Lab Exercises 2017-2018

• 1. Installation
  • 1.1. OpenCV
  • 1.2. Standard Images and Data Sets
    • 1.2.1. Skin Color Data Set
    • 1.2.2. Standard Images
• 2. Python for IPCV
  • 2.1. Python for Image Processing
    • 2.1.1. Contrast Stretching
    • 2.1.2. Linear Filtering
• 3. Histogram Equilization
• 4. Skin Color Detection
• 5. Warping and Estimation
• 6. Convolutions
• 7. Local Structure
  • 7.1. What you will learn
  • 7.2. Coordinate Axes
  • 7.3. Analytical Local Structure
  • 7.4. Gaussian Convolution
  • 7.5. Separable Gaussian Convolution
  • 7.6. Gaussian Derivatives
  • 7.7. Comparison of theory and practice
  • 7.8. Canny Edge Detector
• 8. Histogram of Oriented Gradients
  • 8.1. Step 1: Preprocessing
  • 8.2. Step 2: Calculate the Gradient Images
  • 8.3. Step 3: Calculate HOG in 8x8 Cells
  • 8.4. Step 4: Block Normalization
  • 8.5. Step 5: Calculate the HOG feature vector
  • 8.6. Step 6: Visualizing the HOG
  • 8.7. And Beyond
• 9. Image Stitching using SIFT
  • 9.1. What you will learn
  • 9.2. Introduction
  • 9.3. Projective Transform
  • 9.4. SIFT
  • 9.5. Matching Descriptors
  • 9.6. RANSAC
  • 9.7. Stitching it all together
  • 9.8. Report
• 10. Camera Calibration
  • 10.1. What you will learn
  • 10.2. Camera Calibration
• 11. Motion Tracking
  • 11.1. Tracking an Object
    • 11.1.1. Exercise 1
    • 11.1.2. Exercise 2
  • 11.2. Egomotion: Simulating an Optical Mouse
    • 11.2.1. Exercise 3