Is it Possible to Find out Measurement of ISNT ( Inferior, Superior, Nasal, Temporal) Distances by Detecting Optic Cup and Optic Disc using Circular Hough Transformation from following Image? -
Over on the MathWorks file exchange. There is a function for detecting circles using the Hough Transform. You should be able to calculate distances in pixels and translate those to whatever unit you wish, so long as you have a reference to go against.
I haven't personally used the function so I can't say if it is exactly what you need, but it's a place to start.
Related
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.
I am trying to detect corners (x/y coordinates) in 2D scatter vectors of data.
The data is from a laser rangefinder and our current platform uses Matlab (though standalone programs/libs are an option, but the Nav/Control code is on Matlab so it must have an interface).
Corner detection is part of a SLAM algorithm and the corners will serve as the landmarks.
I am also looking to achieve something close to 100Hz in terms of speed if possible (I know its Matlab, but my data set is pretty small.)
Sample Data:
[Blue is the raw data, red is what I need to detect. (This view is effectively top down.)]
[Actual vector data from above shots]
Thus far I've tried many different approaches, some more successful than others.
I've never formally studied machine vision of any kind.
My first approach was a homebrew least squares line fitter, that would split lines in half resurivly until they met some r^2 value and then try to merge ones with similar slope/intercepts. It would then calculate the intersections of these lines. It wasn't very good, but did work around 70% of the time with decent accuracy, though it had some bad issues with missing certain features completely.
My current approach uses the clusterdata function to segment my data based on mahalanobis distance, and then does basically the same thing (least squares line fitting / merging). It works ok, but I'm assuming there are better methods.
[Source Code to Current Method] [cnrs, dat, ~, ~] = CornerDetect(data, 4, 1) using the above data will produce the locations I am getting.
I do not need to write this from scratch, it just seemed like most of the higher-class methods are meant for 2D images or 3D point clouds, not 2D scatter data. I've read a lot about Hough transforms and all sorts of data clustering methods (k-Means etc). I also tried a few canned line detectors without much success. I tried to play around with Line Segment Detector but it needs a greyscale image as an input and I figured it would be prohibitivly slow to convert my vector into a full 2D image to feed it into something like LSD.
Any help is greatly appreciated!
I'd approach it as a problem of finding extrema of curvature that are stable at multiple scales - and the split-and-merge method you have tried with lines hints at that.
You could use harris corner detector for detecting corners.
I'm very new to 3D image processing.i'm working in my project to find the perspective angle of an circle.
A plate having set of white circles,using those circles i want to find the rotation angles (3D) of that plate.
For that i had finished camera calibration part and got camera error parameters.The next step i have captured an image and apply the sobel edge detection.
After that i have a little bit confusion about the ellipse fitting algorithm.i saw a lot of algorithms in ellipse fit.which one is the best method and fast method?
after finished ellipse fit i don't know how can i proceed further?how to calculate rotation and translation matrix using that ellipse?
can you tell me which algorithm is more suitable and easy. i need some matlab code to understand concept.
Thanks in advance
sorry for my English.
First, find the ellipse/circle centres (e.g. as Eddy_Em in other comments described).
You can then refer to Zhang's classic paper
https://research.microsoft.com/en-us/um/people/zhang/calib/
which allows you to estimate camera pose from a single image if some camera parameters are known, e.g. centre of projection. Note that the method fails for frontal recordings, i.e. the more of a perspective effect, the more accurate your estimate will be. The algorithm is fairly simple, you'll need a SVD and some cross products.
I have a binary image, i want to detect/trace curves in that image. I don't know any thing (coordinates, angle etc). Can any one guide me how should i start? suppose i have this image
I want to separate out curves and other lines. I am only interested in curved lines and their parameters. I want to store information of curves (in array) to use afterward.
It really depends on what you mean by "curve".
If you want to simply identify each discrete collection of pixels as a "curve", you could use a connected-components algorithm. Each component would correspond to a collection of pixels. You could then apply some test to determine linearity or some other feature of the component.
If you're looking for straight lines, circular curves, or any other parametric curve you could use the Hough transform to detect the elements from the image.
The best approach is really going to depend on which curves you're looking for, and what information you need about the curves.
reference links:
Circular Hough Transform Demo
A Brief Description of the Application of the Hough
Transform for Detecting Circles in Computer Images
A method for detection of circular arcs based on the Hough transform
Google goodness
Since you already seem to have a good binary image, it might be easiest to just separate the different connected components of the image and then calculate their parameters.
First, you can do the separation by scanning through the image, and when you encounter a black pixel you can apply a standard flood-fill algorithm to find out all the pixels in your shape. If you have matlab image toolbox, you can find use bwconncomp and bwselect procedures for this. If your shapes are not fully connected, you might apply a morphological closing operation to your image to connect the shapes.
After you have segmented out the different shapes, you can filter out the curves by testing how much they deviate from a line. You can do this simply by picking up the endpoints of the curve, and calculating how far the other points are from the line defined by the endpoints. If this value exceeds some maximum, you have a curve instead of a line.
Another approach would be to measure the ratio of the distance of the endpoints and length of the object. This ratio would be near 1 for lines and larger for curves and wiggly shapes.
If your images have angles, which you wish to separate from curves, you might inspect the directional gradient of your curves. Segment the shape, pick set of equidistant points from it and for each point, calculate the angle to the previous point and to the next point. If the difference of the angle is too high, you do not have a smooth curve, but some angled shape.
Possible difficulties in implementation include thick lines, which you can solve by skeleton transformation. For matlab implementation of skeleton and finding curve endpoints, see matlab image processing toolkit documentation
1) Read a book on Image Analysis
2) Scan for a black pixel, when found look for neighbouring pixels that are also black, store their location then make them white. This gets the points in one object and removes it from the image. Just keep repeating this till there are no remaining black pixels.
If you want to separate the curves from the straight lines try line fitting and then getting the coefficient of correlation. Similar algorithms are available for curves and the correlation tells you the closeness of the point to the idealised shape.
There is also another solution possible with the use of chain codes.
Understanding Freeman chain codes for OCR
The chain code basically assigns a value between 1-8(or 0 to 7) for each pixel saying at which pixel location in a 8-connected neighbourhood does your connected predecessor lie. Thus like mention in Hackworths suggestions one performs connected component labeling and then calculates the chain codes for each component curve. Look at the distribution and the gradient of the chain codes, one can distinguish easily between lines and curves. The problem with the method though is when we have osciallating curves, in which case the gradient is less useful and one depends on the clustering of the chain codes!
Im no computer vision expert, but i think that you could detect lines/curves in binary images relatively easy using some basic edge-detection algorithms (e.g. sobel filter).