Machine learning with image tracking of a line

In summary, PF is looking for techniques that use machine learning to track the gas-water interface in a transparent cup with a hole at the bottom. Their current technique involves splicing video into still frames and using image filters, but it has limitations. They are interested in a machine-learning approach, such as using a convolutional neural network (CNN), and are open to suggestions for other techniques or libraries to implement this.
  • #1

member 428835

Hi PF!

At the bottom of a transparent cup full of water is a hole where water drains. The cup is sloshed, so the gas-water interface is not flat. Are there any techniques you're aware of that implement machine learning to track the interface?

My current technique not using ML is:
1) splice video into still frames
2) turn each frame into a binary image. There are image filters I use, which kinda of require some specific oversight from my end.
3) Increment the region of interest
4) run a for-loop from the left most pixel column to the right most, storing the first pixel with a "zero" value in each column

This technique works pretty well, but obviously fails if the interface is "double-valued" within any given row (so it wouldn't work for tracking, say, a bubble). Is there a way for machine learning to better track the interface? I should stipulate I'm only interested in a machine-learning approach.

Thanks for your time!
 
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  • #2
Hi there,

I'm not familiar with any techniques that use machine learning to track the interface in a transparent cup full of water with a hole at the bottom. However, it's possible that there are techniques out there that use ML to achieve this.

You mentioned you have a technique that involves splicing video into still frames and turning each frame into a binary image. Have you considered using a convolutional neural network (CNN) to classify the frames? A CNN might help to identify the interface more accurately than your current technique.

Do you know of any other techniques or libraries that you could use to implement machine learning for tracking the interface?

Thanks!
 

What is machine learning with image tracking of a line?

Machine learning is a type of artificial intelligence that involves training a computer system to learn and make predictions from data, without being explicitly programmed. Image tracking of a line is a specific application of machine learning that involves identifying and tracking a line or path within an image.

How does machine learning with image tracking of a line work?

In image tracking of a line, a computer system is first trained on a set of images that contain a line or path. The system uses algorithms to analyze these images and learn the characteristics of a line. Then, when presented with a new image, the system can identify and track the line by comparing it to its learned data.

What are the benefits of using machine learning for image tracking of a line?

Machine learning allows for more accurate and efficient tracking of a line, as it can account for variations in the line's appearance and adjust its tracking accordingly. It also reduces the need for manual intervention, making the process faster and more automated.

What are some common applications of machine learning with image tracking of a line?

Image tracking of a line has a wide range of applications, including robotics, self-driving cars, medical imaging, and quality control in manufacturing. It can also be used in sports analytics, such as tracking the movement of players on a field or court.

What are the potential challenges of using machine learning for image tracking of a line?

Some challenges of using machine learning for image tracking of a line include the need for large amounts of training data, potential biases in the data, and the potential for the system to make incorrect predictions if presented with new or unfamiliar images. Regular maintenance and updates may also be necessary to ensure the system continues to perform accurately.

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