- W8 section (WB 130, TA: TBD)
- F2 section (WB 130, TA: TBD)
| Part I: Representing images as 2D arrays of pixels | ||||
| Week 1 | ||||
| Date | Topic | Sub-topic | Readings | Resources |
| Wed, Jan 10 | Introduction; Cameras and Images | Understanding digital images; basic camera controls; color image acquisition; image noise | Sections 1.1-1.2, 2.1, 2.2, 2.4.2 (only paragraph entitled "silicon sensors"), 2.6.2 from Castleman book | clarkvision.com: A very comprehensive website about photography, cameras and how to characterize their properties |
| Wed/Fri tutorial | Tutorial on OpenCV & Python | |||
| Week 2 | ||||
| Wed, Jan 17 | HDR Imaging and Alpha Matting | Computing camera response functions from images; the matting equation | Sections 1 and 2, up to Eq (2), from the Debevec 1997 Siggraph paper in the Dropbox Readings/ directory. As you read the paper, note that film response curve and camera response curve in the case of digital cameras, are one and the same. | The HDRShop home page. Rendering with Natural Light (a movie that uses high-dynamic-range photography to capture outtdoor illumination and re-use it for image synthesis) |
| Wed/Fri tutorial | TBD | See dropbox Tutorials/ directory | ||
| Part II: Representing images as continuous 1D and 2D functions | ||||
| Week 3 | ||||
| Wed, Jan 24 | Computing 1D image derivatives | Least-squares polynomial fitting; intensity derivatives; weighted least squares; RANSAC | To run the demo shown in class: (1) unpack the file polydemo.zip in the Demo Code directory, (2) run MATLAB, (3) change the current MATLAB directory to the directory you unpacked the code, (4) type polydemo at the matlab prompt. You should run the demo for a variety of fits (LS, WLS, 1st degree, 2nd degree, etc) to see their effect. | |
| Wed/Fri tutorial | TBD | See dropbox Tutorials/ directory | ||
| Week 4 | ||||
| Wed, Jan 31 | Representing 2D image curves | Local analysis of curves: the tangent & normal vectors, the moving frame | ||
| Wed/Fri tutorial | TBD | See dropbox Tutorials/ directory | ||
| Week 5 | ||||
| Wed, Feb 7 | Edge detection | Local analysis of 1D and 2D image patches: the Image Gradient; case study: Painterly Rendering | See Litwinowicz paper on painterly rendering in the Readings/ directory on Dropbox (this is not required reading). | |
| Wed/Fri tutorial | TBD | See dropbox Tutorials/ directory | ||
| Week 6 | ||||
| Wed, Feb 14 | Corner detection, Intelligent Scissors, Seam Carving | Relation between local shape near extrema and the eigenvectors/eigenvalues of the Hessian; relation between eigenvalues & the trace & determinant of a matrix; localizing edges as zero crossings of the Laplacian; the Lowe feature detector: finding non-cylindrical points through eigenvalue analysis of the Hessian of the Laplacian | See Mortensen paper on intelligent scissors in the Readings/ directory on Dropbox (this is not required reading). This paper and the technique will be covered in one of the forthcoming tutorials. | |
| Wed/Fri tutorial | TBD | See dropbox Tutorials/ directory | ||
| Part III: Representing images as N-dimensional vectors | ||||
| Week 7 | ||||
| Wed, Feb 28 | Template matching, correlation and patch-based image processing | Representing images as vectors; evaluating similarity using RMS distance error, cross-correlation and normalized cross-correlation; non-local means denoising | To run the demo shown in class: (1) unpack the file corrdemo.zip in the Demo Code directory, (2) run MATLAB, (3) change the current MATLAB directory to the directory you unpacked the code, (4) type corrdemo at the matlab prompt. | |
| Wed, Feb 28 | MIDTERM | |||
| Fri, Mar 2 | TBD | See dropbox Tutorials/ directory | ||
| Week 8 | ||||
| Wed, Mar 7 | Principal Component Analysis | Face recognition using Eigenfaces | Section 13.6 from Castleman | To run the demo shown in class: (1) unpack the file recognition_demo.zip in the Demo Code directory, (2) run MATLAB, (3) change the current MATLAB directory to the directory you unpacked the code, (4) type pca_recdemo at the matlab prompt. |
| Wed/Fri tutorial | TBD | See dropbox Tutorials/ directory | ||
| Part IV: Multi-resolution image representations | ||||
| Week 9 | ||||
| Wed, Mar 14 | Gaussian Pyramids | Original paper by Burt and Adelson on the Gauss/Laplacian pyramids in the Dropbox Readings/ directory. You should read up to, but not including, section entitled Entropy. | The Matlab demo scale_space1D_demo.zip in the Demo Code directory on Dropbox shows how a 1D image changes as we smooth it with a sequence of Gaussians of increasing standard deviation. | |
| Wed/Fri tutorial | TBD | See dropbox Tutorials/ directory | ||
| Week 10 | ||||
| Wed, Mar 21 | The Haar Wavelet Transform | Wavelet compression of 1D and 2D images | The tutorial paper on the Haar Wavelets by Stollnitz et al in the Readings/ directory on Dropbox. | The Matlab demo wavedemo.zip in the Demo Code/ directory is the demo shown in class. Type wavedemo at the Matlab prompt to run it. |
| Wed/Fri tutorial | TBD | See dropbox Tutorials/ directory | ||
| Week 11 | ||||
| Wed, Mar 28 | Polynomial fitting vs. correlation; Matching images using SIFT | Analysis of WLS polynomial fitting and image smoothing as a template matching operation; template matching expressed as a multiplication of an image with a Toeplitz matrix; Gaussian image smoothing; SIFT-based feature detection; the SIFT descriptor; image matching using SIFT | Sections 1-3 of the Lowe paper on SIFT found in the Readings/ directory on Dropbox. | Web page on SIFT (with demo code) |
| Wed/Fri tutorial | TBD | See dropbox Tutorials/ directory | ||
| Part V: Introduction to 2D Image Transformations | ||||
| Week 12 | ||||
| Wed, Apr 4 | Homogeneous coordinates | Homography-based image warping | ||
| Wed tutorial | TBD | See dropbox Tutorials/ directory | ||
Site last modified on Sunday, January 8, 2017
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