2. Module 2
by Michael Beyeler, Prateek Joshi, Joseph Howse
OpenCV: Computer Vision Projects with Python
- OpenCV: Computer Vision Projects with Python
- Table of Contents
- OpenCV: Computer Vision Projects with Python
- OpenCV: Computer Vision Projects with Python
- Credits
- Preface
- 1. Module 1
- 1. Setting up OpenCV
- Choosing and using the right setup tools
- Running samples
- Finding documentation, help, and updates
- Summary
- 2. Handling Files, Cameras, and GUIs
- 3. Filtering Images
- 4. Tracking Faces with Haar Cascades
- 5. Detecting Foreground/Background Regions and Depth
- A. Integrating with Pygame
- B. Generating Haar Cascades for Custom Targets
- 1. Setting up OpenCV
- 2. Module 2
- 1. Detecting Edges and Applying Image Filters
- 2. Cartoonizing an Image
- 3. Detecting and Tracking Different Body Parts
- 4. Extracting Features from an Image
- Why do we care about keypoints?
- What are keypoints?
- Detecting the corners
- Good Features To Track
- Scale Invariant Feature Transform (SIFT)
- Speeded Up Robust Features (SURF)
- Features from Accelerated Segment Test (FAST)
- Binary Robust Independent Elementary Features (BRIEF)
- Oriented FAST and Rotated BRIEF (ORB)
- Summary
- 5. Creating a Panoramic Image
- 6. Seam Carving
- 7. Detecting Shapes and Segmenting an Image
- 8. Object Tracking
- 9. Object Recognition
- 10. Stereo Vision and 3D Reconstruction
- 11. Augmented Reality
- 3. Module 3
- 1. Fun with Filters
- 2. Hand Gesture Recognition Using a Kinect Depth Sensor
- 3. Finding Objects via Feature Matching and Perspective Transforms
- 4. 3D Scene Reconstruction Using Structure from Motion
- 5. Tracking Visually Salient Objects
- 6. Learning to Recognize Traffic Signs
- 7. Learning to Recognize Emotions on Faces
- A. Bibliography
- Index
-
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