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Computer vision

From photons and optics to a measurement somebody has to act on, with the failure modes at every stage.

  1. Computer Vision as Measurement and Decision Engineering
  2. Pixels, Color, and Dynamic Range
  3. Cameras, Optics, Sensors, and Compression
  4. Camera Geometry, Coordinates, and Calibration
  5. Sampling, Resizing, Filtering, and Morphology
  6. Vision Dataset Design, Annotation, and Provenance
  7. Augmentation, Synthetic Data, and Simulation
  8. Classical Features: Edges, Corners, and Descriptors
  9. Learned Visual Representations and Transfer
  10. Image Classification Systems
  11. Multilabel, Fine-Grained, and Hierarchical Recognition
  12. Object Detection: From Presence to Localization
  13. Detection Matching, Suppression, and Operating Points
  14. Semantic Segmentation: Pixelwise Prediction
  15. Instance and Panoptic Segmentation
  16. Keypoints, Landmarks, and Human Pose
  17. Depth, Stereo, and Projective Geometry
  18. Point Clouds and Three-Dimensional Perception
  19. Optical Flow and Dense Correspondence
  20. Multi-Object Tracking and Identity
  21. Video Understanding and Temporal Events
  22. OCR and Scene Text Recognition
  23. Document Vision and Layout Understanding
  24. Visual Search, Embeddings, and Re-Identification
  25. Self-Supervised Vision and Foundation Features
  26. Vision Transformers in Practice
  27. Vision-Language Models and Grounding
  28. Image Restoration and Inverse Problems
  29. Image Generation, Editing, and Diffusion
  30. Anomaly Detection and Industrial Inspection
  31. Face Recognition, Liveness, and Biometrics
  32. Medical Imaging Systems
  33. Remote Sensing and Geospatial Vision
  34. Robustness, OOD, Calibration, and Interpretability
  35. Privacy, Fairness, Security, and Media Provenance
  36. Edge Deployment, Compression, and Monitoring
  37. Vision Evaluation and Error Analysis Across Tasks
  38. Capstone: Design and Defend a Computer Vision System