Date 
Content of the Lecture 
Assignments/Readings/Notes 
18/07 (Thurs) 


22/07 (Mon) 
 Image basics: spatial resolution, intensity quantization, pixel; Image Alignment and need for it; Image Motion models: translation, rotation, scaling, shearing (affine and rigid); Image warping: forward and reverse;
Image alignment using control points; Image alignment using sum of squared differences; Field of view changes in image alignment


23/07 (Tue) 

Image alignment when intensity profiles are different: concept of image
histogram, joint image histogram; entropy of a random variable, joint
entropy; use of joint entropy for image alignment; mention of some
related applications: template matching, image panoramas.


Slides
 Assignment 0: download MATLAB from here and
install
it on your
machine. Start playing around with it!
 Read section 2.6.5 of Gonzalez

25/07 (Thurs) 

Image enhancement: histogram equalization: derivation and example, histogram specification; Smoothing filters: mean filter, weighted mean filter, median filter


Slides

Read section 3.2.4 and 3.3 of Gonzalez

29/07 (Mon) 


30/07 (Tue) 

Mean and median filtering, Gaussian and impulse noise, conceptual difference between mean and median, Image derivatives: first and second, gradient magnitude, image Laplacian, rotational
invariance of gradient magnitude and Laplacian, image sharpening using the Laplacian, unsharp masking


Slides

Sections 3.5 and 3.6 (upto and including 3.6.3 only) of Gonzalez

01/08 (Thurs) 

Fourier analysis: Fourier series for periodic signals, Fourier transform of aperiodic signals and inverse Fourier transform, properties of the Fourier transform: linearity, scaling, shifting, duality; Fourier
transform of the rect and sinc signals; Dirac delta function; convolution theorem; twodimensional Fourier transform


05/08 (Mon) 

Discrete fourier transform (DFT) and its inverse, matrix representation of DFT, DFT in 2D, Fourier spectrum and phase, MATLAB examples: effect of translation and rotation on Fourier spectrum, decay of spectrum values,
reconstruction of signal by removing some frequency components; discrete convolution and convolution theorem, MATLAB examples


06/08 (Tue) 

Filter design in the frequency domain: lowpass (ideal and Gaussian), highpass (ideal and Gaussian), phenomenon of ripples (Gibb's phenomenon) in ideal lowpass and highpass filtering, bandpass filters;
Motivation for convolution  in case of linear timeinvariant (or, equivalently, spaceinvariant systems), examples of linear and nonlinear systems, examples of timeinvariant and timevariant systems


08/08 (Thurs) 

Applications of filtering: photomosaics and hybrid images (How do
you think a hybrid image is generated given two constituent images?); Notch
filters and Moire patterns; Derivation
of
Fourier
Transform of a sampled signal, Shannon's sampling theorem for
bandlimited signals
and the WhittakerShannon interpolation formula, Aliasing due to undersampling and examples thereof; Derivation of the Discrete Fourier Transform of a sampled signal.


12/08 (Mon) 

Heat equation and its application to image processing; Equivalence
between execution of heat equation and Gaussian convolution; Numerical
simulation of Heat equation (with Neumann boundary conditions); Introduction to anisotropic diffusion (to be continued)


13/08 (Tue) 

Gradient vector and divergence; Anisotropic diffusion: Perona and Malik PDE; generation of interesting blurs in an image using diffusion PDEs: concept of directional derivatives
of first and second order.


19/08 (Mon) 

Recap of PDEs for diffusion; Edge detection  first and second derivative operators in 1D and 2D, Prewiit and Sobel masks


HW2 out (due 30th August before 11:59 pm)

Slides

Read sections 10.2 (upto 10.2.6) of Gonzalez

20/08 (Tue) 

Edge detection: MarrHildreth edge detector (using Laplacian of Gaussians) and Canny edge detector; Review of Fourier transforms: Fourier transform of vertical and horizontal edges/ridges


Slides

Read sections 10.2 (upto 10.2.6) of Gonzalez

22/08 (Thurs) 

Face recognition: intro; Principal components analysis for face recognition (eigenfaces): intro, concept of covariance matrix


26/08 (Mon) 


27/08 (Tue) 

Eigenfaces continued: detailed derivation of PCA


29/08 (Thurs) 

Eigenfaces: wrapup  personspecific eigenspaces versus single eigenspace for the whole database; PCA for compression of a database of similar images.
Singular value decomposition (SVD) of a matrix, SVD of a natural image and its application in image compression.


3/9 (Mon) 

Image restoration: degradation models, inverse filter, wiener filter, motion blur


04/09 (Tue) 

Motion blur, fluttershutter camera (side topic: not on exam), leastsquares restoration and its relation to inverse filter


06/09 (Thurs) 


09/09 to 16/09 


17/09 (Tue) 

Demonstration of inverse filter with coded aperture blur,
Least squares restoration, Circulant matrices and least squares restoration, block circulant matrices, regularized least squares restoration


19/09 (Tue) 

Distribution of midterm papers


23/09 (Mon) 

Color image processing: color perception  rods and cones in the retina, RGB, CMY(K) color models, halftoning for printing, a colorbased optical illusion


24/09 (Tue) 

HSI color model: derivation of HSI from RGB, concept of hue, saturation, intensityin HSI; lighting model involving ambient, diffuse and specular reflections; practical applications of hue  invariance
of hue to change in ambient lighting and specular reflections,


26/09 (Thurs) 

Hue  advantages, chromaticity vector, problems with hue and chromaticity; Color image processing: histogram equalization, bilateral filtering, color edge detection examples; PCA of RGB triples (to be
continued)


30/9 (Mon) 

YCbCr color space; hyperspectral images;
Introduction to image segmentation  what is image segmentation? applications of image segmentation; clustering algorithms, kmeans


1/10 (Tue) 

Kmeans clustering algorithm; probability density estimation and its relation to clustering (see MATLAB code here (for 1D) and here (for
2D)), kernel density estimation


3/10 (Thurs) 

Mean shift algorithm, movation for algorithm, kernel density estimation, applications in image segmentation, smoothing and splicing


7/10 (Mon) 


8/10 (Tue) 


10/10 (Thurs) 

OpenCV:
 Installation of OpenCV 2.4.6 on Windows
(requires Visual Studio on your machine  pay careful attention to the information on path
settings, mentioned by the uploader below the video clip)
 Download OpenCV 2.4.6
 OpenCV samples programs (canny edge detector, erosion and dilation, object tracking and many more will be in the samples directory when you install opencv)


14/10 (Mon) 
 Recap of image segmentation: mean shift and snakes, discussion for HW5


15/10 (Tue) 

Image compression: lossy and lossless, motivation for lossy compression for images; OVerview of JPEG standard; discrete cosine transform in 1D and 2D, properties of DCT matrix, computation of DCT using
FFT, energy compaction of DCT, experiments with natural images to show relationship between DCT and PCA (principal components analysis)


17/10 (Thurs) 

JPEG standard: use of DCT, quantization of DCT coefficients, Huffman encoding, overview of JPEG encoder and decoder


21/10 (Mon) 

JPEG decoder, JPEG artifacts, JPEG for color images; MPEG standard overview: concept of predictive coding, concept of predictive coding with motion compensation, Iframes (also called keyframes),
Pframes,
Bframes


Slides
 Read section 8.2.9 of the book

22/10 (Tue) 

Wrapup of MPEG standard: motion compensation;
Introduction to image retrieval


24/10 (Thurs) 

Introduction to image retrieval; histograms in 1D, 2D, 3D; distance measures to compare histograms: L1,L2, KullbackLeibler, Jeffreys's divergence; Concept of Earth Mover's distance


28/10 (Mon) 

Earth movers distance; retrieval experiments  concept of precision and recall, partial matches in color histogram based image retrieval; concept of texture


29/10 (Tue) 

Concept of texture  Gabor filters and Gabor filter banks, orientation and frequency selectivity of Gabor filters, biological motivation, texture retrieval experiments


31/10 (Thurs) 

Tomography: introduction, meaning of projection, overview of CT scanner, Radon Transform, Projection Slice Theorem


7/11 (Thurs) 

Tomography: projection slice theorem, backprojection and blur, filtered backprojection (Ramachandran Lakshminarayanan filter), relationship between filtered backprojection and simple backprojection,
properties of the Radon Transform

 Slides


(note: syllabus for the final exam covers all the topics from the beginning of the semester)


11/11 (Mon) 

Project discussion with students (one on one)



(note: syllabus for the final exam covers all the topics from the beginning of the semester)

 Hw solutions all in one place :)  HW1sol, HW2sol, HW3sol, HW4sol, HW5sol
