Sampling." IEEE Transactions on Visualization and Computer Graphics 18, no. 10 (October 2012): 1784–96. http://dx.doi.org/10.1109/tvcg.2012.94.

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Qin, Hongxing, Yi Chen, Jinlong He, and Baoquan Chen. "Wasserstein Blue Noise Sampling." ACM Transactions on Graphics 36, no. 4 (July 20, 2017): 1. http://dx.doi.org/10.1145/3072959.3119910.

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Qin, Hongxing, Yi Chen, Jinlong He, and Baoquan Chen. "Wasserstein blue noise sampling." ACM Transactions on Graphics 36, no. 4 (July 20, 2017): 1. http://dx.doi.org/10.1145/3072959.3126841.

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Li, Hongwei, Li-Yi Wei, Pedro V. Sander, and Chi-Wing Fu. "Anisotropic blue noise sampling." ACM Transactions on Graphics 29, no. 6 (December 2010): 1–12. http://dx.doi.org/10.1145/1882261.1866189.

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Reinert, Bernhard, Tobias Ritschel, Hans-Peter Seidel, and Iliyan Georgiev. "Projective Blue-Noise Sampling." Computer Graphics Forum 35, no. 1 (August 20, 2015): 285–95. http://dx.doi.org/10.1111/cgf.12725.

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Qin, Hongxing, Yi Chen, Jinlong He, and Baoquan Chen. "Wasserstein Blue Noise Sampling." ACM Transactions on Graphics 36, no. 5 (October 17, 2017): 1–13. http://dx.doi.org/10.1145/3119910.

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Chen, Jiating, Xiaoyin Ge, Li-Yi Wei, Bin Wang, Yusu Wang, Huamin Wang, Yun Fei, Kang-Lai Qian, Jun-Hai Yong, and Wenping Wang. "Bilateral blue noise sampling." ACM Transactions on Graphics 32, no. 6 (November 2013): 1–11. http://dx.doi.org/10.1145/2508363.2508375.

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Ahmed, Abdalla G. M., Hélène Perrier, David Coeurjolly, Victor Ostromoukhov, Jianwei Guo, Dong-Ming Yan, Hui Huang, and Oliver Deussen. "Low-discrepancy blue noise sampling." ACM Transactions on Graphics 35, no. 6 (November 11, 2016): 1–13. http://dx.doi.org/10.1145/2980179.2980218.

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Wei, Li-Yi. "Multi-class blue noise sampling." ACM Transactions on Graphics 29, no. 4 (July 26, 2010): 1–8. http://dx.doi.org/10.1145/1778765.1778816.

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Dissertations / Theses on the topic "Blue-noise Sampling"

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Yuan, Zhan. "Multiphase implicit modeling and variational blue noise sampling." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/197095.

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This thesis investigates two fundamental problems in computer graphics including object modeling and sampling. In object modeling problems, implicit function is widely used. It has a wide range of applications in entertainment, engineering and medical imaging. A standard two-phase implicit function only represents the interior and exterior of a single object. To facilitate solid modeling of heterogeneous objects with multiple internal regions, object-space multiphase implicit functions are much desired. Multiphase implicit functions have much potential in modeling natural organisms, heterogeneous mechanical parts and anatomical atlases. In the first part of this thesis, we introduce a novel class of object-space multiphase implicit functions that are capable of accurately and compactly representing objects with multiple internal regions. Our proposed multiphase implicit functions facilitate true object-space geometric modeling of heterogeneous objects with non-manifold features. We present multiple methods to create object-space multiphase implicit functions from existing data, including meshes and segmented medical images. Our algorithms are inspired by machine learning algorithms for training multicategory max-margin classifiers. Comparisons demonstrate that our method achieves an error rate one order of magnitude smaller than alternative techniques. In the second part of this thesis we study another important problem, sampling, which is a core process for numerous graphics applications including rendering, non-photorealistic image stippling, imaging, and geometry processing. Among all the existing sampling algorithms, blue noise point sampling is especially popular because it can generate spatial uniform point distribution with no aliasing artifacts. We present a new and versatile variational framework for generating point distributions with high-quality blue noise characteristics while precisely adapting to given density functions. Different from previous approaches based on discrete settings of capacity-constrained Voronoi tessellation, we cast the blue noise sampling generation as a variational problem with continuous settings. Based on an accurate evaluation of the gradient of an energy function, an efficient optimization is developed which delivers significantly faster performance than the previous optimization-based methods. Our framework can easily be extended to generating blue noise point samples on manifold surfaces and for multi-class sampling. The optimization formulation also allows us to naturally deal with dynamic domains, such as deformable surfaces, and to yield blue noise samplings with temporal coherence. We present experimental results to validate the efficacy of our variational framework. A core step in our blue noise sampling algorithm is to compute the Voronoi diagram. This is a fundamental geometry structure which has numerous applications including computer animation, pattern recognition and so on. Efficient computation of Voronoi diagram is critical for improving the performance of these applications. Thus, we also study the problem of using the GPU to compute the generalized Voronoi diagram (GVD) for higher-order sites, such as line segments and curves. We propose an algorithm that can compute considerately more accurate GVD with much less memory than using the existing algorithms, with only moderate increase of the running time.
published_or_final_version
Computer Science
Doctoral
Doctor of Philosophy
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TELLES, PABLO VINICIUS FERREIRA. "MULTI-CLASS BLUE NOISE SAMPLING ON POLYGONAL SURFACES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24793@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
A amostragem de pontos por discos de Poisson preserva a uniformidade espacial e as propriedades de ruído azul do seu espectro de Fourier. Esse padrão de amostragem é bem popular por ser visualmente agradável o que favorece algumas aplicações. Diversos estudos se dedicam à amostragem de um único conjunto de pontos distribuídos por discos de Poisson, caracterizando uma única classe de pontos sobre domínios planares ou sobre domínios de superfícies poligonais. Uma recente técnica de amostragem sobre domínios planares estende esse método para múltiplas classes de maneira que cada classe de pontos e a união das classes sejam distribuídas por discos de Poisson. Nossa principal contribuição estende este método de amostragem em múltiplas classes sobre domínios planares para superfícies poligonais preservando a boa qualidade em cada classe e na união das classes. A independência entre os pontos de classes distintas permite ainda atributos independentes por classe e com isso apresentamos uma aplicação da distribuição de distintos objetos sobre superfícies.
Poisson disk sampling preserves the spatial uniformity and blue noise properties of Fourier spectrum. This sampling pattern is very popular for its high visual quality that favors some applications. Several works are dedicated to a single set of Poisson disk sampling characterizing one class of points on the plane or on polygonal surfaces. A recent sampling process on the plane extends this method to multiple classes such that each class as well as their union keep Poisson disk proprieties. Our main contribution extends this method of multi-class Poisson disk sampling on the plane to arbitrary polygonal surfaces preserving the good quality in each class and in the union of the classes. The independence between points of different classes also allows independent attributes for each class and thus we present an application to distribute different objects on surfaces.
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LANARO, MATTEO PAOLO. "TOWARDS A COMPUTATIONAL MODEL OF RETINAL STRUCTURE AND BEHAVIOR." Doctoral thesis, Università degli Studi di Milano, 2020. http://hdl.handle.net/2434/710774.

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Human vision is our most important sensory system, allowing us to perceive our surroundings. It is an extremely complex process that starts with light entering the eye and ends inside of the brain, with most of its mechanisms still to be explained. When we observe a scene, the optics of the eye focus an image on the retina, where light signals are processed and sent all the way to the visual cortex of the brain, enabling our visual sensation. The progress of retinal research, especially on the topography of photoreceptors, is often tied to the progress of retinal imaging systems. The latest adaptive optics techniques have been essential for the study of the photoreceptors and their spatial characteristics, leading to discoveries that challenge the existing theories on color sensation. The organization of the retina is associated with various perceptive phenomena, some of them are straightforward and strictly related to visual performance like visual acuity or contrast sensitivity, but some of them are more difficult to analyze and test and can be related to the submosaics of the three classes of cone photoreceptors, like how the huge interpersonal differences between the ratio of different cone classes result in negligible differences in color sensation, suggesting the presence of compensation mechanisms in some stage of the visual system. In this dissertation will be discussed and addressed issues regarding the spatial organization of the photoreceptors in the human retina. A computational model has been developed, organized into a modular pipeline of extensible methods each simulating a different stage of visual processing. It does so by creating a model of spatial distribution of cones inside of a retina, then applying descriptive statistics for each photoreceptor to contribute to the creation of a graphical representation, based on a behavioral model that determines the absorption of photoreceptors. These apparent color stimuli are reconstructed in a representation of the observed scene. The model allows the testing of different parameters regulating the photoreceptor's topography, in order to formulate hypothesis on the perceptual differences arising from variations in spatial organization.
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Perrier, Hélène. "Anti-Aliased Low Discrepancy Samplers for Monte Carlo Estimators in Physically Based Rendering." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE1040/document.

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Lorsque l'on affiche un objet 3D sur un écran d'ordinateur, on transforme cet objet en une image, c.a.d en un ensemble de pixels colorés. On appelle Rendu la discipline qui consiste à trouver la couleur à associer à ces pixels. Calculer la couleur d'un pixel revient à intégrer la quantité de lumière arrivant de toutes les directions que la surface renvoie dans la direction du plan image, le tout pondéré par une fonction binaire déterminant si un point est visible ou non. Malheureusement, l'ordinateur ne sait pas calculer des intégrales on a donc deux méthodes possibles : Trouver une expression analytique qui permet de supprimer l'intégrale de l'équation (approche basée statistique). Approximer numériquement l'équation en tirant des échantillons aléatoires dans le domaine d'intégration et en en déduisant la valeur de l'intégrale via des méthodes dites de Monte Carlo. Nous nous sommes ici intéressés à l'intégration numérique et à la théorie de l'échantillonnage. L'échantillonnage est au cœur des problématiques d'intégration numérique. En informatique graphique, il est capital qu'un échantillonneur génère des points uniformément dans le domaine d’échantillonnage pour garantir que l'intégration ne sera pas biaisée. Il faut également que le groupe de points généré ne présente aucune régularité structurelle visible, au risque de voir apparaître des artefacts dit d'aliassage dans l'image résultante. De plus, les groupes de points générés doivent minimiser la variance lors de l'intégration pour converger au plus vite vers le résultat. Il existe de nombreux types d'échantillonneurs que nous classeront ici grossièrement en 2 grandes familles : Les échantillonneurs bruit bleu, qui ont une faible la variance lors de l'intégration tout en générant de groupes de points non structurés. Le défaut de ces échantillonneurs est qu'ils sont extrêmement lents pour générer les points. Les échantillonneurs basse discrépance, qui minimisent la variance lors de l'intégration, génèrent des points extrêmement vite, mais qui présentent une forte structure, générant énormément d'aliassage. Notre travail a été de développer des échantillonneurs hybrides, combinant à la fois bruit bleu et basse discrépance
When you display a 3D object on a computer screen, we transform this 3D scene into a 2D image, which is a set of organized colored pixels. We call Rendering all the process that aims at finding the correct color to give those pixels. This is done by integrating all the light rays coming for every directions that the object's surface reflects back to the pixel, the whole being ponderated by a visibility function. Unfortunately, a computer can not compute an integrand. We therefore have two possibilities to solve this issue: We find an analytical expression to remove the integrand (statistic based strategy). Numerically approximate the equation by taking random samples in the integration domain and approximating the integrand value using Monte Carlo methods. Here we focused on numerical integration and sampling theory. Sampling is a fundamental part of numerical integration. A good sampler should generate points that cover the domain uniformly to prevent bias in the integration and, when used in Computer Graphics, the point set should not present any visible structure, otherwise this structure will appear as artifacts in the resulting image. Furthermore, a stochastic sampler should minimize the variance in integration to converge to a correct approximation using as few samples as possible. There exists many different samplers that we will regroup into two families: Blue Noise samplers, that have a low integration variance while generating unstructured point sets. The issue with those samplers is that they are often slow to generate a pointset. Low Discrepancy samplers, that minimize the variance in integration and are able to generate and enrich a point set very quickly. However, they present a lot of structural artifacts when used in Rendering. Our work aimed at developing hybriod samplers, that are both Blue Noise and Low Discrepancy
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Lebrat, Léo. "Projection au sens de Wasserstein 2 sur des espaces structurés de mesures." Thesis, Toulouse, INSA, 2019. http://www.theses.fr/2019ISAT0035.

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Cette thèse s’intéresse à l’approximation pour la métrique de 2-Wasserstein de mesures de probabilité par une mesure structurée. Les mesures structurées étudiées sont des discrétisations consistantes de mesures portées par des courbes continues à vitesse et à accélération bornées. Nous comparons deux types d’approximations pour ces courbes continues : l’approximation constante par morceaux et linéaire par morceaux. Pour chaque méthode, des algorithmes rapides et fonctionnant pour une discrétisation fine ont été développés. Le problème d’approximation se divise en deux étapes avec leurs propres défis théoriques et algorithmiques : le calcul de la distance de Wasserstein 2 et son optimisation par rapport aux paramètres de structure. Ce travail est initialement motivé par la génération de trajectoires d’IRM en acquisition compressée, toutefois nous donnons de nouvelles applications potentielles pour ces méthodes
This thesis focuses on the approximation for the 2-Wasserstein metric of probability measures by structured measures. The set of structured measures under consideration is made of consistent discretizations of measures carried by a smooth curve with a bounded speed and acceleration. We compare two different types of approximations of the curve: piecewise constant and piecewise linear. For each of these methods, we develop fast and scalable algorithms to compute the 2-Wasserstein distance between a given measure and the structured measure. The optimization procedure reveals new theoretical and numerical challenges, it consists of two steps: first the computation of the 2-Wasserstein distance, second the optimization of the parameters of structure. This work is initially motivated by the design of trajectories in MRI acquisition, however we provide new applications of these methods
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Wu, Yi-Chian, and 吳宜倩. "Generating Pointillism Paintings Using Multi-Class Blue Noise Sampling Based on Seurat's Color Composition." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/67910894761664349305.

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碩士
國立交通大學
多媒體工程研究所
100
In this thesis, we propose a new stippling technique, using a simple and intuitive concept to convert a color image into a pointillism painting. First, we collect, analyze, and imitate the color composition structure from Seurat‘s paintings. We further infer more color compositions, which do not contain in the reference painting, and include them in our color statistical model. Then, we use the modified multi-class blue noise sampling to distribute color points by looking up the color statistical model to imitate Seurat’s color composition. The blue noise property ensures that the color points are randomly located but remain spatially uniform. In our experiments, we use the multivariate goodness-of-fit tests to analyze our and other previous research’s results, comparing the color composition of each segmentation region to Seurat’s, and confirming that the color compositions of our results are most similar to Seurat’s painting habit.
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Conference papers on the topic "Blue-noise Sampling"

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Georgiev, Iliyan, and Marcos Fajardo. "Blue-noise dithered sampling." In SIGGRAPH '16: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2897839.2927430.

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Li, Hongwei, Li-Yi Wei, Pedro V. Sander, and Chi-Wing Fu. "Anisotropic blue noise sampling." In ACM SIGGRAPH Asia 2010 papers. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1882262.1866189.

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Wei, Li-Yi. "Multi-class blue noise sampling." In ACM SIGGRAPH 2010 papers. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1833349.1778816.

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Zhao, Jiaojiao, Jie Feng, and Bingfeng Zhou. "Image vectorization using blue-noise sampling." In IS&T/SPIE Electronic Imaging, edited by Qian Lin, Jan P. Allebach, Zhigang Fan, and Jerry Liu. SPIE, 2013. http://dx.doi.org/10.1117/12.2009412.

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Parada-Mayorga, Alejandro, Daniel L. Lau, Jhony H. Giraldo, and Gonzalo R. Arce. "Blue-Noise Sampling of Signals on Graphs." In 2019 13th International conference on Sampling Theory and Applications (SampTA). IEEE, 2019. http://dx.doi.org/10.1109/sampta45681.2019.9030829.

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Dapena, Daniela, Daniel L. Lau, and Gonzalo R. Arce. "Density Aware Blue-Noise Sampling on Graphs." In 2022 30th European Signal Processing Conference (EUSIPCO). IEEE, 2022. http://dx.doi.org/10.23919/eusipco55093.2022.9909671.

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Fattal, Raanan. "Blue-noise point sampling using kernel density model." In ACM SIGGRAPH 2011 papers. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1964921.1964943.

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Parada-Mayorga, Alejandro, Daniel L. Lau, Jhony H. Giraldo, and Gonzalo R. Arce. "Sampling of Graph Signals with Blue Noise Dithering." In 2019 IEEE Data Science Workshop (DSW). IEEE, 2019. http://dx.doi.org/10.1109/dsw.2019.8755603.

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Zheng, Xiuyu, Junjun Si, and Shuaifu Dai. "Blue noise sampling with a PBF-based method." In CGI '16: Computer Graphics International. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2949035.2949055.

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Ostromoukhov, Victor, Charles Donohue, and Pierre-Marc Jodoin. "Fast hierarchical importance sampling with blue noise properties." In ACM SIGGRAPH 2004 Papers. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1186562.1015750.

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