1	Announcements Final is Thursday, March 20, 10:30-12:20pm EE 037 Sample final out today
2	Filtering An image as a function Digital vs. continuous images Image transformation: range vs. domain Types of noise Noise reduction by averaging multiple images Cross-correlation and convolution properties mean, Gaussian, bilinear filters Median filtering Image scaling Image resampling Aliasing Gaussian pyramids
3	Edge detection What is an edge and where does it come from Edge detection by differentiation Image gradients continuous and discrete filters (e.g., Sobel operator) Effects of noise on gradients Derivative theorem of convolution Derivative of Gaussian (DoG) operator Laplacian operator Laplacian of Gaussian (LoG) Canny edge detector (basic idea) Effects of varying sigma parameter Approximating an LoG by subtraction
4	Features What makes a good feature? Derivation in terms of shifting a window H matrix Definition Meaning of eigenvalues and eigenvectors Harris operator How to use it to detect features Feature descriptors MOPS (rotated square window) SIFT (high level idea) Invariance (how to achieve it) Rotation Scale Lighting Matching features Ratio test RANSAC
5	Projection Properties of a pinhole camera effects of aperture size Properties of lens-based cameras focal point, optical center, aperture thin lens equation depth of field circle of confusion Modeling projection homogeneous coordinates projection matrix and its elements types of projections (orthographic, perspective) Camera parameters intrinsics, extrinsics types of distortion and how to model
6	Mosaics Image alignment Image reprojection homographies spherical projection Creating spherical panoramas Handling drift Image blending Image warping forward warping inverse warping
7	Projective geometry Homogeneous coordinates and their geometric intuition Homographies Points and lines in projective space projective operations: line intersection, line containing two points ideal points and lines (at infinity) Vanishing points and lines and how to compute them Single view measurement computing height Cross ratio Camera calibration using vanishing points direct linear method
8	Stereo Epipolar lines Stereo image rectification (basic idea) Stereo matching window-based epipolar search effect of window size sources of error Energy-minimization (MRF) stereo (basic idea) Depth from disparity Active stereo (basic idea) structured light laser scanning
9	Structure from motion Correcting drift in mosaics through global optimization Least squares Structure from motion Solving for camera rotations, translations, and 3D points The objective function The pipeline (from Photo tourism slides: detection, matching, iterative reconstruction…) Photo tourism
10	Light, perception, and reflection Light field, plenoptic function Light as EMR spectrum Perception color constancy, color contrast adaptation the retina: rods, cones (S, M, L), fovea what is color response function, filters the spectrum metamers Finding camera response function (basic idea, not details) Materials and reflection what happens when light hits a surface BRDF diffuse (Lambertian) reflection specular reflection Phong reflection model measuring the BRDF (basic idea)
11	Photometric stereo Shape from shading (equations) Diffuse photometric stereo derivation equations solving for albedo, normals depths from normals Handling shadows Computing light source directions from a shiny ball Limitations
12	Recognition Classifiers Probabilistic classification decision boundaries learning PDF’s from training images Bayes law Maximum likelihood MAP Principle component analysis Eigenfaces algorithm use for face recognition use for face detection
13	Segmentation Graph representation of an image Intelligent scissors method Image histogram K-means clustering Morphological operations dilation, erosion, closing, opening Normalized cuts method (basic idea)
14	Motion Optical flow problem definition Aperture problem and how it arises Assumptions Brightness constancy, small motion, smoothness Derivation of optical flow constraint equation Lucas-Kanade equation Derivation Conditions for solvability Relation to Harris operator Iterative refinement Newton’s method Pyramid-based flow estimation
15	Texture Markov chains Text synthesis algorithm Markov random field (MRF) Efros and Leung’s texture synthesis algorithm Improvements Fill order Block-based Texture transfer (basic idea)
16	Guest Lectures Richard Ladner—Tactile graphics Jenny Yuen—cateract detection Jeff Bigham—object-based image retrieval (basic ideas)