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Journal articles on the topic 'Adaptive multiresolution'

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1

Domingues, Margarete O., Sônia M. Gomes, Olivier Roussel, and Kai Schneider. "Adaptive multiresolution methods." ESAIM: Proceedings 34 (December 2011): 1–96. http://dx.doi.org/10.1051/proc/201134001.

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2

Meer, P., R. H. Park, and K. J. Cho. "Multiresolution Adaptive Image Smoothing." CVGIP: Graphical Models and Image Processing 56, no. 2 (March 1994): 140–48. http://dx.doi.org/10.1006/cgip.1994.1013.

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3

LIVNY, YOTAM, NETA SOKOLOVSKY, and JIHAD EL-SANA. "DUAL ADAPTIVE PATHS FOR MULTIRESOLUTION HIERARCHIES." International Journal of Image and Graphics 07, no. 02 (April 2007): 273–90. http://dx.doi.org/10.1142/s0219467807002726.

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The recent increase in the generated polygonal dataset sizes has outpaced the performance of graphics hardware. Several solutions such as multiresolution hierarchies and level-of-detail rendering have been developed to bridge the increasing gap. However, the discrete levels of detail generate annoying popping effects, the preliminaries multiresolution schemes cannot perform drastic changes on the selected level of detail within the span of small number of frames, and the current cluster-based hierarchies suffer from the high-detailed representation of the boundaries between clusters. In this paper, we are presenting a novel approach for multiresolution hierarchy that supports dual paths for run-time adaptive simplification — fine and coarse. The proposed multiresolution hierarchy is based on the fan-merge operator and its reverse operator fan-split. The coarse simplification path is achieved by directly applying fan-merge/split, while the fine simplification route is performed by executing edge-collapse/vertex-split one at a time. The sequence of the edge-collapses/vertex-splits is encoded implicitly by the order of the children participating in the fan-merge/split operator. We shall refer to this multiresolution hierarchy as fan-hierarchy. Fan-hierarchy provides a compact data structure for multiresolution hierarchy, since it stores 7/6 pointers, on the average, instead of 3 pointers for each node. In addition, the resulting depth of the fan-hierarchy is usually smaller than the depth of hierarchies generated by edge-collapse based multiresolution schemes. It is also important to note that fan-hierarchy inherently utilizes fan representation for further acceleration of the rendering process.
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4

Sturani, R., and R. Terenzi. "Adaptive multiresolution for wavelet analysis." Journal of Physics: Conference Series 122 (July 1, 2008): 012036. http://dx.doi.org/10.1088/1742-6596/122/1/012036.

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5

Biyikli, Emre, and Albert C. To. "Multiresolution molecular mechanics: Adaptive analysis." Computer Methods in Applied Mechanics and Engineering 305 (June 2016): 682–702. http://dx.doi.org/10.1016/j.cma.2016.02.038.

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6

Tsukanov, I., and V. Shapiro. "Adaptive multiresolution refinement with distance fields." International Journal for Numerical Methods in Engineering 72, no. 11 (2007): 1355–86. http://dx.doi.org/10.1002/nme.2087.

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7

Harten, Ami. "Adaptive Multiresolution Schemes for Shock Computations." Journal of Computational Physics 115, no. 2 (December 1994): 319–38. http://dx.doi.org/10.1006/jcph.1994.1199.

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8

Peifer, Maria, Luiz F. O. Chamon, Santiago Paternain, and Alejandro Ribeiro. "Sparse Multiresolution Representations With Adaptive Kernels." IEEE Transactions on Signal Processing 68 (2020): 2031–44. http://dx.doi.org/10.1109/tsp.2020.2976577.

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9

von Tycowicz, Christoph, Felix Kälberer, and Konrad Polthier. "Context-Based Coding of Adaptive Multiresolution Meshes." Computer Graphics Forum 30, no. 8 (August 1, 2011): 2231–45. http://dx.doi.org/10.1111/j.1467-8659.2011.01972.x.

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10

Saatchi, M. R., E. M. Allen, J. W. K. Rowe, and C. Gibson. "Adaptive multiresolution analysis based evoked potential filtering." IEE Proceedings - Science, Measurement and Technology 144, no. 4 (July 1, 1997): 149–55. http://dx.doi.org/10.1049/ip-smt:19971319.

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11

Han, Bin, Gitta Kutyniok, and Zuowei Shen. "Adaptive Multiresolution Analysis Structures and Shearlet Systems." SIAM Journal on Numerical Analysis 49, no. 5 (January 2011): 1921–46. http://dx.doi.org/10.1137/090780912.

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12

Poston, Tim, Tien-Tsin Wong, and Pheng-Ann Heng. "Multiresolution Isosurface Extraction with Adaptive Skeleton Climbing." Computer Graphics Forum 17, no. 3 (August 1998): 137–47. http://dx.doi.org/10.1111/1467-8659.00261.

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13

Cohen, Albert, Nira Dyn, Frédéric Hecht, and Jean-Marie Mirebeau. "Adaptive multiresolution analysis based on anisotropic triangulations." Mathematics of Computation 81, no. 278 (September 28, 2011): 789–810. http://dx.doi.org/10.1090/s0025-5718-2011-02495-6.

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14

Nichols, Greg, and Chris Wyman. "Interactive Indirect Illumination Using Adaptive Multiresolution Splatting." IEEE Transactions on Visualization and Computer Graphics 16, no. 5 (September 2010): 729–41. http://dx.doi.org/10.1109/tvcg.2009.97.

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15

Bendahmane, Mostafa, Raimund Bürger, Ricardo Ruiz, and Kai Schneider. "Adaptive multiresolution schemes for reaction-diffusion systems." PAMM 8, no. 1 (December 2008): 10969–70. http://dx.doi.org/10.1002/pamm.200810969.

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16

Thuillard, Marc. "Adaptive multiresolution and wavelet-based search methods." International Journal of Intelligent Systems 19, no. 4 (2004): 303–13. http://dx.doi.org/10.1002/int.10164.

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17

Ohlberger, M., and M. Rumpf. "Adaptive projection operators in multiresolution scientific visualization." IEEE Transactions on Visualization and Computer Graphics 5, no. 1 (1999): 74–94. http://dx.doi.org/10.1109/2945.764874.

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18

Ohlberger, M., and M. Rumpf. "Adaptive projection operators in multiresolution scientific visualization." IEEE Transactions on Visualization and Computer Graphics 4, no. 4 (1998): 344–64. http://dx.doi.org/10.1109/2945.765328.

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19

Maximo, Andre, Luiz Velho, and Marcelo Siqueira. "Adaptive multi-chart and multiresolution mesh representation." Computers & Graphics 38 (February 2014): 332–40. http://dx.doi.org/10.1016/j.cag.2013.11.013.

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20

Öllös, Gergely, and Rolland Vida. "Adaptive multiresolution sampling in event-driven WSNs." Telecommunication Systems 61, no. 2 (March 17, 2015): 337–47. http://dx.doi.org/10.1007/s11235-015-0005-x.

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21

Santos, J. C., P. Cruz, M. A. Alves, P. J. Oliveira, F. D. Magalhães, and A. Mendes. "Adaptive multiresolution approach for two-dimensional PDEs." Computer Methods in Applied Mechanics and Engineering 193, no. 3-5 (January 2004): 405–25. http://dx.doi.org/10.1016/j.cma.2003.10.005.

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22

He, Yu Min, Xi Chen, and Xiao Long Zhang. "Construction of the Beam Element Based Second Generation Wavelet." Applied Mechanics and Materials 80-81 (July 2011): 532–35. http://dx.doi.org/10.4028/www.scientific.net/amm.80-81.532.

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Second generation wavelet (SGW) provides diversity and agility for constructing wavelet besides the multiresolution property. By introducing SGW into finite element method, a series sequence of finite element approximation spaces which are nested and hierarchically expanded can be constructed. The method has high calculation speed and precision and is suited for constructing adaptive algorithm. In this paper, the beam element based SEW is constructed, which set up a basis for the adaptive finite element method based on multiresolution analysis.
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23

Amat, Sergio, Rosa Donat, Jacques Liandrat, and J. Carlos Trillo. "A fully adaptive multiresolution scheme for image processing." Mathematical and Computer Modelling 46, no. 1-2 (July 2007): 2–11. http://dx.doi.org/10.1016/j.mcm.2006.12.003.

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24

To, Danny, Rynson W. H. Lau, and Mark Green. "An Adaptive Multiresolution Method for Progressive Model Transmission." Presence: Teleoperators and Virtual Environments 10, no. 1 (February 2001): 62–74. http://dx.doi.org/10.1162/105474601750182324.

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Although there are many adaptive (or view-dependent) multiresolution methods, support for progressive transmission and reconstruction has not been addressed. A major reason for this is that most of these methods require a large portion of the hierarchical data structure to be available at the client before rendering starts. This is due to the dependency constraints among neighboring vertices. In this paper, we present an efficient, adaptive, multiresolution method that allows progressive and selective model transmission. It is achieved by reducing the neighboring dependency to a minimum. The new method allows visually important parts of an object to be transmitted to the client at higher priority than the less important parts, and progressively reconstructed there for display. It is even possible to transmit only the visible parts of a model and reconstruct these visible parts at the client. The ability to selectively transmit allows the visualization of very large models across the network with minimal delay. We will present how our method works in a client-server environment. We will also show the data structure of the transmission record and some performance results of the method.
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25

Coupé, P., J. V. Manjón, M. Robles, and D. L. Collins. "Adaptive Multiresolution Denoising Filter for 3D MR Images." NeuroImage 47 (July 2009): S50. http://dx.doi.org/10.1016/s1053-8119(09)70119-3.

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26

Alves, M. A., P. Cruz, A. Mendes, F. D. Magalhães, F. T. Pinho, and P. J. Oliveira. "Adaptive multiresolution approach for solution of hyperbolic PDEs." Computer Methods in Applied Mechanics and Engineering 191, no. 36 (August 2002): 3909–28. http://dx.doi.org/10.1016/s0045-7825(02)00334-1.

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27

Palmerio, Bernadette, and Alain Dervieux. "Multimesh and multiresolution analysis for mesh adaptive interpolation." Applied Numerical Mathematics 22, no. 4 (December 1996): 477–93. http://dx.doi.org/10.1016/s0168-9274(96)00050-5.

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28

Kutyniok, Gitta, and Tomas Sauer. "Adaptive Directional Subdivision Schemes and Shearlet Multiresolution Analysis." SIAM Journal on Mathematical Analysis 41, no. 4 (January 2009): 1436–71. http://dx.doi.org/10.1137/08072276x.

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29

Hovhannisyan, Nune, Siegfried Müller, and Roland Schäfer. "Adaptive multiresolution discontinuous Galerkin schemes for conservation laws." Mathematics of Computation 83, no. 285 (July 10, 2013): 113–51. http://dx.doi.org/10.1090/s0025-5718-2013-02732-9.

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30

Liao, Honghong, Jinhai Xiang, Weiping Sun, and Shengsheng Yu. "Adaptive Aggregating Multiresolution Feature Coding for Image Classification." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/847608.

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The Bag of Visual Words (BoW) model is one of the most popular and effective image classification frameworks in the recent literature. The optimal formation of a visual vocabulary remains unclear, and the size of the vocabulary also affects the performance of image classification. Empirically, larger vocabulary leads to higher classification accuracy. However, larger vocabulary needs more memory and intensive computational resources. In this paper, we propose a multiresolution feature coding (MFC) framework via aggregating feature codings obtained from a set of small visual vocabularies with different sizes, where each vocabulary is obtained by a clustering algorithm, and different clustering algorithm discovers different aspect of image features. In MFC, feature codings from different visual vocabularies are aggregated adaptively by a modified Online Passive-Aggressive Algorithm under the histogram intersection kernel, which lead to a closed-form solution. Experiments demonstrate that the proposed method (1) obtains the same if not higher classification accuracy than the BoW model with a large visual vocabulary; and (2) needs much less memory and computational resources.
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31

Yi, Zonggen, and Peter H. Bauer. "Adaptive Multiresolution Energy Consumption Prediction for Electric Vehicles." IEEE Transactions on Vehicular Technology 66, no. 11 (November 2017): 10515–25. http://dx.doi.org/10.1109/tvt.2017.2720587.

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32

Alkishriwo, Osama A. S. "Image compression using adaptive multiresolution image decomposition algorithm." IET Image Processing 14, no. 14 (December 1, 2020): 3572–78. http://dx.doi.org/10.1049/iet-ipr.2019.1699.

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33

Thuillard, Marc. "Adaptive multiresolution search: How to beat brute force?" International Journal of Approximate Reasoning 35, no. 3 (March 2004): 223–38. http://dx.doi.org/10.1016/j.ijar.2003.08.003.

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34

Bihari, Barna L., and Ami Harten. "Application of generalized wavelets: An adaptive multiresolution scheme." Journal of Computational and Applied Mathematics 61, no. 3 (August 1995): 275–321. http://dx.doi.org/10.1016/0377-0427(94)00070-1.

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35

Sendov, Bl. "Adaptive Multiresolution Analysis on the Dyadic Topological Group." Journal of Approximation Theory 96, no. 2 (February 1999): 258–80. http://dx.doi.org/10.1006/jath.1998.3234.

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36

KARAMI, A., H. R. KARIMI, B. MOSHIRI, and P. JABEDAR MARALANI. "WAVELET-BASED ADAPTIVE COLLOCATION METHOD FOR THE RESOLUTION OF NONLINEAR PDEs." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 06 (November 2007): 957–73. http://dx.doi.org/10.1142/s0219691307002154.

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Theoretical modeling of dynamic processes in chemical engineering often implies the numeric solution of one or more partial differential equations. The complexity of such problems is increased when the solutions exhibit sharp moving fronts. An efficient adaptive multiresolution numerical method is described for solving systems of partial differential equations. This method is based on multiresolution analysis and interpolating wavelets, that dynamically adapts the collocation grid so that higher resolution is automatically attributed to domain regions where sharp features are present. Space derivatives were computed in an irregular grid by cubic splines method. The effectiveness of the method is demonstrated with some relevant examples in a chemical engineering context.
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37

Beylkin, Gregory, Lucas Monzón, and Xinshuo Yang. "Adaptive algorithm for electronic structure calculations using reduction of Gaussian mixtures." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2226 (June 2019): 20180901. http://dx.doi.org/10.1098/rspa.2018.0901.

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We present a new adaptive method for electronic structure calculations based on novel fast algorithms for reduction of multivariate mixtures. In our calculations, spatial orbitals are maintained as Gaussian mixtures whose terms are selected in the process of solving equations. Using a fixed basis leads to the so-called basis error since orbitals may not lie entirely within the linear span of the basis. To avoid such an error, multiresolution bases are used in adaptive algorithms so that basis functions are selected from a fixed collection of functions, large enough to approximate solutions within any user-selected accuracy. Our new method achieves adaptivity without using a multiresolution basis. Instead, as part of an iteration to solve nonlinear equations, our algorithm selects the ‘best’ subset of linearly independent terms of a Gaussian mixture from a collection that is much larger than any possible basis since the locations and shapes of the Gaussian terms are not fixed in advance. Approximating an orbital within a given accuracy, our algorithm yields significantly fewer terms than methods using multiresolution bases. We demonstrate our approach by solving the Hartree–Fock equations for two diatomic molecules, HeH + and LiH, matching the accuracy previously obtained using multiwavelet bases.
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38

Shyh-Fang, Huang. "Video Classification and Adaptive QoP/QoS Control for Multiresolution Video Applications on IPTV." International Journal of Digital Multimedia Broadcasting 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/801641.

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With the development of heterogeneous networks and video coding standards, multiresolution video applications over networks become important. It is critical to ensure the service quality of the network for time-sensitive video services. Worldwide Interoperability for Microwave Access (WIMAX) is a good candidate for delivering video signals because through WIMAX the delivery quality based on the quality-of-service (QoS) setting can be guaranteed. The selection of suitable QoS parameters is, however, not trivial for service users. Instead, what a video service user really concerns with is the video quality of presentation (QoP) which includes the video resolution, the fidelity, and the frame rate. In this paper, we present a quality control mechanism in multiresolution video coding structures over WIMAX networks and also investigate the relationship between QoP and QoS in end-to-end connections. Consequently, the video presentation quality can be simply mapped to the network requirements by a mapping table, and then the end-to-end QoS is achieved. We performed experiments with multiresolution MPEG coding over WIMAX networks. In addition to the QoP parameters, the video characteristics, such as, the picture activity and the video mobility, also affect the QoS significantly.
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39

JANSEN, MAARTEN. "REFINEMENT INDEPENDENT WAVELETS FOR USE IN ADAPTIVE MULTIRESOLUTION SCHEMES." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 04 (July 2008): 521–39. http://dx.doi.org/10.1142/s0219691308002471.

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This paper constructs a class of semi-orthogonal and bi-orthogonal wavelet transforms on possibly irregular point sets with the property that the scaling coefficients are independent from the order of refinement. That means that scaling coefficients at a given scale can be constructed with the configuration at that scale only. This property is of particular interest when the refinement operation is data dependent, leading to adaptive multiresolution analyses. Moreover, the proposed class of wavelet transforms are constructed using a sequence of just two lifting steps, one of which contains a linear interpolating prediction operator. This operator easily allows extensions towards directional offsets from predictions, leading to an edge-adaptive nonlinear multiscale decomposition.
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40

González, Martin, Antonio Sánchez-Pedraza, Rebeca Marfil, Juan Rodríguez, and Antonio Bandera. "Data-Driven Multiresolution Camera Using the Foveal Adaptive Pyramid." Sensors 16, no. 12 (November 26, 2016): 2003. http://dx.doi.org/10.3390/s16122003.

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41

Roussel, Olivier, Kai Schneider, Alexei Tsigulin, and Henning Bockhorn. "A conservative fully adaptive multiresolution algorithm for parabolic PDEs." Journal of Computational Physics 188, no. 2 (July 2003): 493–523. http://dx.doi.org/10.1016/s0021-9991(03)00189-x.

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42

Cohen, A., S. M. Kaber, and M. Postel. "Adaptive multiresolution for finite volume solutions of gas dynamics." Computers & Fluids 32, no. 1 (January 2003): 31–38. http://dx.doi.org/10.1016/s0045-7930(01)00096-2.

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43

Acar, Erman, Sari Peltonen, and Ulla Ruotsalainen. "Adaptive multiresolution method for MAP reconstruction in electron tomography." Ultramicroscopy 170 (November 2016): 24–34. http://dx.doi.org/10.1016/j.ultramic.2016.08.002.

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44

Macq, B., and J. Y. Mertes. "Optimization of linear multiresolution transforms for scene adaptive coding." IEEE Transactions on Signal Processing 41, no. 12 (1993): 3568–72. http://dx.doi.org/10.1109/78.258099.

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45

Cai, Guofa, Yi Fang, and Guojun Han. "Design of an Adaptive Multiresolution $M$ -Ary DCSK System." IEEE Communications Letters 21, no. 1 (January 2017): 60–63. http://dx.doi.org/10.1109/lcomm.2016.2614682.

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46

Vargas, Javier. "Novel multiresolution approach for an adaptive structured light system." Optical Engineering 47, no. 2 (February 1, 2008): 023601. http://dx.doi.org/10.1117/1.2857404.

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47

Ping Wah Wong. "Adaptive error diffusion and its application in multiresolution rendering." IEEE Transactions on Image Processing 5, no. 7 (July 1996): 1184–96. http://dx.doi.org/10.1109/83.502397.

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48

Saber, Eli. "Multiresolution adaptive and progressive gradient-based color-image segmentation." Journal of Electronic Imaging 19, no. 1 (January 1, 2010): 013001. http://dx.doi.org/10.1117/1.3277150.

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49

Rastigejev, Yevgenii A., and Samuel Paolucci. "Wavelet-based adaptive multiresolution computation of viscous reactive flows." International Journal for Numerical Methods in Fluids 52, no. 7 (2006): 749–84. http://dx.doi.org/10.1002/fld.1202.

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50

Ziavras, S. G., and P. Meer. "Adaptive Multiresolution Structures for Image Processing on Parallel Computers." Journal of Parallel and Distributed Computing 23, no. 3 (December 1994): 475–83. http://dx.doi.org/10.1006/jpdc.1994.1159.

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