Final Exam Review CS 485685 Computer Vision Prof
- Slides: 26
Final Exam Review CS 485/685 Computer Vision Prof. Bebis
Final Exam • Final exam will be comprehensive. – – – – Midterm Exam material SIFT Object recognition Face recognition using eigenfaces Camera parameters Camera calibration Stereo
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)
SIFT features • Properties – – Scale and rotation invariant Highly distinctive Partially invariant to 3 D 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
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
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
Object Recognition (cont’d) • Representation schemes – advantages/disadvantages – Object centered (3 D/3 D or 3 D/2 D matching) – Viewer centered (2 D/2 D matching) • Matching schemes – advantages/disadvantages – Geometry-based – Appearance-based
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?
Object Recognition Methods • Alignment • Pose Clustering • Geometric Hashing Main ideas and steps
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
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)
Bag of Features (cont’d) • Object categorization using bags of features. – Represent objects using Bag of Features – Classification (NN, k. NN, SVM)
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)
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
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.
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
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?
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?
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.
Correspondence Problem • What is the correspondence problem and why is it difficult? • Main methods: 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
Epipolar Geometry • Stereo parameters: extrinsic + intrinsic • What is the epipolar constraint, why is it important? • How is epipolar geometry represented? – Essential matrix – Fundamental matrix
Essential Matrix • • What is the essential matrix? Properties of essential matrix Study equations Equation satisfied by corresponding points
Fundamental Matrix • • What is the fundamental matrix? Properties of fundamental matrix Study equations Equation satisfied by corresponding points
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?
Rectification • What is the purpose of rectification? • Why is it useful? • Study steps
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)
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