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모든 컴퓨터 비젼 연구자들이 알아야 할 20개의 techniques

MS의 Rick Szeliski 박사님과 옥스포드의 Andrew Zisserman 교수님이 이야기하는 “모든 컴퓨터 비젼 연구자들이 알아야 할 20개의 techniques”.

1. Image formation and optics
2. Image processing, filtering, Fourier analysis
3. Pyramids and wavelets
4. Feature extraction
5. Image matching
6. Bag of words
7. Optical flow
8. Structure from motion
9. Multi view stereo
10. Segmentation
11. Clustering
12. Viola-Jones
13. Bayesian techniques
14. Machine learning
15. RANSAC and robust techniques
16. Numerical methods
17. Optimization
18. Range finding, active illumination
19. Algorithms
20. Graph cuts
21. Dynamic programming
22. Complexity analysis
23. MATLAB and C++. and assembly (optional: GPU programming)
24. Communication and presentation skills

• Image and features
• NCC
• Interest point operators
• Scale invariant and affine invariant detectors & descriptors
• Scale space
• Image processing, filtering, Fourier analysis
• Pyramids and wavelets
• Edge detection
• Restoration e.g. deblurring, super-resolution – Linear, e.g. Wiener filter – MRF – Non-local means/BM3D/bilateral filter
• Segmentation, grouping and tracking
• Segmentation – Normalized cuts
• Grouping – Hough transforms
• Clustering – K-means – Mean-shift – Pedro-clustering
• Tracking – Kalmanfilter – Particle filter
• Multi-view: stereo, SFM, flow
• RANSAC and other robust techniques
• Geometry: – epipolar geometry (projective and affine) – planar homographies – Affine camera
• Geometry estimators – 8 point algorithm for F – 4 point algorithm for H
• Factorization
• Bundle-adjustment
• Flow – Horn & Schunck L2 – Lucas-Kanade – L1 regularized
• Recognition
• Bag of visual words
• HOG, SIFT, GIST
• Spatial pyramid
• Spatial configurations/Pictorial structures
• Sliding window/jumping window
• Cascades
• Machine Learning – Adaboost – kNN – SVM – Random forest – PCA, ICA, CCA – EM – MIL/Latent-SVM – Regularization – HMM – Graphical & Bayesian models
• Optimization – Classical linear and non-linear – Graph operations – Dynamic programming/message passing for MAP, max-marginals – Graph cuts for binary variable MAP
• Texture synthesis

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