文件列表:

contourletHMT (0, 2006-09-01)
contourletHMT\contourlet.m (939, 2003-07-09)
contourletHMT\contourlet2tree.m (2164, 2003-07-08)
contourletHMT\contournc.m (1289, 2006-07-29)
contourletHMT\dump_pdfbimagemodel.m (507, 2003-07-10)
contourletHMT\dump_pdfbmodel_to_file.cc (1536, 2006-07-29)
contourletHMT\dump_pdfbmodel_to_file.mexa64 (226422, 2006-09-01)
contourletHMT\dump_pdfbmodel_to_file.mexglx (222031, 2006-09-01)
contourletHMT\dump_pdfbmodel_to_file.mexmac (843456, 2006-09-01)
contourletHMT\dump_pdfbmodel_to_file.mexsol (230008, 2006-09-01)
contourletHMT\dump_pdfbmodel_to_file.mexw32 (40960, 2006-09-01)
contourletHMT\generate_model.m (1447, 2002-11-16)
contourletHMT\load_pdfbimagemodel.m (1174, 2003-07-10)
contourletHMT\load_pdfbmodel_from_file.cc (1779, 2006-07-29)
contourletHMT\load_pdfbmodel_from_file.mexa64 (226651, 2006-09-01)
contourletHMT\load_pdfbmodel_from_file.mexglx (222076, 2006-09-01)
contourletHMT\load_pdfbmodel_from_file.mexmac (847604, 2006-09-01)
contourletHMT\load_pdfbmodel_from_file.mexsol (230196, 2006-09-01)
contourletHMT\load_pdfbmodel_from_file.mexw32 (40960, 2006-09-01)
contourletHMT\makenoise.m (1123, 2006-07-29)
contourletHMT\matrix.cc (4688, 2006-07-29)
contourletHMT\matrix.hh (704, 2006-07-29)
contourletHMT\pdfbcalc_imagekld.m (2900, 2003-07-10)
contourletHMT\pdfbcalc_KLD.cc (1736, 2006-09-01)
contourletHMT\pdfbcalc_KLD.mexa64 (238374, 2006-07-29)
contourletHMT\pdfbcalc_KLD.mexglx (239388, 2006-07-29)
contourletHMT\pdfbcalc_KLD.mexmac (855676, 2006-07-29)
contourletHMT\pdfbcalc_KLD.mexsol (246068, 2006-07-29)
contourletHMT\pdfbcalc_KLD.mexw32 (40960, 2006-09-01)
contourletHMT\pdfbclassify_texture.m (1931, 2003-07-10)
contourletHMT\pdfbcreate_equiv_models.m (814, 2002-12-05)
contourletHMT\pdfbdenoiseimage.m (4164, 2006-07-29)
contourletHMT\pdfbest_KLD.cc (1831, 2006-09-01)
contourletHMT\pdfbest_KLD.mexa64 (238373, 2006-07-29)
contourletHMT\pdfbest_KLD.mexglx (239451, 2006-07-29)
contourletHMT\pdfbest_KLD.mexmac (855676, 2006-07-29)
contourletHMT\pdfbest_KLD.mexsol (246100, 2006-07-29)
contourletHMT\pdfbest_KLD.mexw32 (49152, 2006-09-01)
contourletHMT\pdfbflip_model.m (1928, 2002-12-05)
contourletHMT\pdfbgentdata.m (572, 2003-07-10)
... ...

Contourlet Hidden Markov Tree (HMT) toolbox (ver. 1.0) Duncan D.-Y. Po, June 2003 This directory contains the MATLAB files that fit a hidden Markov tree model to the contourlet coefficients of images. The files require the contourlet Toolbox by Minh N. Do, which can be downloaded from here: http://www.ifp.uiuc.edu/~minhdo/software/ In addition, the Image Processing Toolbox is also required. Assumptions: 1. For all the functions, the image is assumed to be stored as 8-bit unsigned integers (uint8). 2. For all the functions, the image is assumed to be square. There are 4 main functions in this toolbox: train HMT model, denoise, texture retrieval, and generate data according to HMT model. 1.) Training file: pdfbtrainimagethmt (Pyramidal directional filter bank train image tied hidden Markov tree) This file trains an HMT model to fit an image. For example, if we have an image 'lena.gif' in directory '/dir', then [model, stateprob] = pdfbtrainimagethmt('/dir/lena', 'gif', '', 0.01) trains an HMT model on that image to within an error in model parameter of 0.01 per tree. The resulting model is in the output 'model'. By default, this file uses a contourlet transform with '9-7' pyramidal filter and 'pkva' directional filter, decomposing the image to 4 scales with 4 directions in the two coarser scales and 8 directions in the two finer scales. The HMT model assumes the contourlet coefficients to be distributed between two states and in each state the coefficients have a mean of zero. These attributes can all be modified by modifying the first few lines of this file. The output 'stateprob' contains the probabilities for each coefficient to be in each of the two states. This output is useful for denoising. If we have an existing model 'mymodel' already in memory and we want to start the training using this model, then we can use [model, stateprob] = pdfbtrainimagethmt('/dir/lena', 'gif', 'mymodel', 0.01) This can be used to speed up the training process, if the existing model is available. For other available pyramidal and directional filter names for the contourlet transform, see functions dfilter and pfilters, respectively, in the contourlet toolbox. 2.) Denoising file: pdfbdenoiseimage (Pyramidal Directional Filter Bank Denoise Image) This file takes in a noisy image and denoises the image based on a given HMT model and an estimated noise power. For example, if we have an image noisylena.gif in path /dir/, and estimate the noise power to be 0.01 (noise power is normalized to lie between 0 and 1 always), then we can first train an HMT model on the image (see (1) above) to obtain its model and stateprob. Then we can denoise the image as follows cleanimage = pdfbdenoiseimage( model, stateprob, 0.01, '', '/dir/noisylena', 'gif') The output cleanimage contains the denoised image. By default, this file assumes the HMT model is trained using the '9-7' pyramidal filter and the 'pkva' directional filter. Other filters can be used by changing the first two lines of this file. 3.) Texture Retrieval file: pdfbclassify_texture (Pyramidal Directional Filter Bank Classify Texture) This file takes a query texture image and search through a texture image database to find the most similar texture images. For example, we have a database of *** texture images in tdb: tdb = {'texture1', 'texture2', ..., 'texture***'} The image files of these textures (texture1.gif, texture2.gif, ..., texture***.gif) are located in the path /tdir/. Suppose the HMT models of these *** textures are already stored in the path /mdir/. Now we want to search through this database to find the most similar textures to a texture65.gif. We can do this by kld = pdfbclassify_texture('texture65', 'gif', tdb, '/tdir', '/mdir', 0) Now suppose we don't have the HMT models of the database textures. We can still perform texture retrieval and the program will train the HMT models for the database textures and save them. Suppose we want to save the database texture HMT models in the directory /mdir2/. Then kld = pdfbclassify_texture('texture65', 'gif', tdb, '/tdir', '/mdir2', 1) Note that the output kld is an array of Kublick-Liebler distance between the query texture and every texture in the database in the order specified in tdb. i 4.) Generate Data file: pdfbgentdata (Pyramidal Directional Filter Bank Generate Tree Data) This file uses an existing model to generate random data in tree structure. For example we have an HMT model 'hmtmodel' in memory and we want to generate a random HMT with root level size of ***, then tree = pdfbgentdata(hmtmodel, ***) Notes: There are also other utility files in this toolbox. Their usage can be found by typing 'help ' in matlab prompt. They are: contourlet.m contourlet2tree.m contournc.m dump_pdfbimagemodel.m generate_model.m load_pdfbimagemodel.m pdfbcalc_imagekld.m pdfbcreate_equiv_models.m pdfbflip_model.m pdfbtestall_imagekld.m pdfbtrainthmt.m tree2contourlet.m type3detransform.m type3transform.m type4detransform.m type4transform.m There are also several mex files that might need to be recompiled, depending on the system. This can be easily done by typing on MATLAB command window >> mex .cc pdfbthmt.cc tree.cc matrix.cc utils.cc pdfbcalc_KLD pdfbest_KLD pdfbgen_tdata pdfbprotrain_thmt pdfbtrain_thmt dump_pdfbmodel_to_file load_pdfbmodel_from_file

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