Computer Vision Fundamentals - Lesson 2: Image Processing Basics

Overview:

This lesson focuses on the fundamental techniques of image processing, which are crucial for preparing images for analysis in computer vision tasks. Students will learn how to manipulate and enhance images to extract meaningful information.

Objectives:

By the end of this lesson, students should be able to:

• Understand the basic concepts of image processing.
• Perform basic image manipulations such as resizing, cropping, and color space conversion.
• Apply filters for image enhancement and noise reduction.
• Implement basic edge detection and thresholding techniques.

Topics Covered:

1. Understanding Digital Images:

   • Image representation: Grayscale, RGB, and other color spaces.
   • Image resolution, size, and aspect ratio.
   • Reading and displaying images using OpenCV.

2. Basic Image Manipulations:

   • Resizing and scaling images.
   • Cropping images to focus on regions of interest.
   • Rotating and flipping images.

3. Color Space Conversion:

   • Understanding different color spaces: RGB, HSV, LAB, and grayscale.
   • Converting between color spaces in OpenCV.
   • Applications of color space conversion (e.g., in object tracking).

4. Image Filtering and Enhancement:

   • Smoothing images with filters (e.g., Gaussian blur, median blur).
   • Sharpening images for better edge detection.
   • Histogram equalization for contrast adjustment.

5. Noise Reduction Techniques:

   • Understanding noise in images.
   • Applying noise reduction techniques: Averaging, Gaussian, and Median filters.
   • Practical examples and applications.

6. Edge Detection:

   • Basics of edge detection: Importance in computer vision.
   • Applying Sobel, Prewitt, and Canny edge detection algorithms.
   • Visualizing and interpreting edges in images.

7. Thresholding Techniques:

   • Introduction to thresholding: Binary, Otsu's, and adaptive thresholding.
   • Applying thresholding for image segmentation.
   • Use cases in real-world applications.

8. Practical Examples and Exercises:

   • Step-by-step implementation of the discussed techniques using OpenCV.
   • Real-world examples: Enhancing a noisy image, detecting edges in a landscape photo.

This lesson provides students with hands-on experience in fundamental image processing techniques, preparing them for more complex computer vision tasks in subsequent lessons.