is it possible to find holes in connected components, i.e in objects in an image. if so, can we also count holes? Like, I have used cc = bwlabel(image); to do connected components labeling. Now, how to find number of holes in each object ?
You could use the Euler characteristic. From the Matlab documentation:
The bweuler function returns the Euler number for a binary image. The Euler number is a measure of the topology of an image. It is defined as the total number of objects in the image minus the number of holes in those objects. You can use either 4- or 8-connected neighborhoods.
But be aware that a single pixel "hole" can change the Euler characteristic. You might want to use some opening/closing to smooth object outlines before using bweuler.
A hole is the presence of nothing, so you can just invert the image and then count connected components.
Related
I have two binary images, each of which have a single white filled parallelogram and a black background. The only difference between the two images is that the parallelograms are in different locations and are slightly different from one another in shape. All the parameters between the two images are the same except for that one change.
I want to check how similar the shape of the two parallelograms are, by using some sort of comparing measure.
I looked into ssimval function in MATLAB but it seems to be taking the whole image into consideration rather than just the white blobs. Is there any other function I can use for this purpose?
For visually checking similarity, you can plot their probability density function and for numeric similarity, compute some similarity measure, such as, KL Divergence, etc.
In a simple way, you can segment your binary image with simple bwlabel function. Then use regionprops function to find perimeter and area of your desire segment. Moreover, center of region is also another comparison point.
You could do it with polygons, by using the polyshape class.
First convert the binary mask to a set of corner points. You can do it with a convex hull, by calling regionprops(bwI, 'ConvexHull').
Then convert the corner points into polygons, by calling polyshape.
Finally measure the dissimiliarities of the polygons by measuring their turning distance. Turning distance is rotation- and scaling invariant, so you might want to add additive terms to your distance metric for those if your problem demands it.
A very simple solution for comparing two binary image is using boolean operations.
Your images contains zero and one values. so If you use boolean operation.
suppose your two images are : B1 , B2
C = B1 & (~B2)
if sum(C(:))==0
% two image are same
else
% two image are different
end
I am trying to erode objects in a binary image such that they do not become smaller than some fixed size. Consider, for instance, a binary map composed of connected components (blobs), wherein one defines blob size by either the minimal or maximal antipolar (anti-perimetric) distance (i.e., the distance between two points that are as far from one another as they can be on the perimeter or contour of the blob; if the contour consists of N consecutively numbered points, then the distances evaluated would be those between points 1 and N/2+1, points 2 and N/2+2, etc.). Given such an arrangement, I seek to erode these blobs until the distance metric reaches a specified limit. If the blobs were simple circles, then the effect could be realized by ultimate erosion followed by dilation to a fixed size; however, the contour of an irregular object would be lost by such a procedure. Is there a way to achieve such an effect for connected, irregular components using built-in functions in MATLAB?
With no image and already tried code presented I can understand you wrong, but may be iterative using bwmorph with 'thin','skel' or 'shrink' will help you.
while(cond < cond_threshold)
bw=bwmorph(bw,...,1); %one of the options above
cond = calc_cond(bw);
end
I am using imregionalmax to create a binary image BW that identifies the regional maxima in my image.
Next I want to use regionprops with property WeightedCentroid to identify the coordinates of the centroid centers in the image. However, imregionalmax returns a binary image with very small connected components, which need to be increased in dimension to enable regionprops to weigh the centroid properly.
Possible solutions:
I believe the ideal situation would be to interrupt the regionprops operation at each iteration, and simply increase the size of the current connected component that it is working with by adding a couple of pixels in height and width to it.
In case this is not possible, a work around could be to split BW into an image stack with only a single connected component in each slice, expand each component by some pixels, and run regionprops individually on each image slice. This does not seem like a efficient way of solving this though.
Is there another more efficient way, and how would I implement that?
** I am aware that one way of increasing the connected components in BW is to use imdilate, but this will lead to unconnected components becoming connected.
** Another option is to use bwmorph with property thicken, which performs very well, however in a case where multiple components are close together, the size cannot be increased in one direction and reduce the performance of WeightedCentroid.
You cannot increase measurement accuracy by extending what you want to measure...
Centroid is simply the average of all region coordinates.
WeightedCentroid only takes intensities into account in case you don't have a binary image.
If you increase your object by whatever algorithm you like you risk shifting your centroid away from its true position!
I want to extract orientations of strongly unclosed edges from a binary image. The image consists of blobs, blob rows and unsharp edges as shown below. In the end every pixel should be assigned to an information about the orientation of the edge. If the existence of an edge is not confident the point should not be assigned. Parameters of a line or a whole curve would be fine but are not necessarily needed. The edges to be found are marked as red curves:
I tried a lot and I hope for some hints in regarding to methods I could use.
Hough Transformation with Lines: Because of the existence of curves as well as point clouds it is difficult to extract the relevant extreme values of the HT.
Hough Transformation with Ellipses: Same disadvantages as ‘HT with Lines’. Plus the amount of curves and point arrangements to be detected exceeds the limits of a fast process.
Local masks: Go from pixel to pixel and estimate the orientation with the help of a directed mask (Example: Count all white pixels for every considered direction and make a decision in regarding to the highest number of found pixels). By using this method the view on bigger structures like whole blob rows is obscured. It is easy to see that this method will fail in clouds an edge goes through.
I guess an estimation of the orientation by considering local and global information is the only way. I need to know something about the connectivity of these blobs before making local decisions.
Btw, I am using MATLAB.
What about using image moments? you can calculate the angle, mayor axis, and eccentricity of each single blob and define parameters to merge interceeding ones.
You can use the regionprops() or start from scratch with this code I just so happend to have here:
function M=ImMoment(Image,ii,jj)
ImSize=size(Image);
M=0;
for k=1:ImSize(1);
for l=1:ImSize(2);
M=M+k^ii*l^jj*Image(k,l);
end
end
end
and for the covariance matrix:
function [Matrix,Centroid,Angle,Len,Wid,Eccentricity]=CovMat(Image)
Centroid=[ImMoment(Image,0,1)/ImMoment(Image,0,0),...
ImMoment(Image,1,0)/ImMoment(Image,0,0)];
Miu20=ImMoment(Image,0,2)/ImMoment(Image,0,0)-Centroid(1)^2;
Miu02=ImMoment(Image,2,0)/ImMoment(Image,0,0)-Centroid(2)^2;
Miu11=ImMoment(Image,1,1)/ImMoment(Image,0,0)-Centroid(1)*Centroid(2);
Matrix=[Miu20,Miu11
Miu11,Miu02];
Lambda1=(Miu20+Miu02)/2+sqrt(4*Miu11^2+(Miu20-Miu02)^2)/2;
Lambda2=(Miu20+Miu02)/2-sqrt(4*Miu11^2+(Miu20-Miu02)^2)/2;
Angle=1/2*atand(2*Miu11/(Miu20-Miu02));
Len=4*sqrt(max(Lambda1,Lambda2));
Wid=4*sqrt(min(Lambda1,Lambda2));
Eccentricity=sqrt(1-Lambda2/Lambda1);
end
Play a little bit around with that, I'm pretty sure that should work.
I am writing a matlab code that takes in a photo and detects the circular object. For example, the function takes a picture of a peach (circular object) as an input and will return the same image with the peach circled.
Currently, I am using hough transform, utilizing imfindcircles function. However, this function requires me to specify radius range and some sort of sensitivity/threshold value. These values differ for different sizes of image and round objects. So, to get the desired output, I will have to manually change these values for each input image, which is not what I want. I'm going to use this function on 100+ images, so it's impossible for me to do this manually.
My question is is there any way I can make my circular object detection function less manual and possibly completely automatic (does not require me to input any values, just the image)?
Complexity of circle detection
The Hough transform is a voting procedure that requires assumptions be made about the minimum and maximum radii of your circles. Generally speaking using the Randomized Hough Transform for Circles you would pick three-points and then try to form a circle and check if the radius is within the desired range. Running this for a good number of iterations you should find peaks (multiple hits) in your accumulator matrix that represent circles. If you didn't make any assumptions about object size I think it is obvious this method wouldn't work.
Do some routine pre-processing to adjust for contrast and brightness e.g. contrast stretching, histogram equalization. If you might have some noise in the images, then apply bit of gaussian smoothing as well.
Normalizing images this way will reduce inter-image variance and help you with setting thresholds.
the Hough Transform can be used to detect circles, lines, etc.You can refer the demos in Matlab. There are several cases for the application of Hough Transform.