Exploring Image Segmentation: Sharing My Second Computer Vision Exercise

Hi everyone,

I just realized it’s been almost a month since I posted my first exercise! I was a bit hesitant to share this one, it actually took me two weeks to decide—but I thought I’d go ahead and post it anyway. This time, I'm experimenting with image segmentation, as part of a series of seven exercises I’m working on to dive into computer vision.

For those who read my previous post, you might remember I’m following some ideas ChatGPT suggested for practical exercises in computer vision. I was unsure about sharing this one because I didn’t always get the outcome I expected: in some images, I managed to capture the entire edge of the leaf, while in others, I only segmented parts of it. Still, it was an interesting challenge, and I learned a lot from the process.

Here’s what I focused on for this exercise:

2. Image Segmentation

Objective: Explore techniques to divide an image into segments, or regions of interest.

Tools: OpenCV, Scikit-Image.

Exercises:

Apply thresholding techniques (binary, adaptive)
Use Otsu's method for automatic thresholding
Perform image segmentation using the Watershed algorithm
Experiment with contour detection

Example Project: Segment different objects in an image (e.g., finding and counting coins)

[Link to the notebook]

Previous post if you did not read it: post

By the way, my next exercise might take a bit longer as I am working on another project that I expect to share soon.

I hope you all have a great week ahead! And please, if you have any comments on my notebook, I’d love to hear your thoughts on what I could improve or try differently.

2 comments