Image Processing and Computer Vision
2.0
Course Syllabus 2016-2017
Schedule 2016-2017
Lab Exercises 2016-2017
1. Installation
2. Histogram Equilization
3. Skin Color Detection
4. Warping and Estimation
5. Convolutions
6. Local Structure
7. Histogram of Oriented Gradients
8. Image Stitching using SIFT
9. Camera Calibration
10. Motion Tracking
Lecture Notes
Image Processing and Computer Vision
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Lab Exercises 2016-2017
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Lab Exercises 2016-2017
ΒΆ
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. Histogram Equilization
3. Skin Color Detection
4. Warping and Estimation
5. Convolutions
6. Local Structure
6.1. What you will learn
6.2. Coordinate Axes
6.3. Analytical Local Structure
6.4. Gaussian Convolution
6.5. Separable Gaussian Convolution
6.6. Gaussian Derivatives
6.7. Comparison of theory and practice
6.8. Canny Edge Detector
7. Histogram of Oriented Gradients
7.1. Step 1: Preprocessing
7.2. Step 2: Calculate the Gradient Images
7.3. Step 3: Calculate HOG in 8x8 Cells
7.4. Step 4: Block Normalization
7.5. Step 5: Calculate the HOG feature vector
7.6. Step 6: Visualizing the HOG
7.7. And Beyond
8. Image Stitching using SIFT
8.1. What you will learn
8.2. Introduction
8.3. Projective Transform
8.4. SIFT
8.5. Matching Descriptors
8.6. RANSAC
8.7. Stitching it all together
8.8. Report
9. Camera Calibration
9.1. What you will learn
9.2. Camera Calibration
10. Motion Tracking
10.1. Tracking an Object
10.1.1. Exercise 1
10.1.2. Exercise 2
10.2. Egomotion: Simulating an Optical Mouse
10.2.1. Exercise 3