Relevant bibliographies by topics / Multiresolution analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multiresolution analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Multiresolution analysis"

1

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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Lim, Jae Kun. "Gramian analysis of multivariate frame multiresolution analyses." Bulletin of the Australian Mathematical Society 66, no. 2 (October 2002): 291–300. http://dx.doi.org/10.1017/s0004972700040132.

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We perform a Gramian analysis of a frame multiresolution analysis to give a condition for it to admit a minimal wavelet set and to show that the frame bounds of the natural generator for the wavelet space of a degenerate frame multiresolution analysis shrink.
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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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Combettes, P. L., and J. C. Pesquet. "Convex multiresolution analysis." IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no. 12 (1998): 1308–18. http://dx.doi.org/10.1109/34.735804.

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Bhosale, Bharat. "Curvelet Based Multiresolution Analysis of Graph Neural Networks." International Journal of Applied Physics and Mathematics 4, no. 5 (2014): 313–23. http://dx.doi.org/10.7763/ijapm.2014.v4.304.

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Rakić, Dušan. "Multiresolution expansion in." Integral Transforms and Special Functions 20, no. 3-4 (April 2009): 231–38. http://dx.doi.org/10.1080/10652460802568143.

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Massopust, Peter R. "Generalized Multiresolution Schemes." Journal of Mathematical Analysis and Applications 221, no. 2 (May 1998): 574–94. http://dx.doi.org/10.1006/jmaa.1998.5917.

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Moore, John A., Ying Li, Devin T. O'Connor, Wylie Stroberg, and Wing Kam Liu. "Advancements in multiresolution analysis." International Journal for Numerical Methods in Engineering 102, no. 3-4 (January 13, 2015): 784–807. http://dx.doi.org/10.1002/nme.4840.

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Konik, Hubert, Alain Tremeau, and Bernard Laget. "Multiresolution Color Image Analysis." Color and Imaging Conference 3, no. 1 (January 1, 1995): 82–85. http://dx.doi.org/10.2352/cic.1995.3.1.art00021.

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Mickelin, Oscar, and Sertac Karaman. "Multiresolution Low-rank Tensor Formats." SIAM Journal on Matrix Analysis and Applications 41, no. 3 (January 2020): 1086–114. http://dx.doi.org/10.1137/19m1284579.

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Dissertations / Theses on the topic "Multiresolution analysis"

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Brennan, Victor L. "Principal component analysis with multiresolution." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/ank7079/brennan%5Fdissertation.pdf.

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Thesis (Ph. D.)--University of Florida, 2001.
Title from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also contains graphics. Vita. Includes bibliographical references (p. 120-123).
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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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Carter, Duane B. "Analysis of Multiresolution Data fusion Techniques." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36609.

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In recent years, as the availability of remote sensing imagery of varying resolution has increased, merging images of differing spatial resolution has become a significant operation in the field of digital remote sensing. This practice, known as data fusion, is designed to enhance the spatial resolution of multispectral images by merging a relatively coarse-resolution image with a higher resolution panchromatic image of the same geographic area. This study examines properties of fused images and their ability to preserve the spectral integrity of the original image. It analyzes five current data fusion techniques for three complex scenes to assess their performance. The five data fusion models used include one spatial domain model (High-Pass Filter), two algebraic models (Multiplicative and Brovey Transform), and two spectral domain models (Principal Components Transform and Intensity-Hue-Saturation). SPOT data were chosen for both the panchromatic and multispectral data sets. These data sets were chosen for the high spatial resolution of the panchromatic (10 meters) data, the relatively high spectral resolution of the multispectral data, and the low spatial resolution ratio of two to one (2:1). After the application of the data fusion techniques, each merged image was analyzed statistically, graphically, and for increased photointerpretive potential as compared with the original multispectral images. While all of the data fusion models distorted the original multispectral imagery to an extent, both the Intensity-Hue-Saturation Model and the High-Pass Filter model maintained the original qualities of the multispectral imagery to an acceptable level. The High-Pass Filter model, designed to highlight the high frequency spatial information, provided the most noticeable increase in spatial resolution.
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Srinivas, Sushma. "DETECTING VULNERABLE PLAQUES WITH MULTIRESOLUTION ANALYSIS." Cleveland State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=csu1326932229.

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Kruger, Stefan A. "Motion analysis and estimation using multiresolution affine models." Thesis, University of Bristol, 1998. http://hdl.handle.net/1983/f1c3201e-cc47-4064-a897-5264498767bf.

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Romero, Juan R. "Generalized multiresolution analysis construction and measure theoretic characterization /." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2942.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Zhao, Fangwei. "Multiresolution analysis of ultrasound images of the prostate." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2004. http://theses.library.uwa.edu.au/adt-WU2004.0028.

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[Truncated abstract] Transrectal ultrasound (TRUS) has become the urologist’s primary tool for diagnosing and staging prostate cancer due to its real-time and non-invasive nature, low cost, and minimal discomfort. However, the interpretation of a prostate ultrasound image depends critically on the experience and expertise of a urologist and is still difficult and subjective. To overcome the subjective interpretation and facilitate objective diagnosis, computer aided analysis of ultrasound images of the prostate would be very helpful. Computer aided analysis of images may improve diagnostic accuracy by providing a more reproducible interpretation of the images. This thesis is an attempt to address several key elements of computer aided analysis of ultrasound images of the prostate. Specifically, it addresses the following tasks: 1. modelling B-mode ultrasound image formation and statistical properties; 2. reducing ultrasound speckle; and 3. extracting prostate contour. Speckle refers to the granular appearance that compromises the image quality and resolution in optics, synthetic aperture radar (SAR), and ultrasound. Due to the existence of speckle the appearance of a B-mode ultrasound image does not necessarily relate to the internal structure of the object being scanned. A computer simulation of B-mode ultrasound imaging is presented, which not only provides an insight into the nature of speckle, but also a viable test-bed for any ultrasound speckle reduction methods. Motivated by analysis of the statistical properties of the simulated images, the generalised Fisher-Tippett distribution is empirically proposed to analyse statistical properties of ultrasound images of the prostate. A speckle reduction scheme is then presented, which is based on Mallat and Zhong’s dyadic wavelet transform (MZDWT) and modelling statistical properties of the wavelet coefficients and exploiting their inter-scale correlation. Specifically, the squared modulus of the component wavelet coefficients are modelled as a two-state Gamma mixture. Interscale correlation is exploited by taking the harmonic mean of the posterior probability functions, which are derived from the Gamma mixture. This noise reduction scheme is applied to both simulated and real ultrasound images, and its performance is quite satisfactory in that the important features of the original noise corrupted image are preserved while most of the speckle noise is removed successfully. It is also evaluated both qualitatively and quantitatively by comparing it with median, Wiener, and Lee filters, and the results revealed that it surpasses all these filters. A novel contour extraction scheme (CES), which fuses MZDWT and snakes, is proposed on the basis of multiresolution analysis (MRA). Extraction of the prostate contour is placed in a multi-scale framework provided by MZDWT. Specifically, the external potential functions of the snake are designated as the modulus of the wavelet coefficients at different scales, and thus are “switchable”. Such a multi-scale snake, which deforms and migrates from coarse to fine scales, eventually extracts the contour of the prostate
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Yang, Qingde. "Multiresolution analysis on non-abelian locally compact groups." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0018/NQ43523.pdf.

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Zhao, Fangwei. "Multiresolution analysis of ultrasound images of the prostate /." Connect to this title, 2003. http://theses.library.uwa.edu.au/adt-WU2004.0028.

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Davies, Andrew Richard. "Image feature analysis using the Multiresolution Fourier Transform." Thesis, University of Warwick, 1993. http://wrap.warwick.ac.uk/50490/.

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The problem of identifying boundary contours or line structures is widely recognised as an important component in many applications of image analysis and computer vision. Typical solutions to the problem employ some form of edge detection followed by line following or, more commonly in recent years, Hough transforms. Because of the processing requirements of such methods and to try to improve the robustness of the algorithms, a number of authors have explored the use of multiresolution approaches to the problem. Non-parametric, iterative approaches such as relaxation labelling and "Snakes" have also been used. This thesis presents a boundary detection algorithm based on a multiresolution image representation, the Multiresolution Fourier Transform (MFT), which represents an image over a range of spatial/spatial-frequency resolutions. A quadtree based image model is described in which each leaf is a region which can be modelled using one of a set of feature classes. Consideration is given to using linear and circular arc features for this modelling, and frequency domain models are developed for them. A general model based decision process is presented and shown to be applicable to detecting local image features, selecting the most appropriate scale for modelling each region of the image and linking the local features into the region boundary structures of the image. The use of a consistent inference process for all of the subtasks used in the boundary detection represents a significant improvement over the adhoc assemblies of estimation and detection that have been common in previous work. Although the process is applied using a restricted set of local features, the framework presented allows for expansion of the number of boundary feature models and the possible inclusion of models of region properties. Results are presented demonstrating the effective application of these procedures to a number of synthetic and natural images.
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Books on the topic "Multiresolution analysis"

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Nonlinear smoothing and multiresolution analysis. Basel: Birkhäuser Verlag, 2005.

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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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Ingrid, Daubechies, Mallat Stephane, and Willsky Alan S, eds. Wavelet transforms and multiresolution signal analysis. New York: IEEE, 1992.

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Itai, Yad-Shalom, and Langley Research Center, eds. Fast multiresolution algorithms for matrix-vector multiplication. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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Robinson, Stephen. "Multiresolution analysis of polygonal surfaces" graphics modeller. Manchester: University of Manchester, Department of Computer Science, 1996.

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Davies, Andrew R. Image feature analysis using the Multiresolution Fourier Transform. [s.l.]: typescript, 1993.

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Hsu, Tao I. Texture analysis and synthesis using the multiresolution Fourier transform. [s.l.]: typescript, 1994.

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Stevens, John Davenport. Detection of short transients in colored noise by multiresolution analysis. Monterey, Calif: Naval Postgraduate School, 2000.

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Book chapters on the topic "Multiresolution analysis"

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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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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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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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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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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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Christensen, Ole. "Frame Multiresolution Analysis." In Applied and Numerical Harmonic Analysis, 283–311. Boston, MA: Birkhäuser Boston, 2003. http://dx.doi.org/10.1007/978-0-8176-8224-8_13.

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Garas, John. "Fast Multiresolution Analysis." In Adaptive 3D Sound Systems, 129–63. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4419-8776-1_5.

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Christensen, Ole. "Frame Multiresolution Analysis." In Applied and Numerical Harmonic Analysis, 417–44. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25613-9_17.

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Cohen Tenoudji, Frédéric. "Wavelets; Multiresolution Analysis." In Modern Acoustics and Signal Processing, 337–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42382-1_19.

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Conference papers on the topic "Multiresolution analysis"

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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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GUAN, YUJING, and YUNSHI ZHOU. "GENERALIZED MULTIRESOLUTION ANALYSIS." In Proceedings of the Third International Conference on WAA. World Scientific Publishing Company, 2003. http://dx.doi.org/10.1142/9789812796769_0029.

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Kent, P. "Multiresolution image registration." In IEE Colloquium on `Multiresolution Modelling and Analysis in Image Processing and Computer Vision'. IEE, 1995. http://dx.doi.org/10.1049/ic:19950501.

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Audenino, Pauline, and Mihai Datcu. "Multiresolution analysis of DEM." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2004. http://dx.doi.org/10.1117/12.510980.

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Wilson, R. "Multiresolution stochastic image models." In IEE Colloquium on `Multiresolution Modelling and Analysis in Image Processing and Computer Vision'. IEE, 1995. http://dx.doi.org/10.1049/ic:19950498.

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Linton, Eric, Paul Albee, Patrick Kinnicutt, and En-Bing Lin. "Multiresolution Analysis of DNA Sequences." In 2010 Second International Conference on Computer Research and Development. IEEE, 2010. http://dx.doi.org/10.1109/iccrd.2010.32.

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Erdol and Kyperountas. "Detection over multiresolution analysis subspaces." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1006064.

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Erdol, Nurgun, and Spyros Kyperountas. "Detection over Multiresolution analysis subspaces." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5744923.

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Eck, Matthias, Tony DeRose, Tom Duchamp, Hugues Hoppe, Michael Lounsbery, and Werner Stuetzle. "Multiresolution analysis of arbitrary meshes." In the 22nd annual conference. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/218380.218440.

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Benedetto, J. J., and S. Li. "Multiresolution analysis frames with applications." In Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319496.

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Reports on the topic "Multiresolution analysis"

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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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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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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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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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Rudin, Leonid, and Stanley Osher. Accurate Feature Detection and Estimation Using Nonlinear and Multiresolution Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1994. http://dx.doi.org/10.21236/ada290267.

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Willsky, Alan S. Multiresolution Signal and System Analysis and the Analysis and Control of Discrete Event Dynamic Systems. Fort Belvoir, VA: Defense Technical Information Center, February 1996. http://dx.doi.org/10.21236/ada305529.

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Street, Robert L., Jeffrey R. Koseff, Francis L. Ludwig, and Paul Piccirillo. Analysis of Patterns of Atmospheric Motions at Different Scales by Use of Multiresolution Feature Analysis and Wavelet Decomposition. Fort Belvoir, VA: Defense Technical Information Center, June 1998. http://dx.doi.org/10.21236/ada351189.

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Street, R. L., F. L. Ludwig, and Y. Chen. MULTIRESOLUTION FEATURE ANALYSIS AND OTHER TECHNIQUES FOR UNDERSTANDING AND MODELING TURBULENCE IN STABLE ATMOSPHERES Final Report. Office of Scientific and Technical Information (OSTI), April 2005. http://dx.doi.org/10.2172/838515.

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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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