Linear Algebra and Image Processing
Topics Vectors and Matrices Vector Spaces Eigenvalues and Eigenvectors Digital Images - Basic Concepts Histograms Spatial Filtering
Vectors Scalar – single value Vector – tuple of values Dimension – Cardinality of vector* Standard operations Inner product, Outer product Usage
Matrices Matrix – 2D vector* Dimensions Standard operations Matrix multiplication Trace and determinant Rows and columns Matrix types Usage
Vector Spaces A collection of vectors over a field Supports addition and scalar multiplication Satisfies: Examples
Vector Space Properties Also true: Linear combination Linearly independent vectors
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
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
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
Solving for Eigenvalues Characteristic polynomial Roots are eigenvalues of A Algebraic and geometric multiplicities Diagonalization: Importance
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
Numerical Linear Algebra Further reading QR LU SVD …
Digital Images - Basic Concepts Digital image – A matrix of pixels Pixel – Smallest picture element Digital image acquisition: Optics Sampling Quantization
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
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
Histogram Importance Brightness and contrast Information theory Image matching Local features
Spatial Convolution Convolution in 1D Convolution in 2D Usage Filtering Edge Detection Template matching
Linear Filtering Linear combination of image and filter Examples Averaging Gaussian Laplacian
Non-Linear Filtering Not all filters can be formulated as matrices Minimum, Maximum Median filter Frequency mixer Energy transfer filter …
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?
Examples Wallis filter – local dynamic range correction Edge based denoising Importance for Computer Vision