tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File Exchange - category:"Mathematics" category:"Data Analysis"User-contributed code library2014-11-28T11:19:39-05:006541100tag:www.mathworks.com,2005:FileInfo/470232014-06-21T11:40:25Z2014-11-28T07:00:50ZChebfunChebfun is an open-source package for numerical computation with functions to 15-digit accuracy<p>Chebfun is an open-source software system for numerical computing with functions. The mathematical basis is piecewise polynomial interpolation implemented with what we call “Chebyshev technology”. The foundations are described, with Chebfun examples, in the book Approximation Theory and Approximation Practice (L. N. Trefethen, SIAM 2013). Chebfun has extensive capabilities for dealing with linear and nonlinear differential and integral operators, and also includes continuous analogues of linear algebra notions like QR and singular value decomposition. The Chebfun2 extension works with functions of two variables defined on a rectangle in the x-y plane.</p>
<p>Most Chebfun commands are overloads of familiar MATLAB commands — for example sum(f) computes an integral, roots(f) finds zeros, and u = L\f solves a differential equation.</p>
<p>To get a sense of the breadth and power of Chebfun, a good place to start is by looking at our Examples (<a href="http://www.chebfun.org/examples/">http://www.chebfun.org/examples/</a>) or the introductory Guide (<a href="http://www.chebfun.org/docs/guide/">http://www.chebfun.org/docs/guide/</a>).</p>
<p>Please contact us with any questions/comments at <a href="mailto:help@chebfun.org">help@chebfun.org</a>.</p>Chebfun Teamhttp://www.mathworks.com/matlabcentral/fileexchange/authors/55471MATLAB 8.2 (R2013b)MATLAB23972falsetag:www.mathworks.com,2005:FileInfo/485842014-11-27T08:40:43Z2014-11-27T08:47:24ZIterative Trimmed and Truncated Mean Algorithm filter (ITTM filter)ITTM filter for noise suppression and image processing.<p>The codes for the recently proposed iterative trimmed and truncated arithmetic mean (ITTM) are provided here. Here, trimming a sample means removing it and truncating a sample is to replace its value by a threshold. Simultaneously trimming and truncating enable the proposed filters to attenuate the mixed additive and exclusive noise in an effective way. The proposed trimming and truncating rules ensure that the output of the ITTM filter converges to the median. It offers an efficient method to estimate the median without time-consuming data sorting. Theoretical analysis shows that the ITTM filter of size n has a linear computational complexity O(n). Compared to the median filter and the iterative truncated arithmetic mean (ITM) filter, the proposed ITTM filter suppresses noise more effectively in some cases and has lower computational complexity .
<br />Demo codes include:
<br />1) Single type noise suppression
<br />2) image denoising.</p>
<p>Some further demo codes can be found in the codes for the WITM filters <a href="https://www.mathworks.com/matlabcentral/fileexchange/48585-weighted-iterative-truncated-mean-filter">https://www.mathworks.com/matlabcentral/fileexchange/48585-weighted-iterative-truncated-mean-filter</a>
<br />and fast ITM filters
<br /><a href="https://www.mathworks.com/matlabcentral/fileexchange/48583-fast-iterative-truncated-arithmetic-mean-filter--fitm-filter-">https://www.mathworks.com/matlabcentral/fileexchange/48583-fast-iterative-truncated-arithmetic-mean-filter--fitm-filter-</a></p>
<p>You may need to compile the c files before using them. Please try the function ITTM_compile. </p>
<p>More information can be found from the webpage:
<br /><a href="http://www.ntu.edu.sg/home/exdjiang/default.htm">http://www.ntu.edu.sg/home/exdjiang/default.htm</a>
<br />and my homepage:
<br /><a href="https://sites.google.com/site/miaozhenwei/">https://sites.google.com/site/miaozhenwei/</a></p>
<p>Reference paper:
<br />Z. W. Miao and X. D. Jiang, "Additive and Exclusive Noise Suppression byIterative Trimmed and Truncated Mean Algorithm," Signal Processing, vol. 99, pp. 147-158, June, 2014. </p>
<p>Related papers:
<br />Z. W. Miao and X. D. Jiang, "Weighted Iterative Truncated Mean Filter," IEEE Transactions on Signal Processing, Vol. 61, no. 16, pp. 4149-4160, August, 2013.
<br />Z. W. Miao and X. D. Jiang, "Further Properties and a Fast Realization of the Iterative Truncated Arithmetic Mean Filter" IEEE Transactions on Circuits and Systems-II, vol. 59, no. 11, pp. 810-814, November 2012.
<br /> X.D. Jiang, "Iterative Truncated Arithmetic Mean Filter And Its Properties," IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1537-1547, April 2012.</p>Miao Zhenweihttp://www.mathworks.com/matlabcentral/fileexchange/authors/509251MATLAB 7.2 (R2006a)falsetag:www.mathworks.com,2005:FileInfo/485832014-11-27T08:38:18Z2014-11-27T08:45:53ZFast Iterative Truncated Arithmetic Mean Filter (FITM filter)ITM/FITM filter<p>The codes of the recently proposed ITM filter and the fast ITM (FITM) are given in this part. The ITM/FTIM filter outperforms the median filter in attenuating the single type of noise, such as Gaussian and Laplacian noise, and the mixed type of noise, such as the mixed Gaussian and impulsive noise. It also offers a way to estimate the median by a simple arithmetic computing algorithm.
<br />Demo codes includes:
<br />1) Single type noise suppression</p>
<p>Some further demo codes can be found in the codes for the WITM filters <a href="https://www.mathworks.com/matlabcentral/fileexchange/48585-weighted-iterative-truncated-mean-filter">https://www.mathworks.com/matlabcentral/fileexchange/48585-weighted-iterative-truncated-mean-filter</a>
<br />and ITTM filters
<br /><a href="https://www.mathworks.com/matlabcentral/fileexchange/48584-iterative-trimmed-and-truncated-mean-algorithm-filter--ittm-filter-">https://www.mathworks.com/matlabcentral/fileexchange/48584-iterative-trimmed-and-truncated-mean-algorithm-filter--ittm-filter-</a></p>
<p>You need to compile the c files before using them. Please try the function FITM_compile. </p>
<p>More information can be found from the webpage:
<br /><a href="http://www.ntu.edu.sg/home/exdjiang/default.htm">http://www.ntu.edu.sg/home/exdjiang/default.htm</a>
<br />and my homepage:
<br /><a href="https://sites.google.com/site/miaozhenwei/">https://sites.google.com/site/miaozhenwei/</a></p>
<p>Reference papers:
<br />Z. W. Miao and X. D. Jiang, "Further Properties and a Fast Realization of the Iterative Truncated Arithmetic Mean Filter" IEEE Transactions on Circuits and Systems-II, vol. 59, no. 11, pp. 810-814, November 2012.
<br />X.D. Jiang, "Iterative Truncated Arithmetic Mean Filter And Its Properties," IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1537-1547, April 2012.</p>
<p>Related papers:
<br />Z. W. Miao and X. D. Jiang, "Weighted Iterative Truncated Mean Filter," IEEE Transactions on Signal Processing, Vol. 61, no. 16, pp. 4149-4160, August, 2013.
<br />Z. W. Miao and X. D. Jiang, "Additive and Exclusive Noise Suppression byIterative Trimmed and Truncated Mean Algorithm," Signal Processing, vol. 99, pp. 147-158, June, 2014.</p>Miao Zhenweihttp://www.mathworks.com/matlabcentral/fileexchange/authors/509251MATLAB 7.2 (R2006a)falsetag:www.mathworks.com,2005:FileInfo/485852014-11-27T08:43:11Z2014-11-27T08:43:11ZWeighted Iterative Truncated Mean FilterWITM filters<p>The codes for a rich class of filters named weighted ITM (WITM) filters are provided here. By iteratively truncating the extreme samples, the output of the WITM filter converges to the weighted median. Proper stopping criterion makes the WITM filters own merits of both the weighted mean and median filters and hence outperforms the both in some applications. Three structures are designed to enable the WITM filters being low-, band- and high-pass filters. Properties of these filters are presented and analyzed.
<br />Demo codes includes:
<br />1) low-pass WITM filters,
<br />2) band-pass WITM filters,
<br />3) high-pass WITM filters
<br />4) WITM filters for image denoising.</p>
<p>Some further demo codes can be found in the codes for the fast ITM filters <a href="https://www.mathworks.com/matlabcentral/fileexchange/48583-fast-iterative-truncated-arithmetic-mean-filter--fitm-filter-">https://www.mathworks.com/matlabcentral/fileexchange/48583-fast-iterative-truncated-arithmetic-mean-filter--fitm-filter-</a>
<br />and ITTM filters
<br /><a href="https://www.mathworks.com/matlabcentral/fileexchange/48584-iterative-trimmed-and-truncated-mean-algorithm-filter--ittm-filter-">https://www.mathworks.com/matlabcentral/fileexchange/48584-iterative-trimmed-and-truncated-mean-algorithm-filter--ittm-filter-</a></p>
<p>You may need to compile the c files before using them. Please try the function WITM_compile.</p>
<p>More inforation can be found from the webpage:
<br /><a href="http://www.ntu.edu.sg/home/exdjiang/default.htm">http://www.ntu.edu.sg/home/exdjiang/default.htm</a></p>
<p>and my homepage:
<br /><a href="https://sites.google.com/site/miaozhenwei/">https://sites.google.com/site/miaozhenwei/</a></p>
<p>Reference paper:
<br />Z. W. Miao and X. D. Jiang, "Weighted Iterative Truncated Mean Filter," IEEE Transactions on Signal Processing, Vol. 61, no. 16, pp. 4149-4160, August, 2013.</p>
<p>Related papers:
<br />Z. W. Miao and X. D. Jiang, "Additive and Exclusive Noise Suppression byIterative Trimmed and Truncated Mean Algorithm," Signal Processing, vol. 99, pp. 147-158, June, 2014.
<br />Z. W. Miao and X. D. Jiang, "Further Properties and a Fast Realization of the Iterative Truncated Arithmetic Mean Filter" IEEE Transactions on Circuits and Systems-II, vol. 59, no. 11, pp. 810-814, November 2012.
<br /> X.D. Jiang, "Iterative Truncated Arithmetic Mean Filter And Its Properties," IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1537-1547, April 2012.</p>Miao Zhenweihttp://www.mathworks.com/matlabcentral/fileexchange/authors/509251MATLAB 7.2 (R2006a)MATLABImage Processing ToolboxStatistics ToolboxControl System ToolboxRobust Control ToolboxSignal Processing ToolboxSimulink Verification and Validationfalsetag:www.mathworks.com,2005:FileInfo/485822014-11-27T01:42:16Z2014-11-27T01:42:16ZMutual Information of Two ImagesNormalized mutual information (MI) between histograms of two images<p>MUTUALINFO calculates the normalized Mutual Information (MI) of two
<br />images using their histograms.</p>
<p>Inputs:
<br /> ima : First image
<br /> imb : Second image</p>
<p>Outputs:
<br /> nmi : Normalized mutual information
<br /> jointEntropy : Joint entropy</p>Mohammad Haghighathttp://www.mathworks.com/matlabcentral/fileexchange/authors/140234MATLAB 8.4 (R2014b)MATLAB45926falsetag:www.mathworks.com,2005:FileInfo/485812014-11-27T01:39:33Z2014-11-27T01:39:33ZConvert YUV Videos into Image MatricesExtract Y, U and V components of a YUV 4:2:0 video<p>YUVREAD returns the Y, U and V components of a video in separate
<br />matrices.</p>
<p>Inputs:
<br /> vid : Input video sequence in YUV format
<br /> width : Frame width
<br /> hight : Frame hight
<br /> nFrame : Number of frames</p>
<p>Outputs:
<br /> Y U V : Y, U and V components of the video</p>Mohammad Haghighathttp://www.mathworks.com/matlabcentral/fileexchange/authors/140234MATLAB 8.4 (R2014b)MATLABfalsetag:www.mathworks.com,2005:FileInfo/415112013-04-26T18:13:46Z2014-11-27T01:04:07Zdeprecated -- Light Field Toolbox v0.2 -- v0.3 now availableA set of tools for working with light field (aka plenoptic) imagery in Matlab<p>This has been superseded by v0.3, <a href="http://www.mathworks.com/matlabcentral/fileexchange/48405-light-field-toolbox-v0-3">http://www.mathworks.com/matlabcentral/fileexchange/48405-light-field-toolbox-v0-3</a>
<br />This is a set of tools for working with light field (aka plenoptic) imagery in Matlab. This version of the toolbox is focused on processing images from the Lytro plenoptic camera. Features include decoding, colour correction and visualization of light field images. New in version 0.2 are camera calibration, image rectification, and convenience functions for managing multiple light fields and multiple cameras. Future releases will support additional input formats and depth and volumetric filtering for improved rendering quality.
<br />Download the sample light field pack at <a href="http://www-personal.acfr.usyd.edu.au/ddan1654/LFSamplePack1-r2.zip">http://www-personal.acfr.usyd.edu.au/ddan1654/LFSamplePack1-r2.zip</a>. This revision of the sample pack introduces a folder structure facilitating the use of multiple cameras. A small sample calibration is also available at <a href="http://www-personal.acfr.usyd.edu.au/ddan1654/PlenCalSmallExample.zip">http://www-personal.acfr.usyd.edu.au/ddan1654/PlenCalSmallExample.zip</a>, and further calibration datasets can be found at <a href="http://marine.acfr.usyd.edu.au/index.php/Plenoptic">http://marine.acfr.usyd.edu.au/index.php/Plenoptic</a> .</p>
<p>The image decoding, calibration and rectification process is described in:</p>
<p>[1] D. G. Dansereau, O. Pizarro, and S. B. Williams, "Decoding, calibration and rectification for lenselet-based plenoptic cameras," in Computer Vision and Pattern Recognition (CVPR), IEEE Conference on. IEEE, Jun 2013.</p>Donald Dansereauhttp://www.mathworks.com/matlabcentral/fileexchange/authors/522379MATLAB 7.13 (R2011b)Image Processing ToolboxOptimization ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/341992011-12-14T07:16:07Z2014-11-22T04:44:56ZPseudo B-Mode Ultrasound Image SimulatorSimulate pseudo B-mode ultrasonic images with customized tissue echogenicity maps<p>fcnPseudoBmodeUltrasoundSimulator generates a simulated Pseudo B-Mode Ultrasound image given the tissue acoustic echogenicity model for the structure to be imaged. The simulated image is of same image matrix size as the input echogenicity map. Linear transducer array architecture is assumed for image formation. Image formed with assumption of wave propagating vertically along the echogenicity map.
<br />This implementation is based on method proposed in </p>
<p>[1] Yongjian Yu, Acton, S.T., "Speckle reducing anisotropic diffusion," IEEE Trans. Image Processing, vol. 11, no. 11, pp. 1260-1270, Nov 2002. [<a href="http://dx.doi.org/10.1109/TIP.2002.804276">http://dx.doi.org/10.1109/TIP.2002.804276</a>]</p>
<p>[2] J. C. Bambre and R. J. Dickinson, "Ultrasonic B-scanning: A computer simulation", Phys. Med. Biol., vol. 25, no. 3, pp. 463–479, 1980. [<a href="http://dx.doi.org/10.1088/0031-9155/25/3/006">http://dx.doi.org/10.1088/0031-9155/25/3/006</a>]</p>Debdoot Sheethttp://www.mathworks.com/matlabcentral/fileexchange/authors/207426MATLAB 7.13 (R2011b)MATLABImage Processing ToolboxSignal Processing Toolboxfalsetag:www.mathworks.com,2005:FileInfo/485202014-11-20T08:18:13Z2014-11-20T08:19:55Zcentral_diff2(y,deltat,d,a)Finite difference approximation of any order derivative, any accuracy<p>central_diff2(y,deltat,d,a) returns the dth numeric derivative of y to specified accuracy order deltat^a for evenly sampled data (deltat is constant).
<br />Example:
<br />The third derivative of a time series f sampled at 0.01 second increments to accuracy order deltat^4:
<br />third_derivative_of_f=central_diff2(f,0.01,3,4);
<br />Interior points are calculated using the central difference method. First and last points are calculated using forward and backward difference methods. Intermediate point coefficients are calculated using a Vandermode system. The specified accuracy is maintained for all points. </p>
<p>Source for intermediate point algorithm: <a href="http://www.siam.org/books/ot98/sample/OT98Chapter1.pdf">http://www.siam.org/books/ot98/sample/OT98Chapter1.pdf</a></p>Benjamin Stromhttp://www.mathworks.com/matlabcentral/fileexchange/authors/526687MATLAB 8.4 (R2014b)1236123falsetag:www.mathworks.com,2005:FileInfo/389002012-11-05T15:35:04Z2014-11-20T06:46:13ZZernike MomentsMATLAB Code for the Fast Calculation of Zernike Moments of order n and repetition m on NxN images.<p>This submission includes 3 mfiles and 6 image files:
<br />1- Zernike_main.m (The main script that takes care of everything)
<br />2- Zernikmoment.m (Calculates the Zernike moments for an NxN ROI)
<br />3- radialpoly.m (Calculates the radial polynomials which are prerequisites for calculating Zernike moments)
<br />4- Six .png files to test the code.
<br />When you run the Zernike_main.m, it will calculate the Zernike moments of order n=4 and repetition m=2 for the input images. Since the first row images are just the rotated versions of a unique object (oval), the magnitudes of the Zernike moments for these three images are the same. In addition, the differences between the phases of the moments are proportional to the rotation angles of the images. Expectedly, the Zernike moments of two different shapes (e.g. oval and rectangle) are totally different. The reason of this behavior is the ability of Zernike moments in describing the shape of objects. </p>
<p>License agreement: To acknowledge the use of the code please cite the following papers:</p>
<p>A. Tahmasbi, F. Saki, S. B. Shokouhi, Classification of Benign and Malignant Masses Based on Zernike Moments, Comput. Biol. Med., vol. 41, no. 8, pp. 726-735, 2011.</p>
<p>F. Saki, A. Tahmasbi, H. Soltanian-Zadeh, S. B. Shokouhi, Fast opposite weight learning rules with application in breast cancer diagnosis, Comput. Biol. Med., vol. 43, no. 1, pp. 32-41, 2013.</p>Amir Tahmasbihttp://www.mathworks.com/matlabcentral/fileexchange/authors/292139MATLAB 7.11 (R2010b)MATLABfalse