Part I: Representing images as 2D arrays of pixels  
Week 1  
Date  Topic  Details  Readings  Resources 
Wed, Jan 8  Introduction; The Camera Response Function  Digital images; computing camera response functions from images.
Slides: [Printer friendly] [Color] 
Sections 1.11.2, 2.1, 2.2, 2.4.2 (only paragraph entitled "silicon sensors"), 2.6.2 from Castleman book; Sections 1 and 2, up to Eq (2), from paper. 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 highdynamicrange photography to capture outtdoor illumination and reuse it for image synthesis) 
No tutorial during week 1.  
Fri, Jan 10  Assignment  Assignment 1 is out, due January 27.  
Week 2  
Wed, Jan 15  Pixel components: color and alpha  Color image acquisition; alpha matting and the matting equation
Slides: [pdf] 

Tutorial  Matting and linear equations  
Part II: Representing images as continuous 1D and 2D functions  
Week 3  
Wed, Jan 22  Computing 1D image derivatives  Leastsquares polynomial fitting; intensity derivatives; weighted least squares; RANSAC
Slides: [pdf] 
Polynomial fitting demo to be shown in next week's tutorial: polydemo.zip. If you download it, run it for a variety of fits (LS, WLS, 1st degree, 2nd degree, etc) to see their effect. 

Tutorial  Questions about Assignment 1  
Week 4  
Mon, Jan 27  Assignment  Assignment 1 due at 11:59pm.  Worth 10% of the final mark.  
Wed, Jan 29  Representing 2D image curves  Local analysis of curves: the tangent & normal vectors, the moving frame
Slides: [pdf] 
To run the demo shown in class, unpack the zipfile curvedemo.zip You should run the demo for a variety of fits (LS, WLS, 1st degree, 2nd degree, etc) to see their effect on the estimated curve.  
Tutorial  Polynomial fitting and 2D curves  Tutorial notes on polynomial fitting and 2D curves by Micha Livne.  
Week 5  
Wed, Feb 5  Edge detection  Local analysis of 1D and 2D image patches: the Image Gradient. Case study: Painterly Rendering
Slides: [pdf]  Paper by Litwinowicz on painterly rendering (this is not required reading).  
Tutorial  Description of A2, Questions and answers about Assignment 1, part B [slides], and Paper on Accidental Pinhole and Pinspeck cameras (this is not required reading).  
Fri, Feb 7  Assignment  Assignment 2 is out, due March 2 (two days later than originally advertised!).  
Week 6  
Wed, Feb 12  Corner detection, Intelligent Scissors  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 noncylindrical points through eigenvalue analysis of the Hessian of the Laplacian
Slides: [pdf]  Paper by Mortensen on Intelligent Scissors (this is not required reading). This paper and the technique will be covered in one of the forthcoming tutorials.  
Tutorial  Questions about Assignment 2 and a paper on Object Recognition by Pedro F. Felzenszwalb. [slides]  
Week 7  
Wed, Feb 19  No lecture  Reading week  
No tutorial  
Part III: Multiresolution image representations  
Week 8  
Wed, Feb 26  Template matching and correlation  Representing images as vectors; evaluating similarity using RMS distance error, crosscorrelation and normalized crosscorrelation; Slides: [pdf] 
To run the demo shown in class, unpack the zipfile corrdemo.zip and type corrdemo at the matlab prompt.  
Tutorial  TBD  
Sun, Mar 02  Assignment  Assignment 2 due at 11:59pm.  Worth 10% of the final mark.  
Week 9  
Wed, Mar 5  Principal Component Analysis  Face recognition using Eigenfaces Slides: [pdf] 
Section 13.6 from Castleman  To run the demo shown in class, unpack the zipfile recognition_demo.zip and type pca_recdemo at the matlab prompt. 
Midterm  BA 1190, 6 pm  Worth 20% of the final mark  
Wed, Mar 5  Assignment  Assignment 3 is out, due March 19.  
Part IV: Alternative image representations  
Week 10  
Wed, Mar 12  Gaussian Pyramids  The idea of representing an image at multiple resolutions is described and studied using Gaussian and Laplacian Pyramids. Slides: [pdf] 
Original paper by Burt and Adelson on the Gauss/Laplacian pyramids. You should read up to, but not including, section entitled Entropy.  This Matlab demo shows how a 1D image changes as we smooth it with a sequence of Gaussians of increasing standard deviation 
Tutorial  TBD  
Week 11  
Wed, Mar 19  The Haar Wavelet Transform  Wavelet compression of 1D and 2D images
Slides: [pdf]  A tutorial paper on the Haar Wavelets.  Matlab wavelets demo shown in class. 
Wed, Mar 19  Assignment  Assignment 3 due at 11:59pm.  Worth 10% of the final mark.  
Wed, Mar 19  Assignment  Assignment 4 is out, due April 2.  
Tutorial  PCA, a few theoretical details.  
Week 12  
Wed, Mar 26  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; SIFTbased feature detection; the SIFT descriptor; image matching using SIFT
Slides: [pdf] 
Sections 13 of paper by David Lowe describing SIFT.  Web page on SIFT (with demo code) 
Tutorial  On Morphing Slides: [pdf] 

Part V: Introduction to 2D Image Transformations  
Week 13  
Wed, Apr 2  Homogeneous coordinates  Homographybased image warping
Slides: [pdf] 

Tutorial  TBD  
Wed, Apr 4  Assignment  Assignment 4 due at 11:59pm.  Worth 10% of the final mark.  
Apr 930  Final Exam  Location, time and date to be announced  Worth 40% of the final mark  