모든 컴퓨터 비젼 연구자들이 알아야 할 20개의 techniques"

출처 페북, 패턴인식 뭐 그런데인듯

들어보고나 좀 아는건 굵게. 잘 모르면 언더라인 빨간색. 잘 모르면 표시잘 안하고..

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

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

Others

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