Relevant bibliographies by topics / Wavelets (Mathematics) Image processing Surfaces

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Conference papers

Academic literature on the topic 'Wavelets (Mathematics) Image processing Surfaces'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Wavelets (Mathematics) Image processing Surfaces"

1

JIANG, QINGTANG. "BIORTHOGONAL WAVELETS WITH SIX-FOLD AXIAL SYMMETRY FOR HEXAGONAL DATA AND TRIANGLE SURFACE MULTIRESOLUTION PROCESSING." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 05 (September 2011): 773–812. http://dx.doi.org/10.1142/s0219691311004316.

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This paper discusses the construction of highly symmetric compactly supported wavelets for hexagonal data/image and triangle surface multiresolution processing. Recently, hexagonal image processing has attracted attention. Compared with the conventional square lattice, the hexagonal lattice has several advantages, including that it has higher symmetry. It is desirable that the filter banks for hexagonal data also have high symmetry which is pertinent to the symmetric structure of the hexagonal lattice. The high symmetry of filter banks and wavelets not only leads to simpler algorithms and efficient computations, it also has the potential application for the texture segmentation of hexagonal data. While in the field of computer-aided geometric design (CAGD), when the filter banks are used for surface multiresolution processing, it is required that the corresponding decomposition and reconstruction algorithms for regular vertices have high symmetry, which make it possible to design the corresponding multiresolution algorithms for extraordinary vertices. In this paper we study the construction of six-fold axial symmetric biorthogonal filter banks and the associated wavelets, with both the dyadic and [Formula: see text]-refinements. The constructed filter banks have the desirable symmetry for hexagonal data processing. By associating the outputs (after one-level multiresolution decomposition) appropriately with the nodes of the regular triangular mesh with which the input data is associated (sampled), we represent multiresolution analysis and synthesis algorithms as templates. The six-fold axial symmetric filter banks constructed in this paper result in algorithm templates with desirable symmetry for triangle surface processing.
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de Oliveira, H. M., V. V. Vermehren, and R. J. Cintra. "Multi-dimensional wavelets for scalable image decomposition: Orbital wavelets." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 05 (June 15, 2020): 2050038. http://dx.doi.org/10.1142/s0219691320500381.

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Wavelets are closely related to Schrödinger’s wave functions and the interpretation of Born. Similar to the appearance of atomic orbital, it is proposed to combine anti-symmetric wavelets into orbital wavelets. The proposed approach allows the increase of the dimension of wavelets through this process. New orbital 2D-wavelets are introduced for the decomposition of still images, showing that it is possible to perform an analysis simultaneously in two distinct scales. An example of such an image analysis is shown.
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Farkov, Yu A., and S. A. Stroganov. "The use of discrete dyadic wavelets in image processing." Russian Mathematics 55, no. 7 (July 2011): 47–55. http://dx.doi.org/10.3103/s1066369x11070073.

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Fryzlewicz, Piotr, and Catherine Timmermans. "SHAH: SHape-Adaptive Haar Wavelets for Image Processing." Journal of Computational and Graphical Statistics 25, no. 3 (July 2, 2016): 879–98. http://dx.doi.org/10.1080/10618600.2015.1048345.

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ZHANG, ZHONG, NARIYA KOMAZAKI, TAKASHI IMAMURA, TETSUO MIYAKE, and HIROSHI TODA. "DIRECTIONAL SELECTION OF TWO-DIMENSIONAL COMPLEX DISCRETE WAVELET TRANSFORM AND ITS APPLICATION TO IMAGE PROCESSING." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 04 (July 2010): 659–76. http://dx.doi.org/10.1142/s0219691310003705.

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In this study, a novel direction selection method using the two-dimensional complex discrete wavelet transform (2D-CDWT) is proposed. In order to achieve arbitrary direction selection, the directional filters are first designed. Calculation procedure of directional selection can be shown as follows: (1) The 16 sub-images are generally generated from the original image by the 2D-CDWT without a down-sampling process and the 12 sub-images that correspond to the high-frequency components are selected. (2) The 12 sub-images are filtered by using the designed directional filter. (3) The down-sampling process is carried out and the resulting images are obtained. Furthermore, this method is applied to the surface analysis of a wafer, and it is confirmed that our method is effective in detecting irregular direction components.
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Zhang, Xi, and Noriaki Fukuda. "Lossy to lossless image coding based on wavelets using a complex allpass filter." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 04 (July 2014): 1460002. http://dx.doi.org/10.1142/s0219691314600029.

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Wavelet-based image coding has been adopted in the international standard JPEG 2000 for its efficiency. It is well-known that the orthogonality and symmetry of wavelets are two important properties for many applications of signal processing and image processing. Both can be simultaneously realized by the wavelet filter banks composed of a complex allpass filter, thus, it is expected to get a better coding performance than the conventional biorthogonal wavelets. This paper proposes an effective implementation of orthonormal symmetric wavelet filter banks composed of a complex allpass filter for lossy to lossless image compression. First, irreversible real-to-real wavelet transforms are realized by implementing a complex allpass filter for lossy image coding. Next, reversible integer-to-integer wavelet transforms are proposed by incorporating the rounding operation into the filtering processing to obtain an invertible complex allpass filter for lossless image coding. Finally, the coding performance of the proposed orthonormal symmetric wavelets is evaluated and compared with the D-9/7 and D-5/3 biorthogonal wavelets. It is shown from the experimental results that the proposed allpass-based orthonormal symmetric wavelets can achieve a better coding performance than the conventional D-9/7 and D-5/3 biorthogonal wavelets both in lossy and lossless coding.
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Low, Yin Fen, and Rosli Besar. "Optimal Wavelet Filters for Medical Image Compression." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 02 (June 2003): 179–97. http://dx.doi.org/10.1142/s0219691303000128.

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Recently, the wavelet transform has emerged as a cutting edge technology, within the field of image compression research. The basis functions of the wavelet transform are known as wavelets. There are a variety of different wavelet functions to suit the needs of different applications. Among the most popular wavelets are Haar, Daubechies, Coiflet and Biorthogonal, etc. The best wavelets (functions) for medical image compression are widely unknown. The purpose of this paper is to examine and compare the difference in impact and quality of a set of wavelet functions (wavelets) to image quality for implementation in a digitized still medical image compression with different modalities. We used two approaches to the measurement of medical image quality: objectively, using peak signal to noise ratio (PSNR) and subjectively, using perceived image quality. Finally, we defined an optimal wavelet filter for each modality of medical image.
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ZENG, LI, RUI MA, JIANYUAN HUANG, and P. R. HUNZIKER. "THE CONSTRUCTION OF 2D ROTATIONALLY INVARIANT WAVELETS AND THEIR APPLICATION IN IMAGE EDGE DETECTION." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 01 (January 2008): 65–82. http://dx.doi.org/10.1142/s0219691308002227.

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Construction of rotationally invariant 2D wavelets is important in image processing, but is difficult. In this paper, the discrete form of a 2D rotationally invariant wavelet is constructed by back-projection from a 1D symmetrical wavelet. Such rotationally invariant 2D wavelets allow effective edge detection in any direction. These wavelets are combined with the 2D directional wavelets for the use in non-maximum suppression edge detection. The resulting binary edges are characterized by finer contours, differential detection characteristics and noise robustness compared to other edge detectors in various test images. In particular, where fine binary edges in noisy images are required, this novel approach compares favorably to the classical methods of Canny and Mallat with detection of more edges thanks to the implicit denoising properties and the full rotational invariance of the method.
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BHATNAGAR, GAURAV, R. JAYAGANTHAN, and BALASUBRAMANIAN RAMAN. "WAVELET ANALYSIS OF SURFACE MORPHOLOGIES OF MAGNETRON SPUTTERED Al-Cu THIN FILMS." International Journal of Wavelets, Multiresolution and Information Processing 07, no. 01 (January 2009): 59–74. http://dx.doi.org/10.1142/s0219691309002775.

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Al - Cu thin films were deposited by DC magnetron sputtering. The films are characterized by atomic force microscopy and its surface morphologies are analyzed by wavelet technique. Multiresolution signal decomposition wavelet technique was employed to extract the surface roughness from the AFM images of Al - Cu thin films. It is observed that the Al - Cu thin films exhibit higher surface roughness value with increasing deposition time. The calculated surface roughness of the thin films, using wavelet technique, is comparable with that of its experimental values.
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Josephine, S., and S. Murugan. "Noise Removal from Brain MRI Images Using Adaptive Bayesian Shrinkage." Journal of Computational and Theoretical Nanoscience 17, no. 4 (April 1, 2020): 1818–25. http://dx.doi.org/10.1166/jctn.2020.8446.

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In MR machine, surface coils, especially phased-arrays are used extensively for acquiring MR images with high spatial resolution. The signal intensities on images acquired using these coils have a non-uniform map due to coil sensitivity profile. Although these smooth intensity variations have little impact on visual diagnosis, they become critical issues when quantitative information is needed from the images. Sometimes, medical images are captured by low signal to noise ratio (SNR). The low SNR makes it difficult to detect anatomical structures because tissue characterization fails on those images. Hence, denoising are essential processes before further processing or analysis will be conducted. They found that the noise in MR image is of Rician distribution. Hence, general filters cannot be used to remove these types of noises. The linear spatial filtering technique blurs the object boundaries and degrades the sharp details. The existing works proved that Wavelet based works eliminates the noise coefficient that called wavelet thresholding. Wavelet thresholding estimates the noise level from high frequency content and estimates the threshold value by comparing the estimated noisy wavelet coefficient with other wavelet coefficients and eliminate the noisy pixel intensity value. Bayesian Shrinkage rule is one of the widely used methods. It uses for Gaussian type of noise, the proposed method introduced some adaptive technique in Bayesian Shrinkage method to remove Rician type of noises from MRI images. The results were verified using quantitative parameters such as Peak Signal to Noise Ratio (PSNR). The proposed Adaptive Bayesian Shrinkage Method (ABSM) outperformed existing methods.
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Dissertations / Theses on the topic "Wavelets (Mathematics) Image processing Surfaces"

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Lounsbery, John Michael. "Multiresolution analysis for surfaces of arbitrary topological type /." Thesis, Connect to this title online; UW restricted, 1994. http://hdl.handle.net/1773/6998.

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May, Heather. "Wavelet-based Image Processing." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448037498.

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Sun, Lu. "Geometric transformation and image singularity with wavelet analysis." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/656.

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Liu, Xiaoqiu. "Microscopic tissue image processing for pathological evaluation /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9999304.

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Grant, Jeremy. "Wavelet-Based Segmentation of Fluorescence Microscopy Images in Two and Three Dimensions." Fogler Library, University of Maine, 2008. http://www.library.umaine.edu/theses/pdf/GrantJ2008.pdf.

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Liao, Zhiwu. "Image denoising using wavelet domain hidden Markov models." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/616.

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Yang, Jianwei. "Construction of wavelets based on unitary transform, permutation and matrix extension with applications to watermarking." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/617.

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Silwal, Sharad Deep. "Bayesian inference and wavelet methods in image processing." Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/2355.

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Kim, Il-Ryeol. "Wavelet domain partition-based signal processing with applications to image denoising and compression." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 2.98 Mb., 119 p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3221054.

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Tourshan, Khaled. "Parameterization of slant and slantlet/wavelet transforms with applications /." Thesis, Connect to Dissertations & Theses @ Tufts University, 2003.

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Thesis (Ph.D.)--Tufts University, 2003.
Adviser: Joseph P. Noonan. Submitted to the Dept. of Electrical Engineering. Includes bibliographical references (leaves 149-149). Access restricted to members of the Tufts University community. Also available via the World Wide Web;
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Books on the topic "Wavelets (Mathematics) Image processing Surfaces"

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Prasad, L. Wavelet analysis with applications to image processing. Boca Raton: CRC Press, 1997.

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Starck, J. L. Sparse image and signal processing: Wavelets, curvelets, morphological diversity. New York: Cambridge University Press, 2010.

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Starck, J. L. Image processing and data analysis: The multiscale approach. Cambridge, U.K: Cambridge University Press, 1998.

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Starck, J. L. Sparse image and signal processing: Wavelets, curvelets, morphological diversity. Cambridge: Cambridge University Press, 2010.

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Kaarna, Arto. Multispectral image compression using the wavelet transform. Lappeenranta, Finland: Lappeenranta University of Technology, 2000.

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Applied functional analysis: Numerical methods, wavelet methods, and image processing. New York: M. Dekker, 2004.

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Lin, Cao, ed. Xiao bo fen xi ji qi zai shu zi tu xiang chu li zhong de ying yong: Wavelet analysis and its application in digital image processing / . Beijing Shi: Dian zi gong ye chu ban she, 2012.

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1962-, Bryan Kurt, ed. Discrete fourier analysis and wavelets: Applications to signal and image processing. Hoboken, N.J: Wiley, 2008.

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(Society), SPIE, ed. Wavelets XIII: 2-4 August 2009, San Diego, California, United States. Bellingham, Wash: SPIE, 2009.

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Petrosian, Arthur A. Wavelets in Signal and Image Analysis: From Theory to Practice. Dordrecht: Springer Netherlands, 2001.

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Conference papers on the topic "Wavelets (Mathematics) Image processing Surfaces"

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Anantrasirichai, N., J. Burn, and David R. Bull. "Orientation estimation for planar textured surfaces based on complex wavelets." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025682.

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Denis, Leon, Shahid M. Satti, Adrian Munteanu, Jan Cornelis, and Peter Schelkens. "Context-conditioned composite coding of 3D meshes based on wavelets on surfaces." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5414066.

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