Hi Guanglei,the code is so nice on biological images.and fair on remote sensing images.have tried to test both methods on remote sensing images but results show otsu is better.
though i have used tested your code on several remote sensing images,I have failed to understand how the code operates.all is that your code segments the image into three classes using FCM clustering thresholding based on 3-class Fuzzy clustering,where by the threshold is obtained by averaging the maximum
in the class with the smallest center and the minimum in the class with the middle center. am i right or wrong about your method?.hope to hear from you.chao
Am currently working on flood detection where am supposed to segment the two images , classify them independently and then later obtain a change image.am woundering how i can use to this same method to segment my images and how i can calculate mean for each segment in the image
and also when i try to run this code on my landsat image,it gives this error
??? Index exceeds matrix dimensions.
Error in ==> srm at 66
For all of you having trouble with
srm_boundarygradient or any other of the mex-files: you are probably running Matlab in x64-mode and therefore Matlab will not run the included 32bit files.
On Windows the easiest will be to install a gcc-toolchain since compilation with MSVC seems to fail. A nice how-to has been posted here: http://stackoverflow.com/questions/8552580/using-gccmingw-as-matlabs-mex-compiler
Once I changed the toolchain, mex64-files were successfully created and the code works flawlessly. Thank you!