Rank: 265 based on 376 downloads (last 30 days) and 4 files submitted
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Guanglei Xiong

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If you find my contribution helpful. Please consider to cite the following two papers: (1) G. Xiong, X. Zhou, L. Ji, A. Degterev, and S. Wong, Automated Neurite Labeling and Analysis in Fluorescence Microscopy Images, Cytometry Part A, Vol. 69A, No. 6, pp. 494-505, 2006. (2) G. Xiong, X. Zhou, L. Ji, P. Bradley, N. Perrimon, and S. Wong, Automated Segmentation of Drosophila RNAi Fluorescence Cellular Images using Deformable Models, IEEE Transactions on Circuits and Systems, Vol. 53, No. 11, pp. 2415-2424, 2006.

Professional Interests:
image processing, geometric modeling

 

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Files Posted by Guanglei View all
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(last 30 days)
Comments Rating
13 Jun 2006 Screenshot Local Adaptive Thresholding Threshold with local statistics, such as mean or median. Author: Guanglei Xiong filtering, threshold adaptive lo..., svm 217 27
  • 4.55
4.5 | 20 ratings
09 Sep 2005 Screenshot Gradient using first order derivative of Gaussian Output the gradient image of a grayscale image Author: Guanglei Xiong filtering, gradient gaussian, first order derivativ..., fod, image proc, gradient method 48 12
  • 4.23077
4.2 | 13 ratings
07 Sep 2005 Screenshot Local Normalization Reduce the difference of the illumination. Author: Guanglei Xiong enhancement, local normalization g..., image processing, uniformi 48 2
  • 4.75
4.8 | 4 ratings
25 Aug 2005 Screenshot Fuzzy c-means thresholding Thresholding by 3-class fuzzy c-means clustering. Author: Guanglei Xiong filtering, threshold fuzzy cmean..., kmeans, the source code, fuzzy logic 63 4
  • 4.5
4.5 | 4 ratings
Comments and Ratings on Guanglei's Files View all
Updated File Comment by Comments Rating
03 Jul 2014 Local Adaptive Thresholding Threshold with local statistics, such as mean or median. Author: Guanglei Xiong 2one

For others, it appears that C (=constant) was originally the binary image segmentation threshold.

I have modified my version of the code as follows:

replace:

sIM=mIM-IM-C;
bw=im2bw(sIM,0);

with:

sIM=mIM-IM;
thresh=graythresh(sIM);
bw=im2bw(sIM,thresh);

This now uses the adaptive (mean) filter to highlight image features (i.e. mIM-IM) and then the Otsu threshold to segment and generate a binary image.

03 Jul 2014 Local Adaptive Thresholding Threshold with local statistics, such as mean or median. Author: Guanglei Xiong 2one

this works quite well for my application but can anyone explain to me what the algorithm is doing exactly?
I understand that the algorithm generates a local mean filtered image by iterating over each pixel for user window size but what is line:

sIM=mIM-IM-C

this subtracts the original image and a constant, C, from the local mean filtered image. What is C and why do this?

28 Mar 2013 Local Adaptive Thresholding Threshold with local statistics, such as mean or median. Author: Guanglei Xiong Zheng, Kang

What is C exactly?

24 Nov 2012 Gradient using first order derivative of Gaussian Output the gradient image of a grayscale image Author: Guanglei Xiong Anwer, Atif

29 May 2012 Gradient using first order derivative of Gaussian Output the gradient image of a grayscale image Author: Guanglei Xiong GAUDANI, PRATIK

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