The computer vision course is composed be three session: 1) Introduction to Image Processing and Computer Vision, 2) Visual Tracking and 3) Computer vision for augmented reality. Validating session 1 is necessary before sessions 2) and 3). Several topics are commun between sessions 2) and 3). Slides are mainly in english. Practical courses in matlab are mainly in french and in python are in english.
Please download this tuto (or read the last section of this page) to start the Virtual Machine Mecatronique and select the right virtual environment (for python based practicals)
Introduction to Image Processing and Computer Vision
Lecture1: Introduction (download slides)
Lecture2: Image Processing (part1): (download slides)
- Image coding,
- Global filters,
Lecture3: Image Processing (part2): (download slides)
- Spatial filters,
- Mathematical Morphology introduction.
- Download practical 2 (Python) : spatial filtering and Morphology Mathematic
Lecture4: detectors and descriptors: (download slides)
- Interest Point Detector and Descriptor:
- Download Practical Course (Harris detector, python), (Harris detector, matlab)
Lecture5: Geometry (download slides)
- Introduction to homogeneous coordinate system
- Pinhole camera model
- camera calibration
- Homography, epipolar geometry
- download practical course (Matlab, fr), (Python)
Visual Tracking :
- Introduction to visual tracking (download slides)
- Interest Point Detector and Descriptor
- >> Download Slides (Detectors and Descriptors) (Popular descriptors)
- >> Download Practical Course (Harris detector) (additional video sequence)
- Optimisation based methods. Download Practical Course (Mean Shift Tracker, Matlab)
- Probabilistic based tracking methods and more precisely stochastic ones (particle filters).
- >> Download Slides (Part1, including introduction ti visual tracking) (Part2)
- >> Download Practical Course (Particle Filtering, Matlab) or (Particle Filtering, Opencv3)
- Deep Learning (Download slides)
- Practical on Opencv popular trackers (subject, videos)
Computer Vision for Augmented Reality
- Lecture 1: Introduction to Augmented reality, detectors and descriptors (slides1, slides2)
- Practical course 1: detectors an descriptors (Harris detector, python)
- Lecture 2: geometry for augmented reality
- Practical course 2: warping planar textures
- Lecture 3: Realtime tracking (Download Slides (Part1, including introduction ti visual tracking) (Part2)) ; Download Practical Course (Particle Filtering, Matlab)
- Practical course 3: Realtime planar detection for augmented reality
Practical courses use C++ OpenCv library: Download a Visual C++ 2010 project skeleton configured for opencv (x86).
Using python for computer vision on mecatronique virtual machine.
Once you are logged into the virtual machine (ubuntu 16.04), launch a terminal are run the command:
conda activate opencv
This command activates the virtual environment opencv (all libraries you need for parcticals)
the string (opencv) will appear in the beginning of the prompt command line.
If you want to use the IDE spyder, type spyder& in the command line to launch it in a background process.
If you want an external display of the figure, you should modify the parameters of spyder to set the execution mode to "run in an external console". You may also use your own editor (emacs, vim) and run the python script using the command: python file.py with file.py the name of your python script.