Date |
Content of the Lecture |
Assignments/Readings/Notes |
14/09 (Mon) |
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Face recognition: intro; Principal components analysis for face recognition (eigenfaces): intro, concept of covariance matrix, description of algorithm and its computational complexity; a faster
algorithm for PCA on a small (N) number of large-sized images (N << d case).
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21/09 (Mon) |
-
PCA: main principles, and derivation for k = 1 directions, and for k > 1 directions; choice of k in face recognition; concept of Lagrange multipliers
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24/09 (Thurs) |
-
Derivation sketch for k > 1 directions, concept of person/pose specific eigenspaces, a word about 3D face recognition;
Concept of SVD (Singular Value Decomposition): reduced form, formula using outer products, applications to image compression; Eckart Young theorem
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28/09 (Thurs) |
-
SVD: properties (determinant, frobenius norm, rank, inverse and pseudo-inverse), implementing eigenfaces using SVD, overview of some other applications of SVD
Image restoration: problem statement, simplifying assumptions; Models of blur: defocus and motion blur
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1/10 (Thurs) |
-
Derivation of Motion blur kernel under in-plane translation; Image restoration: inverse filter and problems with the inverse filter; spread-spectrum filters: coded aperture and flutter-shutter camera; Introduction to the Wiener filter
|
|
5/10 (Mon) |
-
Introduction to the Wiener filter: principles on which it based, criterion that it optimizes
- Derivation of Wiener filter, different variants of its formula
- Interactive Wiener filter
- Regularized deblurring filter (penalizing the derivatives)
- Introduction to PCA for denoising
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8/10 (Thurs) |
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12/10 (Mon) |
- Visible spectrum
- Color image perception: the theory of human perception based on the three types of cones
- RGB color model, CMY(K) color models; additive and subtractive color mixing; related optical illusion
- HSI color model, the concept of hue, saturation and intensity, the illumination invariant property of hue
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15/10 (Thurs) |
- HSI color model, the concept of hue, saturation and intensity, the illumination invariant property of hue
- Advantages and disadvantages of hue
- Concept of chromaticity vector
- Color image processing: color image histogram equalization, color image bilateral filtering, concept of edge in a color image as an objective function
using directional derivatives
- PCA of RGB values: reiterating the concept that PCA is a decorrelating transform (to be continued later while explaining the YCbCr color model)
|
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26/10 (Mon) |
- PCA of RGB values: reiterating the concept that PCA is a decorrelating transform
- YCbCr and YUV color spaces
- Hyperspectral image: concept, applications, visualization, PCA on hyperspectral image values
- Concept of color filter arrays, Bayer filter, demosaicing; demosaicing algorithm using bilateral filter
- Concept of compressed sensing and its relation to mosaicing and demosaicing
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29/10 (Thurs) |
- Lossless and lossy compression, importance of lossy compression for images/video
- Introduction to steps of the JPEG standard, concept of quality factor
- Discrete cosine transform: definition, properties, advantages over DFT
|
- Slides
- From the book by Gonzalez: sections 8.2.1, 8.2.8 (skip portion on Walsh Hadamard Transform)
- Section 5.6 from the book by Anil K Jain - for material pertaining to DCT, also see definition of Markov processes in section (2.9) (equations 2.67 and 2.68)
|
31/10 (Sat) (extra lecture) |
- Discrete cosine transform: definition, properties, advantages over DFT
- DCT and its relationship with PCA for a stationary first order Markov process with $\rho$ close to 1
- Quantization step in JPEG
- Huffman encoding and decoding
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2/11 (Mon) |
- Proof of relation between DCT and DFT
- Huffman encoding and run length encoding in JPEG
- JPEG compression for color images - in the YCbCr color space
- Modes of JPEG compression
- Introduction to video compression, concept of predictive encoding for video
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5/11 (Thurs) |
- Introduction to video compression, concept of predictive encoding for video
- First order differential encoding for video, motion compensated residuals
- Concept of P,B,I frames in MPEG
- Architecture of MPEG encoder and decoder
- Introduction to the concept of compressed sensing (not on exam)
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