Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/14788
Title: Computational methods in stochastic modeling for data mining
Researcher: Sailapathi sekar.P
Guide(s): Senthamarai kannan.K
Keywords: Computational method, stochastic modeling, data mining, various linear, impulse noise
Upload Date: 7-Jan-2014
University: Manonmaniam Sundaranar University
Completed Date: December 2010
Abstract: In image processing, various linear and non linear filtering newlinemethods have been proposed for the removal of impulse noise. Linear newlinefiltering techniques used for noise reduction in images are simply given newlineby the average of the pixels contained in the neighborhood of the filter newlinemask. However, linear filters cannot effectively reduce impulse noise and newlinehave a tendency to blur the edges of an image. In such situations, newlinemedian filters, which are non linear filters, provide an effective solution. newlineCompared with convolution filters, the median filter is more robust in newlinethat a single very unrepresentative pixel in the filter window will not newlineaffect the median value significantly. Also, since the median must newlineactually be one of the pixels in the filter window, the median filter does newlinenot create new pixel values when the filter crosses an edge. For this newlinereason, the median filter is better in preserving sharp discontinuities newlinethan linear filters. Unfortunately, the median filter is prone to alter pixels newlineundisturbed by noise, thereby causing a number of artifacts including newlineedge jitter and streaking. Modified forms of the median filter which still newlineretain the rank order structure have been proposed to overcome these newlineshortcomings. Basically, the task is to decide when to apply the median newlinefilter and when to keep the pixels unchanged. Among those are the newline27 newlineCenter-Weighted Median filters, which give current pixel a large weight newlineand the final output is chosen between the median and the current pixel newlinevalue, detail-preserving median filters and rank ordered mean filter newlineexcludes the current pixel itself from the median filter, progressive newlineswitching median filter, soft-decision-based filter and prediction-basedfilter. newline
Pagination: xiv, 186p.
URI: http://hdl.handle.net/10603/14788
Appears in Departments:Department of Statistics

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02_certificate.pdf48.49 kBAdobe PDFView/Open
03_declaration.pdf37.59 kBAdobe PDFView/Open
04_acknowledgement.pdf40.51 kBAdobe PDFView/Open
05_contents.pdf61.13 kBAdobe PDFView/Open
06_list of tables and figures.pdf87.6 kBAdobe PDFView/Open
07_chapter 1.pdf276.65 kBAdobe PDFView/Open
08_chapter 2.pdf205.31 kBAdobe PDFView/Open
09_chapter 3.pdf592.9 kBAdobe PDFView/Open
10_chapter 4.pdf300.32 kBAdobe PDFView/Open
11_chapter 5.pdf3.78 MBAdobe PDFView/Open
12_chapter 6.pdf867.57 kBAdobe PDFView/Open
13_chapter 7.pdf68.09 kBAdobe PDFView/Open
14_bibiliography.pdf148.29 kBAdobe PDFView/Open


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