Open source
Vision and Image Processing library (OSVIP)
Updated
8.5.2012
Basic computer vision processing
- Current work:
- EMGU (wrapper for opencv to be used with C#,C++ etc)
- Features tracking
- To be done
- Kinect applications
- study opencv-Arm systems
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- Major Opencv sites
- Up-to-date documentation
- Installation guide
- Learn opencv
- Opencv download
- C++
- C#
- Python
- Python-opencv
- numpy and scipy
- python networking
- Python debuggers
- Python tools
- Kinect
- Virtual machines
- Linux
- Embedded systems
- Ubuntu Atom installation
- Andiod
- Arm systems
- 3D graphic engines
- Miscellanous
- fix the webcam problem in win7-opencv2.2-vs2008
(ver. 1.1, 1 June 2011)
- Read and display images
- Camera
- Projection
- Camera calibration
i.
Using calibrated 3D
objects
ii.
Using calibrated planar
object
iii.
Auto-calibration (with
specific object)
- Edge detection
- First order
- Second order
- Gaussian filtering
- Gray level processing
- Histogram
- Histogram equalization
- Image
back-projection
- Color processing
- Conversion RGB-> HSV etc
- Object tracking
- Mean-shift
- Cam-shift
- Particle
filter/Kalman filter based object tracking
- Feature detection
- Harris operator
- Point features
i.
Kalman filtering tracking
(Edward)
- Line features
i.
Quadrangle detection
- Blob detection
- Feature tracking
- Point features
i.
KLT
ii.
Kalman
iii.
Condensation/particle
filter
- Line Tracking
i.
Kalman
ii.
IMM Kalman
- Stereo
- Essential matrix
- Fundamental matrix
- Image homography
- Image warping
- Structure from motion SfM using
- Stereo images
- Three images (Trifocal tensor)
- N images
i.
Factorization
ii.
Bundle adjustment
1.
Alternating (extended
Lowe)
2.
Full Bundle adjustment
iii.
Kalman EKF filtering
approach
1.
One camera SfM
2.
Stereo Sfm
3.
Trifocal tensor
4.
Unscented Kalman Filter
- Projector-camera
systems
- System
calibration and projection geometry
- Projection image
warping
- Advanced topics
- Face detection
- Face tracking
- Face recognition