Check out my course site: AIisMath.com
This is my CV course raw data git repo - you can see the raw .py/ .ppt files here.
Intro to CV and Python (slides)
- Python, VSCode & packages installation guide
- Course details
- What is computer vision (CV)?
- Course outline
- Intro to Python
- Ex1: basic python, numpy & matplotlib, opencv
Basic image processing (slides)
- Image representation
- Pixel-wise operations
- Histogram equalization (notebook)
- Template matching
- Morphology operators
- Connected components
- Color space (notebook)
- Ex2: morphological operators and friends
Filtering and resampling (slides)
- Noise and filtering (notebook)
- Frequency representation
- Decimation
- Interpolation
Edge Detection (slides)
- Intro to edges
- Basic edge image (gradient filters notebook)
- Edge thinning
- LoG
- NMS
- Edge mask
- Canny edge detector (edge detection notebook)
- Other edge related topics
- Frequency representation
- Unsharp filter
- Ex3: Bilateral filter
Curve fitting (slides)
- Least squares
- Total least squares
- RANSAC (LS, TLS & RANSAC notebook)
- Ex4a: vignetting
Hough transform (slides)
- Hough transform (Hough transform notebook)
-
$(m,b)$ parameter space -
$(\rho,\theta)$ parameter space
-
- Ex4b: Hough circles
Image formation (slides)
- BRDF
- Pinhole camera
- Digital camera
- The human eye
Geometric transformation (slides)
- 2D->2D transformations (notebook)
- 3D->3D transformations
- 3D->2D transformations (3D projections)
- Perspective projection
- Orthographic projection
Camera calibration (slides)
- What is camera calibration?
- Camera extrinsics
- Perspective projection
- Camera intrinsics
- Full camera matrix
- Calibration methods and distortions (notebook)
Features (slides)
- What and why we need features detection?
- Feature detection
- Blob detection
- Harris corner detection (Harris notebook)
- SIFT detector
- Feature description
- Template matching
- HOG
- SIFT descriptor
- SIFT feature matching (SIFT notebook)
- Panoramas
Stereo (slides)
- Structure from motion
- Triangulation
- Stereo matching
- Camera rectification
- Epipolar geometry
- Essential matrix
- Fundamental matrix
- Estimating the fundamental matrix
- Other 3D sensors
Neural networks basics (slides)
- The classification problem- again
- NN history
- Perceptron
- Hyperplanes
- Activation
- Dense layer
- Multi-layer perceptron (MLP)
- Optimization
- Softmax + cross entropy + loss
- Gradient descent
- Basic data preprocessing
- Data normalization
- Train, validation and test splits
- Fully connected net
Neural networks 2 (slides)
- ConvNets
- Convolution layer
- Pooling layer
- Overfitting