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

▶
1 fundamentals of digital imaging and human perception
SMART_READER_LITE
LIVE PREVIEW

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

Sep 11, 2023 113 likes •567 views

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

slide-1
SLIDE 1

MTAT.03.260 Pattern Recognition and Image Analysis 1

  • 1. Fundamentals of digital imaging

and human perception

Silver Leinberg

slide-2
SLIDE 2

MTAT.03.260 Pattern Recognition and Image Analysis 2

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
slide-3
SLIDE 3

MTAT.03.260 Pattern Recognition and Image Analysis 3

Contents

  • Origins
  • Various digital image processing fields
  • Human perception
  • Basics in digital image processing
  • Programming environment
slide-4
SLIDE 4

MTAT.03.260 Pattern Recognition and Image Analysis 4

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

slide-5
SLIDE 5

MTAT.03.260 Pattern Recognition and Image Analysis 5

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

slide-6
SLIDE 6

MTAT.03.260 Pattern Recognition and Image Analysis 6

Contents

  • Origins
  • Various digital image processing fields
  • Human perception
  • Basics in digital image processing
  • Programming environment
slide-7
SLIDE 7

MTAT.03.260 Pattern Recognition and Image Analysis 7

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
slide-8
SLIDE 8

MTAT.03.260 Pattern Recognition and Image Analysis 8

EM spectrum

slide-9
SLIDE 9

MTAT.03.260 Pattern Recognition and Image Analysis 9

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
slide-10
SLIDE 10

MTAT.03.260 Pattern Recognition and Image Analysis 10

X-ray imaging

  • Medical diagnostics

– 2D: X-ray photography, contrast enhancement

radiography (angiography)

– 3D: Computerized axial tomography (CAT)

  • Industry

– Circuit board inspection

  • Astronomy
slide-11
SLIDE 11

MTAT.03.260 Pattern Recognition and Image Analysis 11

Imaging in the UV Band

  • Fluorescence microscopy

– Invisible ultraviolet light makes fluorescent

material to shine in visible region

  • Astronomy
slide-12
SLIDE 12

MTAT.03.260 Pattern Recognition and Image Analysis 12

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.)

slide-13
SLIDE 13

MTAT.03.260 Pattern Recognition and Image Analysis 13

Imaging in the Microwave Band

  • Radar

– Radiates microwave pulses to illuminate an area

  • f interest and registers microwaves that was

reflected back to radar antenna.

slide-14
SLIDE 14

MTAT.03.260 Pattern Recognition and Image Analysis 14

Imaging in the Radio Band

  • Medicine

– Magnetic resonance imaging (MRI)

  • Astronomy
slide-15
SLIDE 15

MTAT.03.260 Pattern Recognition and Image Analysis 15

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)

slide-16
SLIDE 16

MTAT.03.260 Pattern Recognition and Image Analysis 16

Contents

  • Origins
  • Various digital image processing fields
  • Human perception
  • Basics in digital image processing
  • Programming environment
slide-17
SLIDE 17

MTAT.03.260 Pattern Recognition and Image Analysis 17

Structure of the Human Eye

slide-18
SLIDE 18

MTAT.03.260 Pattern Recognition and Image Analysis 18

Cones and Rods

  • Cones:

– 6..7 million – Located in centre of

retina (fovea)

– Highly sensitive to

colour

– Bright-light

(photopic) vision

  • Rods:

– 75..150 million – Distributed over the

retina

– Not involved in

colour vision

– Dim-light (scotopic)

vision

slide-19
SLIDE 19

MTAT.03.260 Pattern Recognition and Image Analysis 19

Cones and Rods

slide-20
SLIDE 20

MTAT.03.260 Pattern Recognition and Image Analysis 20

Colour sensing

slide-21
SLIDE 21

MTAT.03.260 Pattern Recognition and Image Analysis 21

Colour sensing (stare at the dot)

slide-22
SLIDE 22

MTAT.03.260 Pattern Recognition and Image Analysis 22

Brightness Adaptation and Discrimination

slide-23
SLIDE 23

MTAT.03.260 Pattern Recognition and Image Analysis 23

Brightness Adaptation and Discrimination

slide-24
SLIDE 24

MTAT.03.260 Pattern Recognition and Image Analysis 24

Illusions

slide-25
SLIDE 25

MTAT.03.260 Pattern Recognition and Image Analysis 25

Contents

  • Origins
  • Various digital image processing fields
  • Human perception
  • Basics in digital image processing
  • Programming environment
slide-26
SLIDE 26

MTAT.03.260 Pattern Recognition and Image Analysis 26

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
slide-27
SLIDE 27

MTAT.03.260 Pattern Recognition and Image Analysis 27

White LED spectrum

slide-28
SLIDE 28

MTAT.03.260 Pattern Recognition and Image Analysis 28

Image Sensing and Acquisition

  • Sensor arrangement

– Single imaging sensor (SEM) – Line sensor (scanner, CAT, PET, MRI) – Array sensor (CCD, CMOS)

slide-29
SLIDE 29

MTAT.03.260 Pattern Recognition and Image Analysis 29

Image Sensing and Acquisition

slide-30
SLIDE 30

MTAT.03.260 Pattern Recognition and Image Analysis 30

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)

Lmin ≤ l ≤ Lmax

  • Gray scale [Lmin, Lmax]

– [0, L-1], L = 2^k – dynamic range

slide-31
SLIDE 31

MTAT.03.260 Pattern Recognition and Image Analysis 31

Image Sampling and Quantization

  • Digitalizing

– by coordinate values – sampling (M x N) – by amplitude values – quantization (L = 2^k)

slide-32
SLIDE 32

MTAT.03.260 Pattern Recognition and Image Analysis 32

Sampling

slide-33
SLIDE 33

MTAT.03.260 Pattern Recognition and Image Analysis 33

Quantization

slide-34
SLIDE 34

MTAT.03.260 Pattern Recognition and Image Analysis 34

Quantization

slide-35
SLIDE 35

MTAT.03.260 Pattern Recognition and Image Analysis 35

Zooming and Shrinking

  • Zooming

– nearest neighbour interpolation

  • pixel replication
  • bilinear interpolation
  • Shrinking

– aliasing effect

  • blurring
slide-36
SLIDE 36

MTAT.03.260 Pattern Recognition and Image Analysis 36

Relationships Between Pixels

  • Neighbours of a pixel

– N4(p), horizontal and vertical neighbours – ND(p), diagonal neighbours – N8(p) = N4(p) + ND(p)

slide-37
SLIDE 37

MTAT.03.260 Pattern Recognition and Image Analysis 37

Relationships Between Pixels

  • Adjacency

– 4-adjacency: same value & in N4 – 8-adjacency: same value & in N8 – m(ixed)-adjacency: same value &

  • In N4 or
  • In ND, without common 4-adjacent neighbour
  • Closed path, connected set, region, boundary
slide-38
SLIDE 38

MTAT.03.260 Pattern Recognition and Image Analysis 38

Relationships Between Pixels

  • Distance

– Euclidean distance: De(p,q)=[(x-s)²+(y-t)²]^½ – D4 distance: D4(p,q) = |x - s| + |y – t| – D8 distance: D8(p,q) = max(|x – s|, |y – t|) – Dm distance

slide-39
SLIDE 39

MTAT.03.260 Pattern Recognition and Image Analysis 39

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

slide-40
SLIDE 40

MTAT.03.260 Pattern Recognition and Image Analysis 40

Contents

  • Origins
  • Various digital image processing fields
  • Human perception
  • Basics in digital image processing
  • Programming environment
slide-41
SLIDE 41

MTAT.03.260 Pattern Recognition and Image Analysis 41

Scilab: Basic Matrix Operations

– -->zeros(4,5), ones(2, 3) – -->rand(2, 3) – -->A = [11 12; 21 22] – -->A(1, 2) – -->A(1, 2:-1:1) – -->A(1, 1:2) – -->A(1, :) – -->A(:) – -->A(:,2) = 0 – -->size(A, 2) – -->linspace(3, 1, 5) – -->sum(A) – -->plot(A(1,:)) – ==, ~=, >, >=, <,

<=, &, |, ~

– -, +, *, .*, /, ./, \, .\,

^, .^, ', .'

– Transpose -->A.'

slide-42
SLIDE 42

MTAT.03.260 Pattern Recognition and Image Analysis 42

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
slide-43
SLIDE 43

MTAT.03.260 Pattern Recognition and Image Analysis 43

Scilab: Basic Image Operations

  • imadd(im1, im2)
  • imsubtract(im1, im2)
  • immultiply(im1, im2)
  • imdivide(im1, im2)
  • imabsdiff(im1, im2)
  • imcomplement(im)
  • Imlincomb(...)
slide-44
SLIDE 44

MTAT.03.260 Pattern Recognition and Image Analysis 44

Scilab: Various Commands

  • tic, toc – for timing
  • -->T = input('enter data')
  • strcmp(string1, string2) – compare strings
  • -->help
  • -->help functionName