Relevant bibliographies by topics / Image processing Filters (Mathematics)

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

Academic literature on the topic 'Image processing Filters (Mathematics)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Image processing Filters (Mathematics)"

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Et. al., Ch Kavya ,. "Performance Analysis of Different Filters for Digital Image Processing." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2572–76. http://dx.doi.org/10.17762/turcomat.v12i2.2220.

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Digital image processing is one of the drastically growing areas used in various real- time industries like medical, satellite, remote sensing, and pattern recognition. The output of the image processing depends on the quality of the image. Filters are used to modify the images, such as removing the noise and smoothing the images. It is essential to suppress the high- frequency values in the image for smoothening and improving the low-frequency values to enhance the image of strengthening else it doesn't provide good output. This paper discussed various filters and their functionalities concer
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Radoux, Julien, and Pierre Defourny. "Mathematical morphology pre-processing for enhanced segmentation of heterogeneous spatial regions." Revue Française de Photogrammétrie et de Télédétection, no. 208 (September 8, 2014): 33–38. http://dx.doi.org/10.52638/rfpt.2014.133.

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The very high spatial resolution of Pleiades images allows for the detection of small spatial objects such as buildings or isolated trees. However, the delineation of spatial regions, defined as associations between different spatial objects (such as open canopy forests or urban areas), becomes more challenging with the high level of details. On one hand, automated image segmentation algorithms often yield over-segmented polygons due to due to the high spectral heterogeneity of those regions. On the other hand, manual delineation was shown to end up with a significant bias from the interpreter
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Papanikolaou, V., K. N. Plataniotis, and A. N. Venetsanopoulos. "Adaptive filters for color image processing." Mathematical Problems in Engineering 4, no. 6 (1999): 529–38. http://dx.doi.org/10.1155/s1024123x98000957.

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The color filters that are used to attenuate noise are usually optimized to perform extremely well when dealing with certain noise distributions. Unfortunately it is often the case that the noise corrupting the image is not known. It is thus beneficial to knowa priorithe type of noise corrupting the image in order to select the optimal filter. A method of extracting and characterizing the noise within a digital color image using the generalized Gaussian probability density function (pdf) (B.D. Jeffs and W.H. Pun,IEEE Transactions on Image Processing,4(10), 1451–1456, 1995 andProceedings of the
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Lyra, Maria, Agapi Ploussi, Maritina Rouchota, and Stella Synefia. "Filters in 2D and 3D Cardiac SPECT Image Processing." Cardiology Research and Practice 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/963264.

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Nuclear cardiac imaging is a noninvasive, sensitive method providing information on cardiac structure and physiology. Single photon emission tomography (SPECT) evaluates myocardial perfusion, viability, and function and is widely used in clinical routine. The quality of the tomographic image is a key for accurate diagnosis. Image filtering, a mathematical processing, compensates for loss of detail in an image while reducing image noise, and it can improve the image resolution and limit the degradation of the image. SPECT images are then reconstructed, either by filter back projection (FBP) ana
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Mares-Javier, Marisol, Carlos Guillén-Galván, Rafael Lemuz-López, and Johan Debayle. "On the Properties of Some Adaptive Morphological Filters for Salt and Pepper Noise Removal." Image Analysis & Stereology 40, no. 1 (2021): 29–38. http://dx.doi.org/10.5566/ias.2418.

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Mathematical Morphology (MM) is a tool that can be applied to many digital image processing tasks that include the reduction of impulsive or salt and pepper noise in grayscale images. The morphological filters used for this task are filters resulting from two basic operators: erosion and dilation. However, when the level of contamination of the image is higher, these filters tend to distort the image. In this work we propose a pair of operators with properties, that better adapt to impulsive noise than other classical morphological filters, it is demonstrated to be increasing idempotent morpho
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Capobianco, Giovanni, Carmine Cerrone, Andrea Di Placido, et al. "Image convolution: a linear programming approach for filters design." Soft Computing 25, no. 14 (2021): 8941–56. http://dx.doi.org/10.1007/s00500-021-05783-5.

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AbstractImage analysis is a branch of signal analysis that focuses on the extraction of meaningful information from images through digital image processing techniques. Convolution is a technique used to enhance specific characteristics of an image, while deconvolution is its inverse process. In this work, we focus on the deconvolution process, defining a new approach to retrieve filters applied in the convolution phase. Given an image I and a filtered image $$I' = f(I)$$ I ′ = f ( I ) , we propose three mathematical formulations that, starting from I and $$I'$$ I ′ , are able to identify the f
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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 (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-samplin
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Nazarkevych, Mariia, Natalia Kryvinska, and Yaroslav Voznyi. "Applying Ateb–Gabor Filters to Biometric Imaging Problems." Symmetry 13, no. 4 (2021): 717. http://dx.doi.org/10.3390/sym13040717.

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This article presents a new method of image filtering based on a new kind of image processing transformation, particularly the wavelet-Ateb–Gabor transformation, that is a wider basis for Gabor functions. Ateb functions are symmetric functions. The developed type of filtering makes it possible to perform image transformation and to obtain better biometric image recognition results than traditional filters allow. These results are possible due to the construction of various forms and sizes of the curves of the developed functions. Further, the wavelet transformation of Gabor filtering is invest
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Hu, Ai Jun, Jing Jing Sun, and Wan Li Ma. "Application of Mathematical Morphological Filter in Vibration Signal Processing." Applied Mechanics and Materials 71-78 (July 2011): 4564–67. http://dx.doi.org/10.4028/www.scientific.net/amm.71-78.4564.

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The morphological filter as a nonlinear filtering method has been widely used for image (or signal) processing. Unlike the traditional digital filters, mathematical morphological operations are shape-based computing. Feature extraction of signals is entirely in the time domain without the transforming of the signal from the time domain to frequency domain. The vibration signal contaminated with noise is processed using morphological filter and Butterworth filter respectively. To compare the outputs of the two filters, we find that morphological filter shows better performance. It is effective
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Rafajłowicz, Ewaryst, Mirosław Pawlak, and Angsar Steland. "Nonlinear Image Processing and Filtering: A Unified Approach Based on Vertically Weighted Regression." International Journal of Applied Mathematics and Computer Science 18, no. 1 (2008): 49–61. http://dx.doi.org/10.2478/v10006-008-0005-z.

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Nonlinear Image Processing and Filtering: A Unified Approach Based on Vertically Weighted RegressionA class of nonparametric smoothing kernel methods for image processing and filtering that possess edge-preserving properties is examined. The proposed approach is a nonlinearly modified version of the classical nonparametric regression estimates utilizing the concept of vertical weighting. The method unifies a number of known nonlinear image filtering and denoising algorithms such as bilateral and steering kernel filters. It is shown that vertically weighted filters can be realized by a structur
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Dissertations / Theses on the topic "Image processing Filters (Mathematics)"

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Swarnakar, Vivek. "Optimal morphological filters /." Online version of thesis, 1993. http://hdl.handle.net/1850/11703.

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Loce, Robert P. "Morphological filter mean-absolute-error representation theorems and their application to optimal morphological filter design /." Online version of thesis, 1993. http://hdl.handle.net/1850/11065.

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Jarpa, Pedro F. "Quantifying the differences in low probability of intercept radar waveforms using quadrature mirror filtering." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FJarpa.pdf.

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Thesis (M.S.: Electrical Engineer)--Naval Postgraduate School, September 2002.<br>Thesis advisor(s): Phillip E. Pace, Herschel H. Loomis, Jr. Includes bibliographical references (p. 151-152). Also available online.
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Rosenthal, Jordan. "Filters and filterbanks for hexagonally sampled signals." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/13347.

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Randolph, Tami Rochele. "Image compression and classification using nonlinear filter banks." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/13439.

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Chan, Tai Wai. "Applications of spatial varying filter on image interpolation, demosaicing and video denosing [i.e. denoising] /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?ECED%202006%20CHANT.

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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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Chen, Wei-chun. "Simulation of a morphological image processor using VHDL. mathematical components /." Online version of thesis, 1993. http://hdl.handle.net/1850/11872.

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Paliouras, Eleni J. "Characterization of spatial texture for use in segmentation of synthetic aperture radar imagery /." Online version of thesis, 1994. http://hdl.handle.net/1850/11673.

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Chen, Hao. "Simulation of a morphological image processor using VHDL. control mechanism /." Online version of thesis, 1993. http://hdl.handle.net/1850/11744.

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Books on the topic "Image processing Filters (Mathematics)"

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Image processing and mathematical morphology fundamentals and applications. Taylor & Francis, 2009.

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Weickert, Joachim. Anisotropic diffusion in image processing. B.G. Teubner, 1998.

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Marshall, Stephen. Logic-based nonlinear image processing. SPIE Press, 2007.

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Dougherty, Edward R. An introduction to nonlinear image processing. SPIE Optical Engineering Press, 1994.

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Astola, Jaakko, and Edward R. Dougherty. Mathematical nonlinear image processing: A special issue of the Journal of mathematical imaging and vision. Springer Science+Business Media, LLC, 1993.

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Schröder, Hartmut. One- and multidimensional signal processing algorithms and applications in image processing. Wiley, 2000.

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1967-, Blume Holger, ed. One- and multidimensional signal processing algorithms and applications in image processing. Wiley, 2000.

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Astola, Jaakko. Image processing: Algorithms and systems VI : 28-29 January 2008, San Jose, California, USA. Edited by IS & T--the Society for Imaging Science and Technology and Society of Photo-optical Instrumentation Engineers. SPIE, 2008.

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Astola, Jaakko. Image processing: Algorithms and systems VIII : 19-20 January 2010, San Jose, California, United States. SPIE, 2010.

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Astola, Jaakko. Image processing: Algorithms and systems IX : 24-25 January 2011, San Jose, California, United States. Edited by IS & T--the Society for Imaging Science and Technology and SPIE (Society). SPIE, 2011.

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Book chapters on the topic "Image processing Filters (Mathematics)"

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Koskinen, Lasse, and Jaakko Astola. "Asymptotic Behavior of Morphological Filters." In Mathematical Nonlinear Image Processing. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3148-7_3.

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Longbotham, Harold, and David Eberly. "Statistical Properties, Fixed Points, and Decomposition with WMMR Filters." In Mathematical Nonlinear Image Processing. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3148-7_2.

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Wang, Qiaofei, Moncef Gabbouj, and Yrjö Neuvo. "Root-Signal Sets of Morphological Filters and their Use in Variable-Length BTC Image Coding." In Mathematical Nonlinear Image Processing. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3148-7_5.

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Dougherty, Edward R., and Robert M. Haralick. "Unification of Nonlinear Filtering in the Context of Binary Logical Calculus, Part I: Binary Filters." In Mathematical Nonlinear Image Processing. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3148-7_6.

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Dougherty, Edward R. "Unification of Nonlinear Filtering in the Context of Binary Logical Calculus, Part II: Gray-Scale Filters." In Mathematical Nonlinear Image Processing. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3148-7_7.

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Heijmans, Henk J. A. M. "Morphological Filters for Dummies." In Mathematical Morphology and its Applications to Image and Signal Processing. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0469-2_15.

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Nie, Xiaoning, and Rolf Unbehauen. "Mathematics of Denominator-separable Multidimensional Digital Filters with Application to Image Processing." In Progress in Industrial Mathematics at ECMI 96. Vieweg+Teubner Verlag, 1997. http://dx.doi.org/10.1007/978-3-322-96688-9_25.

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Schonfeld, Dan. "Weighted Composite Order-Statistics Filters." In Mathematical Morphology and its Applications to Image and Signal Processing. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0469-2_19.

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Hirata, R., Edward R. Dougherty, and Junior Barrera. "Some Applications of Aperture Filters." In Mathematical Morphology and its Applications to Image and Signal Processing. Springer US, 2002. http://dx.doi.org/10.1007/0-306-47025-x_14.

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Pessoa, Lúcio F. C., and Petros Maragos. "MRL-Filters and Their Adaptive Optimal Design for Image Processing." In Mathematical Morphology and its Applications to Image and Signal Processing. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0469-2_18.

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Conference papers on the topic "Image processing Filters (Mathematics)"

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Ye, Jong Chul. "Annihilating Filter-based Low Rank Hankel Matrix Approach for Biomedical Imaging and Image Processing." In Mathematics in Imaging. OSA, 2016. http://dx.doi.org/10.1364/math.2016.mt2h.2.

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Pires, Bernardo Rodrigues, Karanhaar Singh, and Jose M. F. Moura. "Approximating image filters with box filters." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6116693.

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Kondra, Shripad, and Vincent Torre. "Texture Classification Using Three Circular Filters." In Image Processing (ICVGIP). IEEE, 2008. http://dx.doi.org/10.1109/icvgip.2008.24.

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Nguyen, Chuong T., and Joseph P. Havlicek. "AM-FM image filters." In 2008 15th IEEE International Conference on Image Processing. IEEE, 2008. http://dx.doi.org/10.1109/icip.2008.4711873.

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Wang, Shih-Luen, Seyed M. M. Kahaki, and Armen Stepanyants. "Artificial neural network filters for enhancing 3D optical microscopy images of neurites." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2019. http://dx.doi.org/10.1117/12.2512989.

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Ell, Todd A. "Multi-Vector Color-Image Filters." In 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4379811.

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Vashisht, Manisha, and Madhulika Bhatia. "Role of Mathematics in Image Processing." In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). IEEE, 2019. http://dx.doi.org/10.1109/comitcon.2019.8862438.

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Hanson, Kenneth M. "Image Processing: Mathematics, Engineering, Or Art?" In 1985 Medical Imaging Conferences, edited by Samuel J. Dwyer III and Roger H. Schneider. SPIE, 1985. http://dx.doi.org/10.1117/12.947239.

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Matei, Radu, and Paul Ungureanu. "Image processing using elliptically-shaped filters." In 2009 International Symposium on Signals, Circuits and Systems - ISSCS 2009. IEEE, 2009. http://dx.doi.org/10.1109/isscs.2009.5206111.

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Ray, N., and S. T. Acton. "Adaptive image processing via snake filters." In Conference Record. Thirty-Fifth Asilomar Conference on Signals, Systems and Computers. IEEE, 2001. http://dx.doi.org/10.1109/acssc.2001.986946.

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