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Relevant bibliographies by topics / Multiresolution

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Academic literature on the topic 'Multiresolution'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 May 2024

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Journal articles on the topic "Multiresolution"

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Kannan, S. Thabasu, and S. Azhagu Senthil. "Evaluvation of Multiresolution Watermarking Algorithm." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 85. http://dx.doi.org/10.23956/ijarcsse.v7i8.29.

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Now-a-days watermarking plays a pivotal role in most of the industries for providing security to their own as well as hired or leased data. This paper its main aim is to study the multiresolution watermarking algorithms and also choosing the effective and efficient one for improving the resistance in data compression. Computational savings from such a multiresolution watermarking framework is obvious. The multiresolutional property makes our watermarking scheme robust to image/video down sampling operation by a power of two in either space or time. There is no common framework for multiresolutional digital watermarking of both images and video. A multiresolution watermarking based on the wavelet transformation is selected in each frequency band of the Discrete Wavelet Transform (DWT) domain and therefore it can resist the destruction of image processing. The rapid development of Internet introduces a new set of challenging problems regarding security. One of the most significant problems is to prevent unauthorized copying of digital production from distribution. Digital watermarking has provided a powerful way to claim intellectual protection. We proposed an idea for enhancing the robustness of extracted watermarks. Watermark can be treated as a transmitted signal, while the destruction from attackers is regarded as a noisy distortion in channel. For the implementation, we have used minimum nine coordinate positions. The watermarking algorithms to be taken for this study are Corvi algorithm and Wang algorithm. In all graph, we have plotted X axis as peak signal to noise ratio (PSNR) and y axis as Correlation with original watermark. The threshold value ά is set to 5. The result is smaller than the threshold value then it is feasible, otherwise it is not.

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Yanjun Zhao and S. Belkasim. "Multiresolution Fourier Descriptors for Multiresolution Shape Analysis." IEEE Signal Processing Letters 19, no. 10 (October 2012): 692–95. http://dx.doi.org/10.1109/lsp.2012.2210040.

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San Antolín, Angel. "On translation invariant multiresolution analysis." Glasnik Matematicki 49, no. 2 (December 18, 2014): 377–94. http://dx.doi.org/10.3336/gm.49.2.11.

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Chiou-Ting Hsu and Ja-Ling Wu. "Multiresolution mosaic." IEEE Transactions on Consumer Electronics 42, no. 4 (1996): 981–90. http://dx.doi.org/10.1109/30.555800.

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Starck, Jean-Luc, and Albert Bijaoui. "Multiresolution deconvolution." Journal of the Optical Society of America A 11, no. 5 (May 1, 1994): 1580. http://dx.doi.org/10.1364/josaa.11.001580.

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Meyers, David. "Multiresolution Tiling." Computer Graphics Forum 13, no. 5 (December 1994): 325–40. http://dx.doi.org/10.1111/1467-8659.1350325.

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Xiaoqiang Lu and Xuelong Li. "Multiresolution Imaging." IEEE Transactions on Cybernetics 44, no. 1 (January 2014): 149–60. http://dx.doi.org/10.1109/tcyb.2013.2286496.

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Zhang, Liyan, Jian Ma, Bin Ran, and Lixin Yan. "Traffic Multiresolution Modeling and Consistency Analysis of Urban Expressway Based on Asynchronous Integration Strategy." Modelling and Simulation in Engineering 2017 (2017): 1–19. http://dx.doi.org/10.1155/2017/3694791.

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The paper studies multiresolution traffic flow simulation model of urban expressway. Firstly, compared with two-level hybrid model, three-level multiresolution hybrid model has been chosen. Then, multiresolution simulation framework and integration strategies are introduced. Thirdly, the paper proposes an urban expressway multiresolution traffic simulation model by asynchronous integration strategy based on Set Theory, which includes three submodels: macromodel, mesomodel, and micromodel. After that, the applicable conditions and derivation process of the three submodels are discussed in detail. In addition, in order to simulate and evaluate the multiresolution model, “simple simulation scenario” of North-South Elevated Expressway in Shanghai has been established. The simulation results showed the following.(1)Volume-density relationships of three submodels are unanimous with detector data.(2)When traffic density is high, macromodel has a high precision and smaller error and the dispersion of results is smaller. Compared with macromodel, simulation accuracies of micromodel and mesomodel are lower but errors are bigger.(3)Multiresolution model can simulate characteristics of traffic flow, capture traffic wave, and keep the consistency of traffic state transition. Finally, the results showed that the novel multiresolution model can have higher simulation accuracy and it is feasible and effective in the real traffic simulation scenario.

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Li, Ai Min, and Hai Bo Tian. "A Multiresolution Fairing Approach for NURBS Curves." Applied Mechanics and Materials 215-216 (November 2012): 1205–8. http://dx.doi.org/10.4028/www.scientific.net/amm.215-216.1205.

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Curve fairing has an important influence on curve editing and geometric modeling. Though there has been several different kinds of fairing methods, Multiresolution curve fairing has higher efficiency and simpler algorithms. Different from existing multiresolution curve fairing, a new multiresolution approach is presented based on non-uniform semiorthogonal B-spline wavelets, which can be applied for NURBS curve fairing. It has no restriction to B-spline curves’ knot sequence. This method effectively overcomes the limit of uniform or quasi-uniform B-spline wavelets for fairing. A detailed example is given to show the effectiveness of this multiresolution fairing method.

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Li, Ai Min, Wei Guo, Hai Bo Tian, and Fa Rong Kou. "Wavelet-Based Multiresolution NURBS Curve Fairing." Advanced Materials Research 314-316 (August 2011): 1562–65. http://dx.doi.org/10.4028/www.scientific.net/amr.314-316.1562.

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A multiresolution approach is presented for NURBS curve fairing based on nonuniform semiorthogonal B-spline wavelets built. This method provides greater flexibility and applicability than uniform B-spline wavelets for multiresolution curve fairing. An example is presented to validate effectiveness of this multiresolution fairing method. Furthermore, the algorithm can be easily applied to NURBS curves in three dimensions as well as in two.

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Dissertations / Theses on the topic "Multiresolution"

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Blanke, William John. "Multiresolution techniques on a parallel multidisplay multiresolution image compositing system." Access restricted to users with UT Austin EID Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3035942.

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Camilleri, Kenneth P. "Multiresolution texture segmentation." Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843549/.

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The problem of unsupervised texture segmentation was studied and a texture segmentation algorithm was developed making use of the minimum number of prior assumptions. In particular, no prior information about the type of textures, the number of textures and the appropriate scale of analysis for each texture was required. The texture image was analysed by the multiresolution Gabor expansion. The Gabor expansion generates a large number of features for each image and the most suitable feature space for segmentation needs to be determined automatically. The two-point correlation function was used to test the separability of the distributions in each feature space. A measure was developed to evaluate evidence of multiple clusters from the two-point correlation function, making it possible to determine the most suitable feature space for clustering. Thus, at a given resolution level, the most appropriate feature space was selected and used to segment the image. Due to inherent ambiguities and limitations of the two-point correlation function, this feature space exploration and segmentation was performed several times at the same resolution level until no further evidence of multiple clusters was found, at which point, the process was repeated at the next finer resolution level. In this way, the image was progressively segmented, proceeding from coarse to fine Gabor resolution levels without any knowledge of the actual number of textures present. In order to refine the region-labelled image obtained at the end of the segmentation process, two postprocessing pixel-level algorithms were developed and implemented. The first was the mixed pixel classification algorithm which is based on the analysis of the effect of the averaging window at the boundary between two regions and re-assigns the pixel labels to improve the boundary localisation. Multiresolution probabilistic relaxation is the second postprocessing algorithm which we developed. This algorithm incorporates contextual evidence to relabel pixels close to the boundary in order to smooth it and improve its localisation. The results obtained were quantified by known error measures, as well as by new error measures which we developed. The quantified results were compared to similar results by other authors and show that our unsupervised algorithm performs as well as other methods which assume prior information.

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Bhalerao, Abhir. "Multiresolution image segmentation." Thesis, University of Warwick, 1991. http://wrap.warwick.ac.uk/60866/.

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Image segmentation is an important area in the general field of image processing and computer vision. It is a fundamental part of the 'low level' aspects of computer vision and has many practical applications such as in medical imaging, industrial automation and satellite imagery. Traditional methods for image segmentation have approached the problem either from localisation in class space using region information, or from localisation in position, using edge or boundary information. More recently, however, attempts have been made to combine both region and boundary information in order to overcome the inherent limitations of using either approach alone. In this thesis, a new approach to image segmentation is presented that integrates region and boundary information within a multiresolution framework. The role of uncertainty is described, which imposes a limit on the simultaneous localisation in both class and position space. It is shown how a multiresolution approach allows the trade-off between position and class resolution and ensures both robustness in noise and efficiency of computation. The segmentation is based on an image model derived from a general class of multiresolution signal models, which incorporates both region and boundary features. A four stage algorithm is described consisting of: generation of a low-pass pyramid, separate region and boundary estimation processes and an integration strategy. Both the region and boundary processes consist of scale-selection, creation of adjacency graphs, and iterative estimation within a general framework of maximum a posteriori (MAP) estimation and decision theory. Parameter estimation is performed in situ, and the decision processes are both flexible and spatially local, thus avoiding assumptions about global homogeneity or size and number of regions which characterise some of the earlier algorithms. A method for robust estimation of edge orientation and position is described which addresses the problem in the form of a multiresolution minimum mean square error (MMSE) estimation. The method effectively uses the spatial consistency of output of small kernel gradient operators from different scales to produce more reliable edge position and orientation and is effective at extracting boundary orientations from data with low signal-to-noise ratios. Segmentation results are presented for a number of synthetic and natural images which show the cooperative method to give accurate segmentations at low signal-to-noise ratios (0 dB) and to be more effective than previous methods at capturing complex region shapes.

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陳健華 and Kin-wah Chan. "Multiresolution polygonal approximation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B30257402.

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Chan, Kin-wah. "Multiresolution polygonal approximation /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1985965X.

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Salem, Mohammed Abdel-Megeed Mohammed. "Multiresolution image segmentation." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15846.

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Systeme der Computer Vision spielen in der Automatisierung vieler Prozesse eine wichtige Rolle. Die wichtigste Aufgabe solcher Systeme ist die Automatisierung des visuellen Erkennungsprozesses und die Extraktion der relevanten Information aus Bildern oder Bildsequenzen. Eine wichtige Komponente dieser Systeme ist die Bildsegmentierung, denn sie bestimmt zu einem großen Teil die Qualitaet des Gesamtsystems. Fuer die Segmentierung von Bildern und Bildsequenzen werden neue Algorithmen vorgeschlagen. Das Konzept der Multiresolution wird als eigenstaendig dargestellt, es existiert unabhaengig von der Wavelet-Transformation. Die Wavelet-Transformation wird zur Verarbeitung von Bildern und Bildsequenzen zu einer 2D- bzw. 3D-Wavelet- Transformation erweitert. Fuer die Segmentierung von Bildern wird der Algorithmus Resolution Mosaic Expectation Maximization (RM-EM) vorgeschlagen. Das Ergebnis der Vorverarbeitung sind unterschiedlich aufgeloesten Teilbilder, das Aufloesungsmosaik. Durch dieses Mosaik lassen sich raeumliche Korrelationen zwischen den Pixeln ausnutzen. Die Verwendung unterschiedlicher Aufloesungen beschleunigt die Verarbeitung und verbessert die Ergebnisse. Fuer die Extraktion von bewegten Objekten aus Bildsequenzen werden neue Algorithmen vorgeschlagen, die auf der 3D-Wavelet-Transformation und auf der Analyse mit 3D-Wavelet-Packets beruhen. Die neuen Algorithmen haben den Vorteil, dass sie sowohl die raeumlichen als auch die zeitlichen Bewegungsinformationen beruecksichtigen. Wegen der geringen Berechnungskomplexitaet der Wavelet-Transformation ist fuer den ersten Segmentierungsschritt Hardware auf der Basis von FPGA entworfen worden. Aktuelle Anwendungen werden genutzt, um die Algorithmen zu evaluieren: die Segmentierung von Magnetresonanzbildern des menschlichen Gehirns und die Detektion von bewegten Objekten in Bildsequenzen von Verkehrsszenen. Die neuen Algorithmen sind robust und fuehren zu besseren Segmentierungsergebnissen.
More and more computer vision systems take part in the automation of various applications. The main task of such systems is to automate the process of visual recognition and to extract relevant information from the images or image sequences acquired or produced by such applications. One essential and critical component in almost every computer vision system is image segmentation. The quality of the segmentation determines to a great extent the quality of the final results of the vision system. New algorithms for image and video segmentation based on the multiresolution analysis and the wavelet transform are proposed. The concept of multiresolution is explained as existing independently of the wavelet transform. The wavelet transform is extended to two and three dimensions to allow image and video processing. For still image segmentation the Resolution Mosaic Expectation Maximization (RM-EM) algorithm is proposed. The resolution mosaic enables the algorithm to employ the spatial correlation between the pixels. The level of the local resolution depends on the information content of the individual parts of the image. The use of various resolutions speeds up the processing and improves the results. New algorithms based on the 3D wavelet transform and the 3D wavelet packet analysis are proposed for extracting moving objects from image sequences. The new algorithms have the advantage of considering the relevant spatial as well as temporal information of the movement. Because of the low computational complexity of the wavelet transform an FPGA hardware for the primary segmentation step was designed. Actual applications are used to investigate and evaluate all algorithms: the segmentation of magnetic resonance images of the human brain and the detection of moving objects in image sequences of traffic scenes. The new algorithms show robustness against noise and changing ambient conditions and gave better segmentation results.

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Kim, Yong Ku. "Bayesian multiresolution dynamic models." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180465799.

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Reyes-Aldasoro, Constantino Carlos. "Multiresolution volumetric texture segmentation." Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/67756/.

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This thesis investigates the segmentation of data in 2D and 3D by texture analysis using Fourier domain filtering. The field of texture analysis is a well-trodden one in 2D, but many applications, such as Medical Imaging, Stratigraphy or Crystallography, would benefit from 3D analysis instead of the traditional, slice-by-slice approach. With the intention of contributing to texture analysis and segmentation in 3D, a multiresolution volumetric texture segmentation (M-VTS) algorithm is presented. The method extracts textural measurements from the Fourier domain of the data via sub-band filtering using a Second Orientation Pyramid. A novel Bhattacharyya space, based on the Bhattacharyya distance is proposed for selecting of the most discriminant measurements and produces a compact feature space. Each dimension of the feature space is used to form a Quad Tree. At the highest level of the tree, new positional features are added to improve the contiguity of the classification. The classified space is then projected to lower levels of the tree where a boundary refinement procedure is performed with a 3D equivalent of butterfly filters. The performance of M-VTS is tested in 2D by classifying a set of standard texture images. The figures contain different textures that are visually stationary. M-VTS yields lower misclassification rates than reported elsewhere ([104, 111, 124]). The algorithm was tested in 3D with artificial isotropic data and three Magnetic Resonance Imaging sets of human knees with satisfactory results. The regions segmented from the knees correspond to anatomical structures that could be used as a starting point for other measurements. By way of example, we demonstrate successful cartilage extraction using our approach.

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Baker, Jonathan D. (Jonathan Daniel). "Multiresolution statistical object recognition." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/37721.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
Includes bibliographical references (leaves 105-108).
by Jonathan D. Baker.
M.S.

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Bobichon, Yves. "Restauration d'image en multiresolution." Nice, 1997. http://www.theses.fr/1997NICE5090.

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La restauration est un pretraitement indispensable a l'exploitation scientifique des images. Elle vise a obtenir une image de haute qualite a partir de donnees degradees. Cette these traite du filtrage des images radar a ouverture synthetique (ros) et de la restauration des images astronomiques comprimees avec pertes. Pour le filtrage du bruit de speckle dans les images ros. Nous avons propose une methode multi-echelles basee sur une transformation en ondelettes non decimee. La resolution geometrique de l'image est ainsi preservee, et grace a une transformation quadratique prealable des donnees, l'estimation de la radiometrie a partir de l'image filtree est optimale. Contrairement aux filtres homomorphiques, cette transformation n'elimine pas le caractere multiplicatif du speckle, ce qui nous a conduit a mener une etude originale sur le filtrage d'un tel bruit dans un espace multi-echelles. La decompression d'une image comprimee avec pertes peut etre vue comme un probleme de restauration d'image dont le but est de restituer une partie de l'information perdue. Nous avons propose un algorithme de compresssion pour les images astronomiques, qui concentre en priorite les pertes sur les structures non significatives. Cependant, pour des taux de compression eleves, la perte d'information est inevitable. Nous avons developpe une methode de restauration qui considere la decompression d'une image comprimee avec pertes, comme la resolution d'un probleme inverse sous contraintes. Une contrainte de douceur ameliore la qualite visuelle de l'image et les mesures astrometriques et photometriques effectuees sur les objets sont plus precises. Neanmoins, une telle contrainte ne permet pas de restituer les structures complexes liees a la fonction d'etalement (psf) du telescope. Nous avons propose une methode de restauration, qui utilise la psf comme contrainte supplementaire, et dans laquelle sont couples le filtrage, la compression et la deconvolution dans un cadre multiresolution.

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Books on the topic "Multiresolution"

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Bhalerao, Abhir H. Multiresolution image segmentation. [s.l.]: typescript, 1991.

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Merrill, Kathy D. Generalized Multiresolution Analyses. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99175-7.

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Barth, Timothy J., Tony Chan, and Robert Haimes, eds. Multiscale and Multiresolution Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56205-1.

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Lisowska, Agnieszka. Geometrical Multiresolution Adaptive Transforms. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05011-9.

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Gauch, John M. Multiresolution Image Shape Description. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2832-5.

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Gauch, John M. Multiresolution Image Shape Description. New York, NY: Springer New York, 1992.

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Gauch, John M. Multiresolution image shape description. New York: Springer-Verlag, 1992.

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Muszkats, Juan Pablo, Silvia Alejandra Seminara, and María Inés Troparevsky, eds. Applications of Wavelet Multiresolution Analysis. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61713-4.

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Ouahabi, Abdeldjalil, ed. Signal and Image Multiresolution Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118568767.

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Rohwer, Carl. Nonlinear Smoothing and Multiresolution Analysis. Basel: Birkhäuser Basel, 2005. http://dx.doi.org/10.1007/3-7643-7382-2.

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Book chapters on the topic "Multiresolution"

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Radunović, Desanka P. "Multiresolution." In Wavelets from Math to Practice, 35–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00614-2_3.

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Walnut, David F. "Multiresolution Analysis." In An Introduction to Wavelet Analysis, 163–214. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-1-4612-0001-7_7.

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Huck, Friedrich O., Carl L. Fales, and Zia-ur Rahman. "Multiresolution Decomposition." In Visual Communication, 97–125. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-2568-1_5.

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Cohen, Albert, and Robert D. Ryan. "Multiresolution analysis." In Wavelets and Multiscale Signal Processing, 7–35. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4425-2_2.

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Bonneau, Georges-Pierre, Gershon Elber, Stefanie Hahmann, and Basile Sauvage. "Multiresolution Analysis." In Mathematics and Visualization, 83–114. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-33265-7_3.

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Gomes, Jonas, and Luiz Velho. "Multiresolution Representation." In From Fourier Analysis to Wavelets, 75–88. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22075-8_6.

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Kaiser, Gerald. "Multiresolution Analysis." In A Friendly Guide to Wavelets, 139–75. Boston: Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-8111-1_7.

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Kunoth, Angela. "Multiresolution Methods." In Encyclopedia of Applied and Computational Mathematics, 990–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-540-70529-1_385.

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Cariolaro, Gianfranco. "Multiresolution and Wavelets." In Unified Signal Theory, 719–78. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-464-7_15.

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Brémaud, Pierre. "Smooth Multiresolution Analysis." In Mathematical Principles of Signal Processing, 229–37. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3669-4_16.

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Conference papers on the topic "Multiresolution"

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Pai, Dinesh K., and L. M. Reissell. "Touching Multiresolution Curves." In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0366.

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Abstract We describe a multiresolution representation of parametric curves that is well suited to haptic interaction. Our approach has been implemented on an embedded controller for the Pantograph haptic device, and has been used for haptic exploration of boundary curves obtained from images. The resolution of the representation can be dynamically adapted to the task and hardware limitations. Specifically, we use wavelet multiresolution, using biorthogonal wavelets with good approximation properties. In addition to the multiresolution representation, we maintain a hierarchy of error bounding boxes at each resolution level that is then used for fast, “anytime” collision detection.

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Finkelstein, Adam, and David H. Salesin. "Multiresolution curves." In the 21st annual conference. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/192161.192223.

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Finkelstein, Adam, Charles E. Jacobs, and David H. Salesin. "Multiresolution video." In the 23rd annual conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/237170.237266.

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Kraemer, Pierre, David Cazier, and Dominique Bechmann. "Multiresolution half-edges." In the 23rd Spring Conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/2614348.2614376.

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Seung-mok Oh and McClellan. "Multiresolution quadtree beamformer." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005302.

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Oh, Seung-mok, and James H. McClellan. "Multiresolution Quadtree Beamformer." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5745264.

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Wang, Lili, and Yi Wan. "Multiresolution image inpainting." In 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE). IEEE, 2012. http://dx.doi.org/10.1109/csae.2012.6272899.

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Navarro, Laurent, Michel Jourlin, and Guy Courbebaisse. "Logarithmic multiresolution analysis." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351521.

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Dougherty, Edward R., Junior Barrera, Gerard Mozelle, Seungchan Kim, and Marcel Brun. "Multiresolution filter design." In Electronic Imaging, edited by Edward R. Dougherty and Jaakko T. Astola. SPIE, 2000. http://dx.doi.org/10.1117/12.379392.

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Balasubramanian, Raja, Ricardo L. de Queiroz, and Zhigang Fan. "Multiresolution color correction." In Photonics West '98 Electronic Imaging, edited by Giordano B. Beretta and Reiner Eschbach. SPIE, 1998. http://dx.doi.org/10.1117/12.298277.

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Reports on the topic "Multiresolution"

1

AZIZMOHAMMADI, Siroos, and Stephan K. MATTHÄI. Multiresolution Wavelet Based Upscaling of Structured Grids. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0299.

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2

Basseville, Michele, Albert Benveniste, Kenneth C. Chou, Stuart A. Golden, Ramine Nikoukhah, and Alan S. Willsky. Modeling and Estimation of Multiresolution Stochastic Processes. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada459289.

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3

Kwong, Man Kam. MATLAB implementation of W-matrix multiresolution analyses. Office of Scientific and Technical Information (OSTI), January 1997. http://dx.doi.org/10.2172/427626.

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4

Dyn, N., and A. Ron. Multiresolution Analysis by Infinitely Differentiable Compactly Supported Functions. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada256526.

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5

Beylkin, G. Accurate Multiresolution Modeling of the Earth's Gravitational Field. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada389080.

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6

Chou, K. C., S. A. Golden, and A. S. Willsky. Multiresolution Stochastic Models, Data Fusion, and Wavelet Transforms. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada459326.

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7

Liu, Wing K. Multiresolution Analysis of Compressible Viscous Flow-Structure Interaction. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada377739.

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8

Fisher, John, Eric Grimson, and Alan Willsky. A Unified Multiresolution Framework for Automatic Target Recognition (ATR). Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada400047.

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9

Pulido, Jesus J., Daniel Livescu, Jonathan Lee Woodring, James Paul Ahrens, and Bernd Hamann. Survey and Analysis of Multiresolution Methods for Turbulence Data. Office of Scientific and Technical Information (OSTI), October 2015. http://dx.doi.org/10.2172/1222680.

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10

Ludwig, Francis L., James C. Cross, Street III, and Robert L. Multiresolution Feature Analysis and Wavelet Decomposition of Atmospheric Flows. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada294418.

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