Comments on: Intensity-weighted centroids

By: Steve

Steve — Mon, 19 Oct 2009 19:26:21 +0000

Matt—You might try looking at the marker-controlled watershed segmentation demo.

By: Matt

Matt — Thu, 08 Oct 2009 16:27:37 +0000

I have some separated blobs to detect. Each one should have a Gaussian shape. Usually the bwlabel works well but sometimes two blobs are found as 1 object when they should really be two objects.
What do you think to do about this? Could I detect that one feature has 2 peaks?
Thanks Matt

By: Travis

Travis — Wed, 23 Sep 2009 17:21:07 +0000

Just that intensity-weighted second moment (orientation)… for now! Thanks.

By: Steve

Steve — Wed, 16 Sep 2009 18:13:05 +0000

Travis—Your method sounds promising. Are there any specific grayscale properties you are particularly interested in? Thanks for your suggestions.

By: Travis

Travis — Wed, 16 Sep 2009 16:51:25 +0000

I believe I found a solution… By using the gray scale pixel values for intensity, the calculations for uxx, uyy, and uxy are weighted. These parameters are then used to calculate orientation the same way regionprops does for binary images.

The part of the code in regionprops for the non-weighted case is:

uxx = sum(x.^2)/N + 1/12;
uyy = sum(y.^2)/N + 1/12;
uxy = sum(x.*y)/N;

I changed this to:

uxx = sum((x.^2.*PixelWeights)/sum_region)-xbar^2;
uyy = sum((y.^2.*PixelWeights)/sum_region)-ybar^2;
uxy = sum((y.*x.*PixelWeights)/sum_region)-xbar*ybar;

Where xbar and ybar are the intensity-weighted centroids (now available as an output from regionprops as “WeightedCentroid”) and sum_region is the sum of the gray level area. Hopefully my approach is correct… it seems to make sense on some test images.

It would be nice to have regionprops expanded to include more properties for grayscale images. Thanks for the input.

By: Steve

Steve — Wed, 16 Sep 2009 12:13:01 +0000

Travis—What is your definition of intensity-weighted orientation? For “flat” (nonweighted) regions, regionprops defines object orientation as the orientation of the major axis of an ellipse that has the same second-order moments as the region. It’s not obvious to me how to extend this definition to a nonflat region.

By: Travis

Travis — Tue, 15 Sep 2009 18:00:06 +0000

I found this post useful for finding the intensity-weighted centroid of a gray scale image. However, I need to go one step further… is there a way to compute the intensity-weighted orientation of an object (or region) in a gray scale image?

Thanks,
Travis

By: Steve

Steve — Mon, 06 Jul 2009 15:22:11 +0000

Murat—Use rgb2gray, then threshold using the MATLAB < operator, and then label the objects using bwlabel. Use regionprops to compute measurements that might be useful in estimating the diameter (bounding box, area, perimeter, ellipse parameters).

By: murat

murat — Fri, 03 Jul 2009 08:39:04 +0000

Hi Steve,
I have an rgb image of a kind of cream and it contains some small black particles (black dots). In colormap I can see that black particles composed of 10-15 pixels (according to its size) and in grayscale usually they have a pixel value smaller than 100 (in 0-255). So first I want to label all particles in the image which have a average pixel value under 100, count them (how many dots in my image) and finally measure the approximate diameter of these dots. Thank you in advance Steve..
regards

By: Steve

Steve — Mon, 29 Jun 2009 12:39:40 +0000

Shahn—My November 17, 2006 post shows you how to do it.