Vision robotics uses several key computer vision algorithms. The more you know, the more tools you’ll have in your arsenal. Topics include: vision algorithms, OpenCV, installation guides, etc.
Computer Vision Algorithms & Techniques
Mathematical Morphology
- Mathematical Morphology
- Mathematical Morphology – Composite Operations
- Mathematical morphology in OpenCV
Histograms
- Histograms: From the simplest to the most complex
- Accessing Histogram Data
- Histograms with inbuilt functions in OpenCV
- Drawing histograms in OpenCV
Scale Invariant Feature Transform (SIFT)
Feature/Corner Detection
- Features: What are they?
- The Harris Corner Detector
- Interesting windows in the Harris corner detector
- The Shi-Tomasi corner detector
- Corner Detection in OpenCV
- Subpixel corners: Increasing accuracy
- Subpixel corners in OpenCV
The Hough transform
OpenCV specific
- OpenCV memory management
- Primitive structures in OpenCV
- 2D matrices with CvMat in OpenCV
- Memory layout of matrices of multi-dimensional objects
- Efficiently accessing matrices
- OpenCV’s C++ Interface
Noise
- Generating uniform noise
- Noise models (Part 1, 2)
- Noise reduction by averaging (theory)
- Noise reduction by averaging (implementation)
Miscellenaous
- Transparent image overlays in OpenCV
- Template matching
- Tracking coloured objects in OpenCV
- Integral images in OpenCV
- Community Core Vision: An app for simple vision stuff
Segmentation & blobs
- Thresholding
- An introduction to contours
- Colour spaces (Part 1, 2)
- Normalized RGB
- Pixel neighbourhoods and connectedness
- Connected component labelling
- Labelling connected components: Example
- Fast connected components labeling
Capturing images
Camera calibration
Convolutions
Guides/Walkthroughs
Installations
Getting started with OpenCV
- Why OpenCV?
- Installing and getting OpenCV running
- Hello world! with images!
- Filtering images
- Capturing images
- HighGUI: Creating Interfaces
- Using OpenCV on Windows
