Course: Computer Vision Basics

- 👁️ Basics of Computer Vision
  
  The Basics of Computer Vision Course, launched in 2022, provides a comprehensive introduction to the field. Each chapter includes video lectures, notes, slides, and quizzes. Mostly Content is included from Udacity Course Introduction of Computer Vision. The course also offers a curated collection of resources such as GitHub repositories, YouTube channels, and Reddit groups.
  
  🚫 Guest access is limited. To unlock full course content, please enroll in the course.
- 👉📚GitHub Repository URL
- 👉 📝Notebook URL
- Gaussian Filter Quiz
- 📚🧑‍🎓📝Other Best Free Resources to Learn Computer Vision
  Welcome to an Exciting Chapter on Computer Vision!
  
  Discover a carefully curated collection of top-rated courses and valuable resources dedicated to Computer Vision. Whether you're just beginning your journey or a seasoned practitioner, there's something here for everyone to enhance their expertise in this cutting-edge field.
- 📚Chapter: 1-Introduction
  
  In this chapter, learn about the difference between CV and CP, what is Computer Vision, Why study Computer Vision,OCR and Face Recognition, Object recognition, Special Effects and 3D Modeling, Smart Cars, and Vision is NOT Image Processing
- 📚Chapter: 2-Image As Function
  
  In this chapter, Learn about -Images as functions, Define a Color Image as a Function, Digital Images, Matlab Images are Matrices and Common Types of Noise, etc
- 📚Chapter : 3- Image Processing with openCV and Pillow
  
  In this chapter, Learn about Gaussian Noise,Averaging Assumptions,Weighted Moving Average,Averaging Filter,Gaussian Filter,Variance or Standard Deviat
  - >Table of Content
    >Note
    1-What is digital Image Page
- 📚Chapter: 3-Filtering
  
  In this chapter, Learn about Gaussian Noise,Averaging Assumptions,Weighted Moving Average,Averaging Filter,Gaussian Filter,Variance or Standard Deviat
- 📚Chapter: 4-Linearity and Convolution
  Not available unless: You must be enrolled into this course!
- 📚Chapter: 5-Filters as Templates
  Not available unless: You must be enrolled into this course!
- 📚Chapter: 6-Edge detection: Gradients
  Not available unless: You must be enrolled into this course!
- 📚Chapter:7: Edge detection: 2D operators
  Not available unless: You must be enrolled into this course!
- 📚Chapter : 8: L1 Hough transform: Lines
  Not available unless: You must be enrolled into this course!
- 📚Chapter : 9: L2 Hough transform: Circles
  Not available unless: You must be enrolled into this course!
- 📚Chapter : 10: L3 Generalized Hough transform
  Not available unless: You must be enrolled into this course!
- 📚Chapter : 11-L1 Fourier transform
  Not available unless: You must be enrolled into this course!
- 📚Chapter : 12-L2 Convolution in frequency domain
  Not available unless: You must be enrolled into this course!
- 📚Chapter : 13-Aliasing
  
  This chapter series introduces Fourier Transform, explaining how signals are analyzed in the frequency domain, the role of convolution, and the efficiency of FFT. It also covers practical applications like smoothing, blurring, and filtering, along with key properties and common Fourier pairs to understand signal behavior.
- 📚Chapter : 14-L1 Cameras and images
  
  This chapter explains the basics of how images are created using light, apertures, and different types of lenses
- 📚Chapter : 15-L2 Perspective imaging
  
  This chapter explains the basics of how images are created using light, apertures, and different types of lenses

👁️ Basics of Computer Vision

📚Chapter: 4-Linearity and Convolution

📚Chapter: 5-Filters as Templates

📚Chapter: 6-Edge detection: Gradients

📚Chapter:7: Edge detection: 2D operators

📚Chapter : 8: L1 Hough transform: Lines

📚Chapter : 9: L2 Hough transform: Circles

📚Chapter : 10: L3 Generalized Hough transform

📚Chapter : 11-L1 Fourier transform

📚Chapter : 12-L2 Convolution in frequency domain