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Things to take away from this lecture |
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An image as a function |
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Digital vs. continuous images |
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Image transformation: range vs. domain |
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Types of noise |
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LSI filters |
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cross-correlation and convolution |
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properties of LSI filters |
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mean, Gaussian, bilinear filters |
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Median filtering |
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Image scaling |
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Image resampling |
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Aliasing |
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Gaussian pyramids |
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Things to take away from this lecture |
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What is an edge and where does it come from |
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Edge detection by differentiation |
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Image gradients |
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continuous and discrete |
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filters (e.g., Sobel operator) |
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Effects of noise on gradients |
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Derivative theorem of convolution |
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Derivative of Gaussian (DoG) operator |
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Laplacian operator |
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Laplacian of Gaussian (LoG) |
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Canny edge detector (basic idea) |
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Effects of varying sigma parameter |
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Approximating an LoG by subtraction |
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Things to take away from this lecture |
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Graph representation of an image |
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Intelligent scissors method |
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Normalized cuts method |
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Image histogram |
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K-means clustering |
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Morphological operations |
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dilation, erosion, closing, opening |
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Hough transform |
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Things to take away from this lecture |
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Optical flow problem definition |
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Aperture problem and how it arises |
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Assumptions |
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Brightness constancy, small motion, smoothness |
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Derivation of optical flow constraint equation |
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Lukas-Kanade equation |
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Derivation |
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Conditions for solvability |
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meanings of eigenvalues and eigenvectors |
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Iterative refinement |
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Newton’s method |
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Coarse-to-fine flow estimation |
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Feature tracking |
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Harris feature detector |
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L-K vs. discrete search method |
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Tracking over many frames |
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Prediction using dynamics |
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Applications |
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MPEG video compression |
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Image alignment |
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Things to take away from this lecture |
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Properties of a pinhole camera |
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effects of aperture size |
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Properties of lens-based cameras |
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focal point, optical center, aperture |
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thin lens equation |
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depth of field |
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circle of confusion |
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Modeling projection |
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homogeneous coordinates |
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projection matrix and its elements |
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orthographic, weak perspective, affine models |
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Camera parameters |
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intrinsics, extrinsics |
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Things to take away from this lecture |
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Image alignment |
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Image reprojection |
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homographies |
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cylindrical projection |
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Radial distortion |
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Creating cylindrical panoramas |
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Image blending |
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Image warping |
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forward warping |
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inverse warping |
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bilinear interpolation |
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Things to take away from this lecture |
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Homogeneous coordinates and their geometric
intuition |
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Homographies |
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Points and lines in projective space |
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projective operations: line intersection, line
containing two points |
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ideal points and lines (at infinity) |
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Vanishing points and lines and how to compute
them |
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Single view measurement |
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within a reference plane |
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height |
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Cross ratio |
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Camera calibration |
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using vanishing points |
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direct linear method |
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Things to take away from this lecture |
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Cues for 3D inference, shape from X |
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Epipolar geometry |
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Stereo image rectification |
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Stereo matching |
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window-based epipolar search |
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effect of window size |
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sources of error |
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Active stereo (basic idea) |
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structured light |
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laser scanning |
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Things to take away from this lecture |
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Baseline tradeoff |
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Multibaseline stereo approach |
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Voxel coloring problem |
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Volume intersection algorithm |
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Voxel coloring algorithm |
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Space carving algorithm |
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Things to take away from this lecture |
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Light field, plenoptic function |
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Light as EMR spectrum |
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Perception |
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color constancy, color contrast |
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adaptation |
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the retina:
rods, cones (S, M, L), fovea |
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what is color |
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response function, filters the spectrum |
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metamers |
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Finding camera response function (basic idea,
not details) |
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Materials and reflection |
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what happens when light hits a surface |
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BRDF |
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diffuse (Lambertian) reflection |
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specular reflection |
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Phong reflection model |
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measuring the BRDF |
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Things to take away from this lecture |
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Classifiers |
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Probabilistic classification |
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decision boundaries |
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learning PDF’s from training images |
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Bayesian estimation |
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Principle component analysis |
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Eigenfaces algorithm |
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