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Computer Vision: Algorithms and Applications

(CTU-AI321.AU1)
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1

Foundations of Computer Vision

  • What is computer vision?
  • A brief history
  • Geometric primitives and transformations
  • Photometric image formation
  • The digital camera
  • Point operators
  • Linear filtering
  • More neighborhood operators
  • Fourier transforms
  • Pyramids and wavelets
  • Geometric transformations
2

Visual Recognition Models

  • Supervised learning
  • Unsupervised learning
  • Deep neural networks
  • Convolutional neural networks
  • More complex models
  • Instance recognition
  • Image classification
  • Object detection
  • Semantic segmentation
  • Video understanding
  • Vision and language
  • Points and patches
  • Edges and contours
  • Contour tracking
  • Lines and vanishing points
  • Segmentation
3

Machine Learning for Vision Tasks

  • Shape from X
  • 3D scanning
  • Surface representations
  • Point-based representations
  • Volumetric representations
  • Model-based reconstruction
  • Recovering texture maps and albedos
  • View interpolation
  • Layered depth images
  • Light fields and Lumigraphs
  • Environment mattes
  • Video-based rendering
  • Neural rendering
4

Optimization Strategies for Vision Models

  • Scattered data interpolation
  • Variational methods and regularization
  • Markov random fields
  • Epipolar geometry
  • Sparse correspondence
  • Dense correspondence
  • Local methods
  • Global optimization
  • Deep neural networks
  • Multi-view stereo
  • Monocular depth estimation
5

Deployment and Real-Time Performance

  • Pairwise alignment
  • Image stitching
  • Global alignment
  • Compositing
  • Translational alignment
  • Parametric motion
  • Optical flow
  • Layered motion
  • Geometric intrinsic calibration
  • Pose estimation
  • Two-frame structure from motion
  • Multi-frame structure from motion
  • Simultaneous localization and mapping (SLAM)
A

Appendix A: Linear algebra and numerical techniques

  • A1 Matrix decompositions
  • A2 Linear least squares
  • A3 Non-linear least squares
  • A4 Direct sparse matrix techniques
  • A5 Iterative techniques
B

Appendix B: Bayesian modeling and inference

  • B1 Estimation theory
  • B2 Maximum likelihood estimation and least squares
  • B3 Robust statistics
  • B4 Prior models and Bayesian inference
  • B5 Markov random fields
  • B6 Uncertainty estimation (error analysis)
C

Appendix C: Supplementary material

  • C1 Datasets and benchmarks
  • C2 Software
  • C3 Slides and lectures

1

Foundations of Computer Vision

  • What Is Computer Vision?
  • Evaluating Camera-Based Perception for Autonomous Hospital Robots
  • Understanding Image Processing
2

Visual Recognition Models

  • Recognition in Computer Vision
  • Exploring Machine Learning and Deep Learning for Computer Vision
  • Understanding Image Features in Computer Vision Systems
3

Machine Learning for Vision Tasks

  • Reconstructing 3D Shape and Appearance
  • Exploring Image-Based Rendering for Immersive Media
4

Optimization Strategies for Vision Models

  • Reconstructing Visual Data
  • Estimating Scene Depth Using Stereo Vision Techniques
5

Deployment and Real-Time Performance

  • Aligning Images Using Geometric Image Registration
  • Structure from Motion and SLAM
  • Estimating Motion in Video Using Classical and Learning-Based Techniques

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