Automatic Thresholding
라이선스 없음
Dhanesh Ramachandram posted on same algorithm, march 2003.
This iterative technique for choosing a threshold was developed by Ridler and Calvard . The histogram is initially segmented into two parts using a starting threshold value such as 0 = 2B-1, half the maximum dynamic range.
The sample mean (mf,0) of the gray values associated with the foreground pixels and the sample mean (mb,0) of the gray values associated with the background pixels are computed. A new threshold value 1 is now computed as the average of these two sample means. The process is repeated, based upon the new threshold, until the threshold value does not change any more.
(quote from http://www.ph.tn.tudelft.nl/Courses/FIP/frames/fip-Segmenta.html)
New feature from the m-file of Dhanesh Ramachandram:
- one does not have to rescale one's image to a uint array. This algorithm works for negative intensities, for example.
Run:
vImage = Image(:);
[n xout]=hist(vImage, <nb_of_bins>);
threshold = isodata(n, xout)
You get a (hopefully relevant) threshold for your image.
인용 양식
Gauthier Fleutot (2024). Automatic Thresholding (https://www.mathworks.com/matlabcentral/fileexchange/5389-automatic-thresholding), MATLAB Central File Exchange. 검색됨 .
MATLAB 릴리스 호환 정보
플랫폼 호환성
Windows macOS Linux카테고리
- Image Processing and Computer Vision > Image Processing Toolbox > Image Segmentation and Analysis > Image Segmentation > Image Thresholding >
태그
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!버전 | 게시됨 | 릴리스 정보 | |
---|---|---|---|
1.0.0.0 |