Particle Tracking, Image Analysis, Proper way to detect an image

Question

Alakesh Upadhyaya 2023-2-16

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https://ww2.mathworks.cn/matlabcentral/answers/1913515-particle-tracking-image-analysis-proper-way-to-detect-an-image

评论： Alakesh Upadhyaya 2023-3-6

Hi, I am new to particle tracking using MATLAB. Here's my question:

So I have 10000 frames of an video recorded at 60fps. The video is basically of a disk shaped object moving on the surface of water inside an petri dish. I want to track the x and y positions of the particle. The image looks like the following :

There's some background noise in the images, I have clean images too but I want to know how do I process the image so that it looks clean and MATLAB can detect only one particle and not the edges of the petri dish.

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Thamonwan Ongrungrueng 2023-2-24

I want to know too, please.

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Answer 1

Aditya Srikar 2023-3-2

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https://ww2.mathworks.cn/matlabcentral/answers/1913515-particle-tracking-image-analysis-proper-way-to-detect-an-image#answer_1183680

编辑：Aditya Srikar 2023-3-2

Hi Alakesh

From what you have mentioned, I understand that you want to know how to remove background noise from an image. Below are the techniques that can be used to filter and remove background noise from an image.

1) You can use the traditional noise removal techniques like linear filtering, median filtering and adaptive filtering to remove noise over an image (pixel matrix).

https://in.mathworks.com/help/images/noise-removal.html#buh9ylp-71

2) The modern image filtering techniques using Deep Learning are proved to be powerful enough to reduce background noise over an image.

=> denoisingImageDatastore:- Object that adds random noise to the image patches for each epoch, so that each epoch uses a slightly different data set. The actual number of training images at each epoch is increased by a factor of PatchesPerImage. The noisy image patches and corresponding noise patches are not stored in memory.

https://in.mathworks.com/help/images/ref/denoisingimagedatastore.html

=> Imboxfilt:- performs filtering using either convolution-based filtering or integral image filtering, using an internal heuristic to determine which filtering approach to use.

https://in.mathworks.com/help/images/ref/imboxfilt.html

Hope it helps !