Final Exam Review

1. Final Exam Review CS485/685 Computer Vision Prof. Bebis

2. Final Exam • Final exam will be comprehensive. • Midterm Exam material • SIFT • Object recognition • Face recognition using eigenfaces • Camera parameters • Camera calibration • Stereo

3. SIFT feature computation • Steps • Scale space extrema detection (how is it different from Harris-Laplace? different parameters) • Keypoint localization (need to know main ideas, no equations; two thresholds, which ones?) • Orientation assignment (how are the histograms built? multiple peaks?) • Keypoint descriptor (how are the histograms built? partial voting, main parameters, invariance to illumination changes)

4. SIFT features • Properties • Scale and rotation invariant • Highly distinctive • Partially invariant to 3D viewpoint and illumination changes • Fast and efficient computation • Main parameters? • Matching • How do we match SIFT features? • How do we evaluate the performance of a feature matcher? • Applications

5. SIFT variations • PCA SIFT • SURF • GLOH • Need to know key ideas and steps (no need to remember exact parameter values) • Similarities/Differences with SIFT • Strengths/Weakeness

6. Object Recognition • Model-based vs category-specific recognition • Preprocessing & Recognition • Challenges? • Photometric effects, scene clutter, changes in shape (e.g., non-rigid objects), viewpoint changes • Requirements? • Invariance, robustness • Performance Criteria? • Efficiency (time + memory), accuracy

7. Object Recognition (cont’d) • Representation schemes – advantages/disadvantages • Object centered (3D/3D or 3D/2D matching) • Viewer centered (2D/2D matching) • Matching schemes – advantages/disadvantages • Geometry-based • Appearance-based

8. Object Recognition (cont’d) • Main steps in matching: • Hypothesis generation • Hypothesis verification • Efficient hypothesis generation • Which scene features to choose? • How to organize and search the model database?

9. Object Recognition Methods • Alignment • Pose Clustering • Geometric Hashing Main ideas and steps

10. Object Recognition using SIFT • Main ideas and steps • Perform nearest neighbor search • Find clusters of features (pose clustering) • Perform verification • Practical issues • Approximate nearest neighbors

11. Bag of Features • Origins of bag of features method • Computing Bag of Features • Feature extraction • Learn “visual vocabulary” (e.g., K-Means clustering) • Quantize features using “visual vocabulary”. • Represent images by frequencies of “visual words” (bugs of features)

12. Bag of Features (cont’d) • Object categorization using bags of features. • Represent objects using Bag of Features • Classification (NN, kNN, SVM)

13. PCA • Need to know steps and equations. • What criterion does PCA minimize? • How is the “best” low-dimensional space determined using PCA? • What is the geometric interpretation of PCA? • Practical issues (e.g., choosing K, computing error, standardization)

14. Using PCA for Face Recognition • Represent faces using PCA – need to know steps and practical issues (e.g., AAT vs ATA) • Face recognition using PCA (i.e., eigenfaces) • DIFS • Face detection using PCA • DFFS • Limitations

15. Camera Parameters • Reference frames – what are they? • World • Camera • Image plane • Pixel plane • Perspective projection • Should know how to derive equations • Matrix notation • Properties of perspective projection • Vanishing points, vanishing lines.

16. Camera Parameters • Orthographic projection • How is related to perspective? • Study equations • Matrix notation • Properties • Weak perspective projection • How is related to perspective? • Study equations • Matrix notation • Properties

17. Camera Parameters (cont’d) • Extrinsic camera parameters • What are they and what is their meaning? • Study equations • Intrinsic camera parameters • What are they and what is their meaning? • Study equations • Projection matrix • What does it represent?

18. Camera Calibration • What is the goal of camera calibration and how is it performed? • Camera calibration using the projection matrix (study equations for step 1 only; you should remember how this method works in general) • Direct parameter calibration (do not memorize equations but remember how they work); how is the orthogonally constraint of the rotation matrix enforced?

19. Stereo • What is the goal of stereo vision? • Triangulation principle. • Familiarity with terminology (e.g., baseline, epipolar plane, epipolar lines, epipoles, disparity) • Two main problems of stereo (i.e., correspondence + reconstruction) • Recover depth from disparity – study proof.

20. Correspondence Problem • What is the correspondence problem and why is it difficult? • Mainmethods: intensity-based, feature-based • How do intensity-based methods work? • Main parameters of intensity-based methods. How can we choose them? • How do feature-based methods work? • Comparison between intensity-based and feature-based methods

21. Epipolar Geometry • Stereo parameters: extrinsic + intrinsic • What is the epipolar constraint, why is it important? • How is epipolar geometry represented? • Essential matrix • Fundamental matrix

22. Essential Matrix • What is the essential matrix? • Properties of essential matrix • Study equations • Equation satisfied by corresponding points

23. Fundamental Matrix • What is the fundamental matrix? • Properties of fundamental matrix • Study equations • Equation satisfied by corresponding points

24. Eight-point algorithm • What is it useful for? • Study steps • How is the rank(2) constraint enforced? • Normalized eight-point algorithm • Estimate epipoles and epipolar lines using the fundamental matrix?

25. Rectification • What is the purpose of rectification? • Why is it useful? • Study steps

26. Stereo Reconstruction • Three cases: • Known extrinsic and intrinsic parameters • Known intrinsic parameters • Unknown extrinsic and intrinsic parameters. • What information could be recovered in each case? • What are the main steps of the first two methods? (do not memorize equations)