ColorMRFdemo

所属分类:图形图像处理
开发工具:C++
文件大小:21355KB
下载次数:30
上传日期:2012-02-11 14:00:36
上 传 者zhjzju
说明:  a Markov random field (MRF) based color image segmentation algorithm
(This is the sample implementation of a Markov random field (MRF) based color image segmentation algorithm. The main code (colormrf.cpp) has been written by Mihaly Gara (gara@inf.u-szeged.hu, http://www.inf.u-szeged.hu/~gara/) with some minor contributions from Zoltan Kato (kato@inf.u-szeged.hu, http://www.inf.u-szeged.hu/~kato/) using the intenisty-based segmentation code of Csaba Gradwohl.)

文件列表:
ColorMRFdemo\images\color.bmp (49206, 2009-01-10)
ColorMRFdemo\images\color.ppm (49210, 1996-06-27)
ColorMRFdemo\images\color_105.bmp (49206, 2009-01-10)
ColorMRFdemo\images\color_105.ppm (49167, 1996-07-02)
ColorMRFdemo\images\color_18.bmp (49206, 2009-01-10)
ColorMRFdemo\images\color_18.ppm (49167, 1996-09-12)
ColorMRFdemo\images\color_26.bmp (49206, 2009-01-10)
ColorMRFdemo\images\color_26.ppm (49167, 1996-09-12)
ColorMRFdemo\images\color_33.bmp (49206, 2009-01-10)
ColorMRFdemo\images\color_33.ppm (49167, 1996-06-27)
ColorMRFdemo\images\color_59.bmp (49206, 2009-01-10)
ColorMRFdemo\images\color_59.ppm (49167, 1996-07-02)
ColorMRFdemo\linux\colormrfdemo (123082, 2009-01-10)
ColorMRFdemo\linux\makefile (1679, 2009-01-10)
ColorMRFdemo\src\CKProcessTimeCounter.cpp (6021, 2004-12-13)
ColorMRFdemo\src\CKProcessTimeCounter.h (1564, 2004-12-13)
ColorMRFdemo\src\colormrf.cpp (44922, 2011-11-06)
ColorMRFdemo\src\mersenne.cpp (5030, 2004-11-29)
ColorMRFdemo\src\randomc.h (6605, 2004-11-29)
ColorMRFdemo\windows\colormrfdemo.ncb (15305728, 2009-01-10)
ColorMRFdemo\windows\colormrfdemo.sdf (80105472, 2011-11-07)
ColorMRFdemo\windows\colormrfdemo.sln (885, 2011-11-04)
ColorMRFdemo\windows\colormrfdemo.sln.old (883, 2009-01-10)
ColorMRFdemo\windows\colormrfdemo.suo (13312, 2011-11-07)
ColorMRFdemo\windows\colormrfdemo.suo.old (7168, 2009-01-10)
ColorMRFdemo\windows\colormrfdemo.vcproj (6933, 2009-01-10)
ColorMRFdemo\windows\colormrfdemo.vcproj.INFORM.kato.user (1419, 2009-01-10)
ColorMRFdemo\windows\colormrfdemo.vcxproj (10223, 2011-11-04)
ColorMRFdemo\windows\colormrfdemo.vcxproj.filters (1369, 2011-11-04)
ColorMRFdemo\windows\colormrfdemo.vcxproj.user (143, 2011-11-04)
ColorMRFdemo\windows\Release\BuildLog.htm (9448, 2009-01-10)
ColorMRFdemo\windows\Release\CKProcessTimeCounter.obj (66652, 2011-11-06)
ColorMRFdemo\windows\Release\cl.command.1.tlog (2714, 2011-11-06)
ColorMRFdemo\windows\Release\CL.read.1.tlog (21142, 2011-11-06)
ColorMRFdemo\windows\Release\CL.write.1.tlog (1834, 2011-11-06)
ColorMRFdemo\windows\Release\colormrfdemo.Build.CppClean.log (637, 2011-11-06)
ColorMRFdemo\windows\Release\colormrfdemo.lastbuildstate (110, 2011-11-06)
ColorMRFdemo\windows\Release\colormrfdemo.log (1820, 2011-11-06)
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This is the sample implementation of a Markov random field (MRF) based color image segmentation algorithm. The main code (colormrf.cpp) has been written by Mihaly Gara (gara@inf.u-szeged.hu, http://www.inf.u-szeged.hu/~gara/) with some minor contributions from Zoltan Kato (kato@inf.u-szeged.hu, http://www.inf.u-szeged.hu/~kato/) using the intenisty-based segmentation code of Csaba Gradwohl. This code is released under the GNU General Public License (see http://www.gnu.org/copyleft/gpl.html). Please acknowledge the use of our program by refering to the following paper: 1) Zoltan Kato, Ting Chuen Pong, and John Chung Mong Lee. Color Image Segmentation and Parameter Estimation in a Markovian Framework. Pattern Recognition Letters, 22(3-4):309--321, March 2001. Note that the current demo program implements only a supervised version of the segmentation method described in the above paper (i.e. parameter values are learned interactively from representative regions selected by the user). Otherwise, the program implements exactly the color MRF model proposed in the paper. The program uses the "Mersenne Twister" random number generator written by Agner Fog (http://www.agner.org/random/). The generator itself is described in the article by M. Matsumoto & T. Nishimura, in: ACM Transactions on Modeling and Computer Simulation, vol. 8, no. 1, 19***, pp. 3-30. Details on the initialization scheme can be found at http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html . INSTALLATION: ============= The code is platform independent. We have succesfully compiled it under Linux (RedHat, Fedora, Debian) and Windows (XP, 2003 server). 1) You need wxWidgets 2.8 or later (http://www.wxwidgets.org) 2a) Under Linux/Unix, please edit the makefile under the "linux" subdirectory and then $ make clean $ make should compile and install the program (it is called colormrfdemo). 2b) Under Windows, we provide .NET 2005 (VC++8) compatible project files. Open the "colormrfdemo" solution file under the "windows" subdirectory and choose "Build -> Rebuild ColorMRFdemo" from the menu to compile the program (it is called colormrfdemo.exe). Make sure the environment variable "WXWIN" is set correctly, otherwise you have to modify the project settings. Note that only the "Release" configuration works properly. USAGE NOTES: ============ The program works on BMP images. Some test images are provided under the 'images' subdirectory. The program GUI should be intuitive. Main steps: 1) Load a color image 2) Enter the number of pixel classes (~region type) 3) Push "Select classes" button 4) Press left mouse button over the input image and draw a rectangle over a representative region of the first class. Then push "Next class" button. The mean and variance-covariance should appear in the "Class parameters" window. Continue with the next class until a representative rectangle for all classes has been selected. 5) Set the weight of doubleton potentials (default is 2.5) and the stopping threshold (iterations are stopped when the energy change is less than the specified value). 6) Choose the optimization method from the pull-down list. 7) Adjust the optimization method's parameters: T0 - Initial temperature c - temperature scheduler (T(n+1) = c*T(n)) alpha - MMD's probability threshold 8) Push "Do it >>" button to execute segmentation. 9) Optionally, you can save the segmentation result as a BMP image. During segmentation, the current classification along with the temperature and global energy are displayed at each iteration. At the end, the elapsed CPU time is also displayed (excluding GUI oveheaad!).

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