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Algorithm for determining the size of air bubbles from image

I'm looking for a good way to isolate an air bubble from the following image. I'm using Visual Studio 2015 and C#.
I've heard of the watershed method and believe it may be a good solution.
I tried implementing the code solution found here: watershed image segmentation
I haven't had much success. The solution has trouble finding functions, for example: FilterGrayToGray.
Does anyone know of a good way to do this?

You should just train a Neural network to recognize parts of image when there are no bubbles (in example groups of 16x16 pixels). Then when recognizing a square is not successfull you do a burst of horizontal scanlines and you register where the edge starts and ends. You can determine pretty precisely the section of a bubble (however determine its volume needs to keep into account surface curvature, wich is possible but more hard) on the image. If you have the possibility to use more cameras you can triangulate more sections of a bubble and get a precise idea of real volume. As another euristic to know bubble size you can also use the known volume throughput, so you know that if in a time interval you emitted X liters of air, and the bubbles have sections given in a certain proportion you can redistribute total volume across bubbles and further increase precision (of course you have to keep in mind pressure since bubbles on bottom of the pool will be more little).
As you see you can play with simple algorithms like gaussian difference and contrast to achieve different quality results.
In the left picture you can easily remove all background noise, however you have lost now part of the bubbles. It is possible you can re-gain the missed bubbles edge by using a different illumination on the pool
In the right picture you have the whole bubbles edges, but now you also have more areas that you need to manually discard from picture.
As for edge detections algorithm you should use an algorithm that do not add a fixed offset to edges (like convolution matrix or laplace), for this I think gaussian difference would work best.
Keep all intermediate data so one can easily verify and tweak the algorithm and increase its precision.
EDIT:
The code depends on wich library you use, you can easily implement Gaussian Blur and Horizontal Scanline, for Neural Networks there are already c# solutions out there.
// Do gaussian difference
Image ComputeGaussianDifference (Image img, float r1, float r2){
Image img = img.GaussianBlur( r1);
Image img2 = img.GaussianBlur( r2);
return (img-img2).Normalize(); // make values more noticeable
}
more edits pending.. try do document yourself in the meantime, I already given enough trace to let you do the job, you just need basic understanding of simple image processing algorithms and usage of ready neural networks.

Just in case if you are looking for some fun - you could investigate Application Example: Photo OCR. Basically you train one NN to detect bubble, and try it on a sliding window across the image. When you capture one - you use another NN, which is trained to estimate bubble size or volume (you probably can measure your air stream to train the NN). It is not so difficult as it sounds, and provides very high precision and adaptability.
P.S. Azure ML looks good as a free source of all the bells and whistles without need to go deep.

To solutions come to mind:
Solution 1:
Use the Hough transform for circles.
Solution 2:
In the past I also had a lot of trouble with similar image segmentation tasks. Basically I ended up with a flood fill, which is similar to the watershed algorithm you programmed.
A few hat tricks that I would try here:
Shrink the image.
Use colors. I notice you're just making everything gray; that makes little sense if you have a dark-blue background and black boundaries.

Do you wish to isolate the air bubble in a single image, or track the same air bubble from an image stream?
To isolate a 'bubble' try using a convolution matrix on the image to detect the edges. You should pick the edge detection convolution based on the nature of the image. Here is an example of a laplace edge detection done in gimp, however it is faily straight forward to implement in code.
This can help in isolating the edges of the bubbles.
If you are tracking the same bubble from a stream, this is more difficult due to as the way bubbles distort when flowing through liquid. If the frame rate is high enough it would be easy to see difference from frame to frame and you can judge which bubble it is likely to be (based on positional difference). i.e you would have to compare current frame to previous frame and use some intelligence to attempt to work out which bubble is the same from frame to frame. Using a fiducial to help give a point of reference would be useful too. The nozzle at the bottom of the image might make a good one, as you can generate a signature for it (nozzle won't change shape!) and check that each time. Signatures for the bubbles aren't going to help much since they could change drastically from one image to the next, so instead you would be processing blobs and their likely location in the image from one frame to the next.
For more information on how convolution matrices work see here.
For more information on edge detection see here.
Hope this helps, good luck.

how to detect an object with the same color as the background

I'm new with EMGU and image processing and I have a project in C# that needs to detect a transparent object, specifically, a moth's wing inside a plastic bottle. Here are some examples.
I tried using YCbCr in EMGU but I can not detect it nor differentiate it from the background.
Another thing is that I tried to enclose it in a "controlled environment" (inside a box where no light can come in) and used LED back-light. Is this advisable? Or can light from the environment (fluorescent light) will do? Will this affect the detection rate? Do lighting play a factor in this kind of problem?
This is the idea of my project and what I use. Basically, my project is just a proof of concept about detecting a transparent object from an image using a webcam (Logitech C910). This is an example of an old industrial problem here in our country when bottling plant over stock their plastic bottle and it got contaminated before use. Moth body and moth wing are the contaminants that were given to us. Also, this is to see if a webcam can suffice as an alternative to an industrial camera for this application.
I place it inside a controlled environment and use LED lights as backlight (this is just made using a prototyping board and high intensity LED light that is diffused with a bond paper). The object (moth wing) will be placed inside a plastic bottle with water and will be tested into 2 parts. The first part is that the bottle is not moving and the second part is when the bottle is moved on a conveyor but at the same controlled environment. I did all the hardware required so that is not an issue anymore. The moth body is manageable (I think) to detect but the moth wing left me scratching my head.
Any help would be very much appreciated. Thank you in advance!

Consider using as many visual cues as possible:
blur/focus
shape - you can use active contour or findControus() on a clean image
location, intensity, and texture in grabcut framework
you can try IR illumination in case moth and glass react to it differently

You should try to adjust brightness/contrast and color balance.
Another idea is to use auto threshold such as Sauvola or auto local thresholds. It will give you interesting results such as this one (I directly convert the image to grayscale) :
I do this tests very quickly by using imageJ.
Click to the link to the image in order to see which image correspond to which binarization algorithm.

People Counting System

I want to develop a "People Counting System" using OpenCV (or Emgu CV).
Please guide me on how to implement or lead me to some examples or open source projects.
(I have done some work: extracting diff then threshold to delete background, using motion history and like that; still no good results.)
Edit 1: I am counting a high people flow (a dozen of them may come through simultaneously).
Edit 2: It must be at least 80% accurate. People are walking through a door that is almost 5 meters wide. The problem is I have no control on the position or angle of the camera. Camera is shouting the place from a 10m distance at a 2.5m height.
Thank you

If you call a people counting system a system that counts people that are in a room then I recommend you implement the hardware with a microcontroller with 2 lazers(normal lazer toys work) and 2 photoresistors.For the microcontroller I recomen you use Arduino.And then make an C# application that has a SerialPort object and reads the data that the arduino sends through the USB.The arduino will send 1 for "someone entered the room" and 0 for "someone left the room" for example.Then the logging and statistics can be done easily in C#.
Arduiono Site:here
Photoresistor for $1: here
This solution is alot cheaper and easyer to implement than using a camera that is with a fairly good quality.
Hope I helped you.

Check out the HOG pedestrian detector that comes with recent versions of OpenCV (>= 2.2).
See modules/objdetect/src/hog.cpp and samples/cpp/peopledetect.cpp in the OpenCV sources. Unfortunately there is no official documentation about it yet.

This would help you to count moving things including people: Motion Detection project on CodeProject

Are people the only kind of "entities" in the scene? If this is not the case, do you care about considering a person some other kind of thing that moves through the scene? Because if that is the case, you could just count blobs that come in or come out from the scene. It may sound a bit naive but I will take some kind of motion image, group motion pixels by distance in clusters. Your distance metric could take into account some restrictions, such as that people will "often" stand so pixels in a cluster should group around some kind of regression line (an straight-up line if the camera is aligned with de floor). It shouldn't be necessary to track them in the scene, just noticing when they enter or they leave, though you'd get some issues with, for example, people entering on their own in the scene and leaving in pairs or in groups... Good luck :)

I think if you have dense people crowd with a lot of occlusions you have to use some machine learning algorithm, for example you can use Implicit Shape Model for features.

It really depends on the position of the camera. Assuming that you can get front facing profiles of the people in the images:
This problem is basically face detection and recognition.
There are many ways to go about finding faces, but this is the approach that I'm a little more familiar with.
For the face detection you need to do image segmentation on the skin tone color. This will extract skin regions. [Arms, the chest (for those wearing V cut tops), face, legs, etc] Then you would need to line up the profiles of the skin regions to the profile of your trained faces.
[You'll need to use Eigenfaces to create a generic profile of what a face looks like]
If the skin region lines up and doesn't devate too far from the profile, then it is considered a face. Once the face is confirmed, then add it into the eigenfaces data store [for recognition]. To save processing you might want to consider limiting the search area if you are looking for a previous face. [Given the frame rate, and last time the person was seen]
If you are referring to "Crowd flow" I think you just mean the density of faces in a crowd.
Now you've confirmed that a moving object in the video is a person. Now you just need to note that and then make sure that you don't consider them as a new person again.
This approach: Really depends on your ability to detect face regions. This may not work if the people in the video are looking down, not fitting the profile of the trained data etc. Also it may be effected if a person puts on sunglasses within the video. [Probably would be considered a "new face"]

C# Create "wireframe"/3D "map"

image http://prod.triplesign.com/map.jpg
How can I produce a similar output in C# window forms in the easiest way?
Is there a good library for this purpose?
I just needs to be pointed in the direction of which graphic library is best for this.

You should just roll your own in a 3d graphics library. You could use directx. If using WPF it is built-in, you can lookup viewport3d. http://msdn.microsoft.com/en-us/magazine/cc163449.aspx
In graphics programming what you are building is a very simple version of a heightmap. I think building your own would give your greater flexibility in the long run.

So a best library doesn't exist. There are plenty of them and some are just for different purposes. Here a small list of possibilities:
Tao: Make anything yourself with OpenGL
OpenTK: The successor of the Tao framework
Dundas: One of the best but quite expensive (lacks in real time performance)
Nevron: Quite good, but much cheaper (also has problems with real time data)
National Instruments: Expensive, not the best looking ones, but damn good in real time data.
... Probably someone else made some other experiences.

Checkout Microsoft Chart Controls library.

Here's how I'd implement this using OpenGL.
First up, you will need a wrapper to import the OpenGL API into C#. A bit of Googling led me to this:
CsGL - OpenGL .NET
There a few example programs available to demonstrate how the OpenGL interface works. Play around with them to get an idea of how the system works.
To implement the 3D map:
Create an array of vectors (that's not the std::vector/List type but x,y,z triplets) where x and y are along the horizontal plane and z is the up amount.
Set the Z compare to less-than-or-equal (so the overlaid line segments are visible).
Create a list of quads where the vertices of the quads are taken from the array in (1)
Calculate the colour of the quad. Use a dot-product of the quad's normal and a light source direction to get a value to shade value, i.e. normal.light of 1 is black and -1 is white.
Create a list of line segments, again from the array in (1).
Calculate the screen position of the various projected axes points.
Set up your camera and world->view transform (use the example programs to get an idea of how to do this).
Render the quads and lines, OpenGL will do the transformation from world co-ordinates (the list in (1)) to screen space. Draw the labels, you might not want to do this using OpenGL as the labels shouldn't scale with distance from camera, otherwise they could get too small to read.
Since the above is quite a lot of stuff, there isn't really the space (and time on my part) to post working code (but someone else might add something if you're lucky). You could break the task down and ask questions on the parts you don't quite understand.

Have you tried this... gigasoft data visualization tools (Its not free)
And you can checkout the online wireframe demo here

Rendering graphics in C#

Is there another way to render graphics in C# beyond GDI+ and XNA?
(For the development of a tile map editor.)

SDL.NET is the solution I've come to love. If you need 3D on top of it, you can use Tao.OpenGL to render inside it. It's fast, industry standard (SDL, that is), and cross-platform.

Yes, I have written a Windows Forms control that wraps DirectX 9.0 and provides direct pixel level manipulation of the video surface.
I actually wrote another post on Stack Overflow asking if there are other better approaches: Unsafe C# and pointers for 2D rendering, good or bad?
While it is relatively high performance, it requires the unsafe compiler option as it uses pointers to access the memory efficiently. Hence the reason for this earlier post.
This is a high level of the required steps:
Download the DirectX SDK.
Create a new C# Windows Forms project and reference the installed
Microsoft DirectX assembly.
Initialize a new DirectX Device object with Presentation Parameters
(windowed, back buffering, etc.) you require.
Create the Device, taking care to record the surface "Pitch" and
current display mode (bits per pixel).
When you need to display something, Lock the backbuffer
surface and store the returned pointer to the start of surface
memory.
Use pointer arithmetic, calculate the actual pixel position in the
data based on the surface pitch,
bits per pixel and the actual x/y pixel coordinate.
In my case for simplicity I am sticking to 32 bpp, meaning setting a pixel is as simple as: *(surfacePointer + (y * pitch + x))=Color.FromARGB(255,0,0);
When finished drawing, Unlock the back buffer surface. Present the surface.
Repeat from step 5 as required.
Be aware that taking this approach you need to be very careful about checking the current display mode (pitch and bits per pxiel) of the target surface. Also you will need to have a strategy in place to deal with window resizing or changes of screen format while your program is running.

Managed DirectX (Microsoft.DirectX namespace) for faster 3D graphics. It's a solid .NET wrapper over DirectX API, which comes with a bit of performance hit for creating .NET objects and marshalling. Unless you are writing a full featured modern 3D engine, it will work fine.
Window Presentation Foundation (WPF) (Windows.Media namespace) - best choice for 2D graphics. Also has limited 3D abilities. Aimed to replace Windows Forms with vector, hardware accelerated resolution-independent framework. Very convenient, supports several flavours of custom controls, resources, data binding, events and commands... also has a few WTFs. Speed is usually faster than GDI and slower than DirectX, and depends greatly on how you do things (seen something to work 60 times faster after rewriting in a sensible way). We had a success implementing 3 1280x1024 screens full of real-time indicators, graphs and plots on a single (and not the best) PC.

You could try looking into WPF, using Visual Studio and/or Expression Blend. I'm not sure how sophisticated you're trying to get, but it should be able to handle a simple editor. Check out this MSDN Article for more info.

You might look into the Cairo graphics library. The Mono project has bindings for C#.

Cairo is an option. I'm currently rewriting my mapping software using both GDI+ and Cairo. It has a tile map generator, among other features.