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Linear Algebra and Image Processing

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Presentation on theme: "Linear Algebra and Image Processing"— Presentation transcript:

1 Linear Algebra and Image Processing

2 Topics Vectors and Matrices Vector Spaces Eigenvalues and Eigenvectors
Digital Images - Basic Concepts Histograms Spatial Filtering

3 Vectors Scalar – single value Vector – tuple of values
Dimension – Cardinality of vector* Standard operations Inner product, Outer product Usage

4 Matrices Matrix – 2D vector* Dimensions Standard operations
Matrix multiplication Trace and determinant Rows and columns Matrix types Usage

5 Vector Spaces A collection of vectors over a field
Supports addition and scalar multiplication Satisfies: Examples

6 Vector Space Properties
Also true: Linear combination Linearly independent vectors

7 Subspaces A subspace is a subset of vectors from the vector space.
It must be closed for addition and scalar multiplication Subspaces are vector spaces themselves Examples

8 Spanning Set and Basis A spanning set is a set of all possible linear combinations of A basis is a set of vectors satisfying Spanning the space Linearly independent Dimension – the length of the basis Examples

9 Eigenvalues and Eigenvectors
Eigenvector of a square matrix is a non-zero vector such that for some scalar The scalar is the matching Eigenvalue Number of non-zero eigenvalues = matrix rank Examples Importance

10 Solving for Eigenvalues
Characteristic polynomial Roots are eigenvalues of A Algebraic and geometric multiplicities Diagonalization: Importance

11 Properties of Eigenvalues
Trace – sum of eigenvalues Determinant – product of eigenvalues Power leads to A is invertible for non-zero eigenvalues only Invertible – power property holds for -1 A is hermitian – eigenvalues are real A is unitary – eigenvalues satisfy

12 Numerical Linear Algebra
Further reading QR LU SVD

13 Digital Images - Basic Concepts
Digital image – A matrix of pixels Pixel – Smallest picture element Digital image acquisition: Optics Sampling Quantization

14 Digital Image Processing
Representation - discrete signal, 1D or 2D Discrete convolution, discrete derivatives, … Discrete transforms (e.g. DFT, DCT) Notable applications Enhancement – Denoising, Inpainting, Debluring Compression Super-Resolution

15 Histogram Density function of the image
Statistical tool for estimation and processing Gray levels vs. number of occurrences Can be normalized  PDF Global, Invariant to order of pixels

16 Histogram Importance Brightness and contrast Information theory
Image matching Local features

17 Spatial Convolution Convolution in 1D Convolution in 2D Usage
Filtering Edge Detection Template matching

18 Linear Filtering Linear combination of image and filter Examples
Averaging Gaussian Laplacian

19 Non-Linear Filtering Not all filters can be formulated as matrices
Minimum, Maximum Median filter Frequency mixer Energy transfer filter

20 Adaptive Filtering Not all filters are space invariant
Image statistics may be local Corruption may be location dependent Different schemes at edges and at textures How to create location dependent filters?

21 Examples Wallis filter – local dynamic range correction
Edge based denoising Importance for Computer Vision

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