I have a multiple plants in a single binary image. How would I identify each leaf in the image assuming that each leaf is approximately elliptical?
example input: http://i.imgur.com/BwhLVmd.png
I was thinking a good place to start would be finding the tip of each leaf and then getting the center of each plant. Then I could fit the curves starting from the tip and then going to the center. I've been looking online and saw something involving a watershed method, but I do not know where to begin with that idea.
You should be aware that these things are tricky to get working robustly - there will always be a failure case.
This said, I think your idea is not bad.
You could start as follows:
Identify the boundary curve of each plant (i.e. pixels with both foreground and background in their neighbourhood).
Compute the centroid of each plant.
Convert each plant boundary to a polar coordinate system, with the centroid as the origin. This amounts to setting up a coordinate system with the distance of each boundary curve point on the Y axis and the angle on the X axis.
In this representation of the boundary curve, try to identify maxima; these are the tips of the leaves. You will probably need to do some smoothing. Use the parts of the curve before and after the maxima the start fitting your ellipses or some other shape.
Generally, a polar coordinate system is always useful for analysing stuff thats roughly circular.
To fit you ellipses, once you have a rough initial position, I would probably try an EM-style approach.
I would do something like this (I is your binary image)
I=bwmorph(bwmorph(I, 'bridge'), 'clean');
SK=bwmorph(I, 'skel', Inf);
endpts = bwmorph(SK,'endpoints');
props=regionprops(I, 'All');
And then connect every segment from the centroids listed in props.centroid to the elements of endpts that should give you your leaves (petals?).
A bit of filtering is probably necessary, bwmorph is your friend. Have fun!
Related
I have image of robot with yellow markers as shown
The yellow points shown are the markers. There are two cameras used to view placed at an offset of 90 degrees. The robot bends in between the cameras. The crude schematic of the setup can be referred.
https://i.stack.imgur.com/aVyDq.png
Using the two cameras I am able to get its 3d co-ordinates of the yellow markers. But, I need to find the 3d-co-oridnates of the central point of the robot as shown.
I need to find the 3d position of the red marker points which is inside the cylindrical robot. Firstly, is it even feasible? If yes, what is the method I can use to achieve this?
As a bonus, is there any literature where they find the 3d location of such internal points which I can refer to (I searched, but could not find anything similar to my ask).
I am welcome to a theoretical solution as well(as long as it assures to find the central point within a reasonable error), which I can later translate to code.
If you know the actual dimensions, or at least, shape (e.g. perfect circle) of the white bands, then yes, it is feasible and possible.
You need to do the following steps, which are quite non trivial to do, and I won't do them here:
Optional but extremely suggested: calibrate your camera, and
undistort it.
find the equation of the projection of a 3D circle into a 2D camera, for any given rotation. You can simplify this by assuming the white line will be completely horizontal. You want some function that takes the parameters that make a circle and a rotation.
Find all white bands in the image, segment them, and make them horizontal (rotate them)
Fit points in the corrected white circle to the equation in (1). That should give you the parameters of the circle in 3d (radious, angle), if you wrote the equation right.
Now that you have an analytic equation of the actual circle (equation from 1 with parameters from 3), you can map any point from this circle (e.g. its center) to the image location. Remember to uncorrect for the rotations in step 2.
This requires understanding of curve fitting, some geometric analytical maths, and decent code skills. Not trivial, but this will provide a solution that is highly accurate.
For an inaccurate solution:
Find end points of white circles
Make line connecting endpoints
Chose center as mid point of this line.
This will be inaccurate because: choosing end points will have more error than fitting an equation with all points, ignores cone shape of view of the camera, ignores geometry.
But it may be good enough for what you want.
I have been able to extract the midpoint by fitting an ellipse to the arc visible to the camera. The centroid of the ellipse is the required midpoint.
There will be wrong ellipses as well, which can be ignored. The steps to extract the ellipse were:
Extract the markers
Binarise and skeletonise
Fit ellipse to the arc (found a matlab function for this)
Get the centroid of the ellipse
hsv_img=rgb2hsv(im);
bin=new_hsv_img(:,:,3)>marker_th; %was chosen 0.35
%skeletonise
skel=bwskel(bin);
%use regionprops to get the pixelID list
stats=regionprops(skel,'all');
for i=1:numel(stats)
el = fit_ellipse(stats(i).PixelList(:,1),stats(i).PixelList(:,2));
ellipse_draw(el.a, el.b, -el.phi, el.X0_in, el.Y0_in, 'g');
The link for fit_ellipse function
Link for ellipse_draw function
I am doing some work related to eye images.
I did edge detection to it. The edge is like a curve and not continuous. I have to assume it to be continuous and find normals to that curve. How do I find the normals to it using MATLAB?
you can see the image below.
I want to find the normals to the upper curve.
I hope that I was clear enough.
Even though it seems unintuitive, the edge direction at every pixel is a pretty good estimate of the normal. This would be the simplest solution, because it doesn't involve any curve fitting.
In MATLAB, you can find pixel-wise edge directions using the Sobel filter:
[BW,thresh,gv,gh] = edge(I,'sobel');
edgeDir = atan2(gv, gh);
This gives you the edge directions as angles in radians.
You may want to consider curve fitting (MSE based or some other criteria) to the data. I believe a second order will do good for the upper curve, and once you have a model you can can calculate the tangent and normal at each point.
As Zaphod recommended the normal is perpendicular to the edge. You don't need to do curve fitting, you can use back projection to identify the focal point of the curve.
Start at each edge point along the curve and draw a line from curve in the direction of the normal. Draw the line by incrementing the value of each pixel the line passes through. Once you do this for all the edges you would hope to find two pixels with higher values then the rest, one for each of your curves. You should then know by there locations which is the focal point for each curve.
I want to get a metric of straightness of contour in my binary image (relatively faster). The image looks as follows:
Now, the contours in the red box are the ones which I would like to be removed preferably. Since they are not straight. These are the things I have tried. I am as of now implementing in MATLAB.
1.Collect row and column coordinates of each contour and then take derivative. For straight objects (such as rectangle), derivative will be mostly low with a few spikes (along the corners of the rectangle).
Problem: The coordinates collected are not in order i.e. the order in which the contour will be traversed if we imaging it as a path. Therefore, derivative gives absurdly high values sometimes. Also, the contour is not absolutely straight, its an output of edge detection algorithm, so you can imagine that there might be some discontinuity (see the rectangle at the bottom, human eye can understand that it is a rectangle though it is not absolutely straight).
2.Tried to think about polyfit, but again this contour issue comes up. Since its a rectangle I don't know how to apply polyfit to that point set.
Also, I would like to remove contours which are distributed vertically/horizontally. Basically this is a lane detection algorithm. So lanes cannot be absolutely vertical/horizontal.
Any ideas?
You should look into the features of regionprops more. To be fair I stole the script from this answer, but here it is:
BW = imread('lanes.png');
BW = im2bw(BW);
figure(1),
subplot(1,2,1);
imshow(BW);
cc = bwconncomp(BW);
l = labelmatrix(cc);
a_rp = regionprops(CC,'Area','MajorAxisLength','MinorAxislength','Orientation','PixelList','Eccentricity');
idx = ([a_rp.Eccentricity] > 0.99 & [a_rp.Area] > 100 & [a_rp.Orientation] < 70 & [a_rp.Orientation] > -90);
BW2 = ismember(l,find(idx));
subplot(1,2,2);
imshow(BW2);
You can mess around with the properties. 'Orientation', 'Eccentricity', and 'Area' are probably the parameters you want to mess with. I also messed with the ratios of the major/minor axis lengths but eccentricity basically does this (eccentricity is a measure of how "circular" an ellipse is). Here's the output:
I actually saw a good video specifically from matlab for lane detection using regionprops. I'll try to see if I can find it and link it.
You can segment your image using bwlabel, then work separately on each bwlabel connected object, using find. This should help solve your order problem.
About a metric, the only thing that come to mind at the moment is to fit to an ellipse, and set the a/b (major axis/minor axis) ratio (basically eccentricity) a parameter. For example a straight line (even if not perfect) will be fitted to an ellipse with a very big major axis and a very small minor axis. So say you set a ratio threshold of >10 etc... Fitting to an ellipse can be done using this FEX submission for example.
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).
the output of some processing consists of a binary map with several connected areas.
The objective is, for each area, to compute and draw on the image a line crossing the area on its longest axis, but not extending further. It is very important that the line lies just inside the area, therefore ellipse fitting is not very good.
Any hint on how to do achieve this result in an efficient way?
If you have the image processing toolbox you can use regionprops which will give you several standard measures of any binary connected region. This includes
You can also get the tightest rectangular bounding box, centroid, perimeter, orientation. These will all help you in ellipse fitting.
Depending on how you would like to draw your lines, the regionprops function also returns the length for major and minor axes in 2-D connected regions and does it on a per-connected-region basis, giving you a vector of axis lengths. If you specify 4 neighbor connected you are fairly sure that the length will be exclusively within the connected region. But this is not guaranteed since `regionprops' calculates major axis length of an ellipse that has the same normalized second central moment as the connected region.
My first inclination would be to treat the pixels as 2D points and use principal components analysis. PCA will give you the major axis of each region (princomp if you have the stat toolbox).
Regarding making line segments and not lines, not knowing anything about the shape of these regions, an efficient method doesn't occur to me. Assuming the region could have any arbitrary shape, you could just trace along each line until you reach the edge of the region. Then repeat in the other direction.
I assumed you already have the binary image divided into regions. If this isn't true you could use bwlabel (if the regions aren't touching) or k-means (if they are) first.