👁️ Welcome to the Computer Vision Compendium!👋🛒

1-Introduction

🚀 Explore the vast landscape of computer vision through our comprehensive repository, serving as your A-Z guide to this captivating field. Whether you're delving into image processing, object detection, or deep learning, you'll find a treasure trove of resources here to deepen your understanding and hone your skills.

🔍 What We Offer:

1-Algorithm Implementations: Dive into meticulously crafted implementations of key computer vision algorithms, from classic techniques to cutting-edge methods.

2-Statistical Methods: Harness the power of statistical analysis for robust image interpretation and feature extraction.

3-Pythonic Solutions: Our repository is entirely Python-based, offering clear and concise code snippets for seamless integration into your projects.

💡 Why Choose Us?:

1-Comprehensive Coverage: We've curated a comprehensive collection of resources covering every aspect of computer vision, providing you with a holistic learning experience.

2-Hands-On Learning: Put theory into practice with hands-on examples and practical exercises designed to reinforce your understanding.

3-Accessible to All: Whether you're a beginner or an expert, our repository caters to learners of all levels, offering something valuable for everyone.

👥Get Involved:

Contribute: Help us expand our repository by contributing your own implementations, insights, and optimizations. Together, we can build a richer resource for the entire computer vision community.

Engage: Join the discussion, ask questions, and share your experiences on our forums. Connect with fellow enthusiasts and expand your network. Learn and Grow: Embark on your journey through the world of computer vision, and let our repository serve as your trusted companion along the way.

Also please subscribe to my youtube channel!

🌟 Join us as we unravel the mysteries of computer vision, one algorithm at a time. Let's empower each other to push the boundaries of what's possible in this fascinating domain!

Star this repo if you find it useful ⭐

📬Contact

If you want to contact me, you can reach me through social handles.

📕Course 01 - 👁️ Introduction of Computer Vision

👁️ Chapter1: - Introduction

Topic Name/Tutorial	Video	Code
🌐1- What is computer Vision⭐️	1
🌐2-Computer Vision Tasks and Applications⭐️	1-2
🌐Best Free Resources to Computer Vision⭐️	---	---

📚Chapter2: - Image As Function

Topic Name/Tutorial	Video	Notbook
🌐1-Images as Functions Part 1?⭐️	1
🌐2-Images as Functions Part 2?⭐️	1
🌐3-Define an Image as a Function (Quiz)⭐️	1-2
🌐4-Color Planes and Color Image as a Function(Quiz)⭐️	1-2-3
🌐5- Digital Images	1-2
🌐6-Compute Image Size Quiz	---
🌐7-Read image in Matlab and Python	---
🌐8-Image Size and Data Type Quiz/Solution	1
🌐9-Crop an Image	1
🌐10-Add 2 Images	1-2-3
🌐11-Multiply image by a scaler and Blend 2 Images	1-2-3
🌐12-Common Types of Noise	1
🌐13-Image Difference	1-2-3
🌐14-Generate Gaussian Noise	1
🌐15-Effect of Sigma on Gaussian Noise	1-2-3
🌐16-Apply Gaussian Noise	1-2
🌐17-Displaying Images in Matlab and Python	1

📚Chapter3: - Filtering

Topic Name/Tutorial	Video	NoteBook
🌐1- What is Filtering?	1
🌐2- What is Gaussian Noise?	1-2
🌐3- Weighted Moving Average?	1-2
🌐4- Correlation Filtering?	1
🌐5- Averaging Filter?	1
🌐6- Gaussian Filter?	1-2
🌐7- Gaussian Filter with Matlab and Python?	1
🌐8- Remove Noise?(r)	1-2

📚Chapter4: - Linearity and Convolution '

Topic Name/Tutorial	Video	NoteBook
🌐1- Introduction of linear intuition of filtering	1
🌐2- Impulse Function and Response	1
🌐4- Filtering an Impulse Signal	1
🌐5- Correlation vs Convolution	1-2
🌐5-Properties of Convolution	1
🌐6-Computational Complexity and Separability	1
🌐7-Boundary Issues	1
🌐8-Methods	1
🌐9-Explore Edge Options	1
🌐10-Practicing with Linear Filters	1-2
🌐11-Different Kinds of Noise	1-2-3

📚Chapter5: - Filters as Templates

Topic Name/Tutorial	Video	NoteBook
🌐1- Introduction of Filters as templates, 1D correlation and 2D Correlations	1-2 -3
🌐2- Find Tempalte ID	1-2
🌐3- Template Matching⭐️	1-2-3-4-5

📚Chapter6: - Edge detection: Gradients

Topic Name/Tutorial	Video	NoteBook
🌐1- Pattern Finding and Feature Detection	1
🌐2- Understanding Edges in Images: Why They Matter in Visual Perception	1-2
🌐3- Edge Detection⭐️	1
🌐4-Derivatives and Edges⭐️	1
🌐5-What is Gradients⭐️	1
🌐6-Finite Differences⭐️	1
🌐7-Partial Derivatives of an Image⭐️	1
🌐8-The Discrete Gradient⭐️	1-2
🌐9-Sobel Operator⭐️	1-2-3

💻 Workflow:

• Fork the repository

• Clone your forked repository using terminal or gitbash.

• Make changes to the cloned repository

• Add, Commit and Push

• Then in Github, in your cloned repository find the option to make a pull request

print("Start contributing for Computer Vision")

⚙️ Things to Note

• Anybody interested in learning and contributing to computer Vision repository
• There are no hard prerequisites other than a dedication to learning
• Some experience with the following will be beneficial:,C++ Programming, Basic of Computer
• You can only work on issues that have been assigned to you.
• If you want to contribute the algorithm, it's preferrable that you create a new issue before making a PR and link your PR to that issue.
• If you have modified/added code work, make sure the code compiles before submitting.
• Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder.
• Do not update the README.md.

🔍 Explore more

Explore cutting-edge tools and Python libraries, access insightful slides and source code, and tap into a wealth of free online courses from top universities and organizations. Connect with like-minded individuals on Reddit, Facebook, and beyond, and stay updated with our YouTube channel and GitHub repository. Don’t wait — enroll now and unleash your Computer Vision potential!”

• Computer Vision Basic

✨Top Contributors

We would love your help in making this repository even better! If you know of an amazing Computer Vision course or you know intrested Computer Vision related tutorial/Video that isn't listed here, or if you have any suggestions for improvement in any course content, feel free to open an issue or submit a course contribution request.

                   Together, let's make this the best AI learning hub website! 🚀

Thanks goes to these Wonderful People. Contributions of any kind are welcome!🚀