1. Fundamentals of digital imaging and human perception Silver - PowerPoint PPT Presentation

1. Fundamentals of digital imaging and human perception Silver Leinberg MTAT.03.260 Pattern Recognition and Image Analysis 1

What Is Digital Image Processing? ● Image may be defined as 2D function f(x,y) – x, y – spatial coordinates – f – grey level ● Image is called digital image, when f, x, y are finite and discrete quantities. ● Pixels ● Low-, mid- and high level processing MTAT.03.260 Pattern Recognition and Image Analysis 2

Contents ● Origins ● Various digital image processing fields ● Human perception ● Basics in digital image processing ● Programming environment MTAT.03.260 Pattern Recognition and Image Analysis 3

The Origins ● Digital images – Submarine cable between London and New York – 1920 Bartlane system with 5 levels of grey – 1929 15 levels of grey – 1964 pictures of moon taken by US spacecraft MTAT.03.260 Pattern Recognition and Image Analysis 4

The Origins ● Digital computers – 1940 key concepts by John von Neumann – 1948 transistor – 1958 integrated circuit – 1960s operating systems, high level programming languages (COBOL, FORTRAN) – 1970s microprocessor – 1981 personal computer MTAT.03.260 Pattern Recognition and Image Analysis 5

Contents ● Origins ● Various digital image processing fields ● Human perception ● Basics in digital image processing ● Programming environment MTAT.03.260 Pattern Recognition and Image Analysis 6

Applications by EM spectrum ● Gamma-ray Imaging ● X-ray Imaging ● Imaging in the Ultraviolet Band ● Imaging in the Visible and Infra-red Band ● Imaging in the Microwave Band ● Imaging in the Radio Band ● Other Imaging Modalities MTAT.03.260 Pattern Recognition and Image Analysis 7

EM spectrum MTAT.03.260 Pattern Recognition and Image Analysis 8

Gamma-ray Imaging ● Nuclear medicine – A small dose of radioactive isotope is injected to patient and images are produces by gamma ray detectors, positron emission tomography (PET) ● Astronomical observation ● Inspection of nuclear objects MTAT.03.260 Pattern Recognition and Image Analysis 9

X-ray imaging ● Medical diagnostics – 2D: X-ray photography, contrast enhancement radiography (angiography) – 3D: Computerized axial tomography (CAT) ● Industry – Circuit board inspection ● Astronomy MTAT.03.260 Pattern Recognition and Image Analysis 10

Imaging in the UV Band ● Fluorescence microscopy – Invisible ultraviolet light makes fluorescent material to shine in visible region ● Astronomy MTAT.03.260 Pattern Recognition and Image Analysis 11

Visible and Infra-red Band ● Microscopy ● Remote sensing ● Weather observation ● Automated inspection of products ● Law enforcement (fingerprints, reading serial numbers from paper currency, vehicle licence plate etc.) MTAT.03.260 Pattern Recognition and Image Analysis 12

Imaging in the Microwave Band ● Radar – Radiates microwave pulses to illuminate an area of interest and registers microwaves that was reflected back to radar antenna. MTAT.03.260 Pattern Recognition and Image Analysis 13

Imaging in the Radio Band ● Medicine – Magnetic resonance imaging (MRI) ● Astronomy MTAT.03.260 Pattern Recognition and Image Analysis 14

Other Imaging Modalities ● Acoustic imaging – Geological exploration (minerals, oil) – Industry – Medicine (imaging of unborn baby with ultrasound) ● Electron microscopy (SEM, TEM) ● Computer generated imaging (fractals, flight simulators) MTAT.03.260 Pattern Recognition and Image Analysis 15

Contents ● Origins ● Various digital image processing fields ● Human perception ● Basics in digital image processing ● Programming environment MTAT.03.260 Pattern Recognition and Image Analysis 16

Structure of the Human Eye MTAT.03.260 Pattern Recognition and Image Analysis 17

Cones and Rods ● Cones: ● Rods: – 6..7 million – 75..150 million – Located in centre of – Distributed over the retina (fovea) retina – Highly sensitive to – Not involved in colour colour vision – Bright-light – Dim-light (scotopic) (photopic) vision vision MTAT.03.260 Pattern Recognition and Image Analysis 18

Cones and Rods MTAT.03.260 Pattern Recognition and Image Analysis 19

Colour sensing MTAT.03.260 Pattern Recognition and Image Analysis 20

Colour sensing (stare at the dot) MTAT.03.260 Pattern Recognition and Image Analysis 21

Brightness Adaptation and Discrimination MTAT.03.260 Pattern Recognition and Image Analysis 22

Brightness Adaptation and Discrimination MTAT.03.260 Pattern Recognition and Image Analysis 23

Illusions MTAT.03.260 Pattern Recognition and Image Analysis 24

Contents ● Origins ● Various digital image processing fields ● Human perception ● Basics in digital image processing ● Programming environment MTAT.03.260 Pattern Recognition and Image Analysis 25

Light ● Wavelength ( λ), frequency (ν), energy (E) – λ = c / ν (400 nm .. 750 nm) – E = h * ν (3.1 eV .. 1.65 eV) ● Intensity: radiance, luminance, brightness ● Spectral distribution ● Polarisation MTAT.03.260 Pattern Recognition and Image Analysis 26

White LED spectrum MTAT.03.260 Pattern Recognition and Image Analysis 27

Image Sensing and Acquisition ● Sensor arrangement – Single imaging sensor (SEM) – Line sensor (scanner, CAT, PET, MRI) – Array sensor (CCD, CMOS) MTAT.03.260 Pattern Recognition and Image Analysis 28

Image Sensing and Acquisition MTAT.03.260 Pattern Recognition and Image Analysis 29

Image Formation Model ● f(x,y) = i(x,y) * r(x,y) – i(x,y) – illumination (90000 .. 0.1 lm/m2) – r(x,y) – reflectance or transmittance (0 .. 1) ● Gray level l = f(x,y) L min ≤ l ≤ L max ● Gray scale [L min , L max ] – [0, L-1], L = 2^k – dynamic range MTAT.03.260 Pattern Recognition and Image Analysis 30

Image Sampling and Quantization ● Digitalizing – by coordinate values – sampling (M x N) – by amplitude values – quantization (L = 2^k) MTAT.03.260 Pattern Recognition and Image Analysis 31

Sampling MTAT.03.260 Pattern Recognition and Image Analysis 32

Quantization MTAT.03.260 Pattern Recognition and Image Analysis 33

Quantization MTAT.03.260 Pattern Recognition and Image Analysis 34

Zooming and Shrinking ● Zooming – nearest neighbour interpolation ● pixel replication ● bilinear interpolation ● Shrinking – aliasing effect ● blurring MTAT.03.260 Pattern Recognition and Image Analysis 35

Relationships Between Pixels ● Neighbours of a pixel – N 4 (p), horizontal and vertical neighbours – N D (p), diagonal neighbours – N 8 (p) = N 4 (p) + N D (p) MTAT.03.260 Pattern Recognition and Image Analysis 36

Relationships Between Pixels ● Adjacency – 4-adjacency: same value & in N 4 – 8-adjacency: same value & in N 8 – m(ixed)-adjacency: same value & ● In N 4 or ● In N D , without common 4-adjacent neighbour ● Closed path, connected set, region, boundary MTAT.03.260 Pattern Recognition and Image Analysis 37

Relationships Between Pixels ● Distance – Euclidean distance: D e (p,q)=[(x-s) ² +(y-t) ² ]^ ½ – D 4 distance: D 4 (p,q) = | x - s | + | y – t | – D 8 distance: D 8 (p,q) = max(|x – s|, |y – t|) – D m distance MTAT.03.260 Pattern Recognition and Image Analysis 38

Linear and Non-linear Operations ● An operator H is said to be linear if H(af + bg) = aH(f) + bH(g) where a, b are scalars and f, g are images – Sum operator is linear – Absolute value of difference of two images in not MTAT.03.260 Pattern Recognition and Image Analysis 39

Contents ● Origins ● Various digital image processing fields ● Human perception ● Basics in digital image processing ● Programming environment MTAT.03.260 Pattern Recognition and Image Analysis 40

Scilab: Basic Matrix Operations – -->size(A, 2) – -->zeros(4,5), ones(2, 3) – -->linspace(3, 1, 5) – -->rand(2, 3) – -->sum(A) – -->A = [11 12; 21 22] – -->plot(A(1,:)) – -->A(1, 2) – ==, ~=, >, >=, <, – -->A(1, 2:-1:1) <=, &, |, ~ – -->A(1, 1:2) – -, +, *, .*, /, ./, \, .\, – -->A(1, :) ^, .^, ', .' – -->A(:) – Transpose -->A.' – -->A(:,2) = 0 MTAT.03.260 Pattern Recognition and Image Analysis 41

Scilab: Basic Image Operations – -->atomsInatall SIVP – -->f = imread('image1.bmp'); – -->imshow(f) – -->imwrite(f, 'image2.bmp') – -->g = im2double(f); – -->g = mat2gray(A) – -->th = 0.3, g = im2bw(f, th) ● th - treshold MTAT.03.260 Pattern Recognition and Image Analysis 42

Scilab: Basic Image Operations ● imadd(im1, im2) ● imsubtract(im1, im2) ● immultiply(im1, im2) ● imdivide(im1, im2) ● imabsdiff(im1, im2) ● imcomplement(im) ● Imlincomb(...) MTAT.03.260 Pattern Recognition and Image Analysis 43

Scilab: Various Commands ● tic, toc – for timing ● -->T = input('enter data') ● strcmp(string1, string2) – compare strings ● -->help ● -->help functionName MTAT.03.260 Pattern Recognition and Image Analysis 44