L0_05_Image_Segmentation_Traditional_CV

Zero - 05: Image Segmentation using Traditional CV Approach

Overview

Image segmentation using traditional computer vision approaches.

(Src: V7labs)

Requirements

• cv2
• numpy
• matplotlib
• IPython
• PIL

Usage

To run the notebook, use Jupyter notebook, Google Colab, Kaggle or any other notebook tool.

Tip

Use the following command to download image resources from github to your Colab or Kaggle environment:

!wget https://github.com/afondiel/computer-vision-hello-world-challenges/tree/main/06_Zero_Feature_Extraction_Alignment/image_files.png

Notebook	Colab	Kaggle
Go to notebook

Contributing

If you want to contribute to this project, you are welcome to do so. You can either add new projects, improve existing ones, or fix bugs and errors.

Please follow these steps to contribute:

• Fork this repository and clone it to your local machine.
• Create a new branch with a descriptive name for your contribution.
• Add your code and files to the branch and commit your changes.
• Push your branch to your forked repository and create a pull request to the main repository.
• Wait for your pull request to be reviewed and merged.

References

• An Introduction to Image Segmentation: Deep Learning vs. Traditional [+Examples] - v7labs

• Image Segmentation with Watershed Algorithm

• Guide to Image Segmentation in Computer Vision: Best Practices - Akruti Acharya - November-7-2022

• Lectures:

  • Image Segmentation Lecture - First Principles of Computer Vision