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computervisioncourse's Introduction
Module 1. Image processing basics. Local image processing and feature descriptors
- Scale pyramid, bluring, Image gradients, Contour detection.
Correspondences and wide baseline stereo:
- Interest point and distinguished regions detection:
- Harris operator (corner detection)
- Hessian detector, affine covariant version, Maximally Stable Extrema Regions (MSER).
- Descriptors of measurement regions
- SIFT (scale invariant feature transform), RootSIFT
- Shape context.
- LBP (local binary patterns)
- Deep learned features (HardNet).
- Deep learned features (R2D2, SuperPoint)
Graph neural networks for matching (LoFTR, SuperGlue)
Module 2. Image Segmentation and Correspondence
Intro into segmentation, matting and correspondence problems
- Level-set methods
- Otsu thresholding
- Postprosessing (erosion, dilation, NL-means)
- Edge-based methods
- Connected components labeling
- Active contour model
- Super-pixel segmentation
- Watershed segmentation
- Expectation-maximization in computer vision
- Gaussian mixture model and K-means
- Segmentation based on graph cuts
- Markov models (chain, trees, P2D, CRF)
- YOLACT
- Sparse and dense optical flow
- Affine and projective transformations
- Birchfield–Tomasi dissimilarity
- Dynamic programming for stereo correspondence
- Polar coordinates and 360 photo
- Panorama stitching
- Lukas-Kanade Optical Flow
- Horn-Schunck optical flow
- Gunnar-Farneback algorithm
- PatchMatch algorithm
- PWC-Net, MaskFlownet
Module 3. Object Tracking and Search
- Histograms and statistical models
- Hidden Markov Models
- Integral images
- HOG detector
- KLT tracker
- Mean-Shift, CamShift tracker
- Kalman Filter
- Binary features
- Bag of visual words
- minHash
- Image Retrieval for large image collections: image description, indexing, geometric consistency
- Neural nets for object tracking and search
Module 4. Geometry and Augmented Reality (AR)
- Homogeneous coordinates
- Translation, rotation, scale, and projection matrices
- Camera models
- Rendering 3D scenes
- Camera calibration and removal of lens distortion
- Search for lines, circles and ellipses in photo
- Stereo and epipolar geometry
- Match moving and 3D reconstruction
- Light and materials
- AR examples
Module 5. Deep Learning for CV
- ML basics: data retrieval, synthesis, augmentation, train and test sets
- Non-deep ML models (PCA, SVM)
- Deep learning
- Different layers. Math and implementation.
- Optimization for size and speed (MobileNet, ... )
- Running in real-time (Core ML 2, etc)
- Study of different architectures:
Image classification (AlexNet),
Object detection (R-CNN),
Object Tracking (SAE and CNN),
Object segmentation (SegNet),
Instance segmentation (Mask R-CNN),
Optical flow (PWC Net),
Human pose estimation (VNect),
3D reconstruction (LayoutNet),
Transfer-based AR (LSTM Neural nets, GANs).
Metrics learning (face recognition)
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