EEM 463 Introduction to Image Processing Week 1 ... 463... · EEM 463 Introduction to Image...

EEM 463 Introduction to Image Processing

Week 1: Introduction and Fundamentals

Fall 2013

Instructor: Hatice Çınar Akakın, Ph.D.

haticecinarakakin@anadolu.edu.tr

Anadolu University

Recommended text book

• R.C.Gonzalez, R.E.Woods, Digital Image Processing, Third edition, 2008.

Prerequisites• Math and programming background

• Familiar with Matlab (tutorials will be provided)

• Signal processing, familiarity with calculus, linear algebraand probability (basic tutorials will be provided)

• Being enthusiastic to learn this popular and fun subject!

Grading

• 2 midterms,: Each 15%

• Assignments: 20%

• Projects: 25%

• Final: 25%

• Extra Credits can be earned if you achieve remarkable results fromyour Course Project!!

Course Project

• It will be mostly chosen from the topics that will not be able tocovered during this course

• Small groups (eg. 2 persons in each group)

• Course Project Timeline

• Proposal due: October 8, 2013

• Progress due: November 26, 2013 (submit 2 pages of your progressreport)

• Presentations @ 2nd week of Final Exams in January 2014 (tentatively!)

• Report due same day of presentations!

Introduction

• Image Processing is a subcategory of Digital Signal Processing, whichdeals withs images

• It is a gigantic and growing subject area that can not be covered in one semester

• Wide range of application areas• Engineering (electrical, computer, biomedical)

• Computer Science, Mathematics

• Aims to improve image quality for• Human perception (subjective)

• Computer interpretation (objective)

Introduction

• Image processing is related to two other fields as follows:• Image Analysis (Image Understanding)

• and Computer Vision (emulate human vision)

• Three types of computerized processes:• Low-level processes (e.g. preprocessings ) such as:

• Noise reduction, contrast enhancement, image sharpening

Low-levelProcesses

Input: image

Output:image

Introduction• Mid-level Processes

• E.g., Segmentation• input: images, output:attributes (edges, contours, etc.)

• High-level Processes• Classification, Recognition

Mid-levelProcesses

Input: image

Output:Attributes

High-levelProcesses

Input: Attributes

Output:Recognizing objects

Introduction

• An image may be defined as two-dimensional (2D) function f(x,y), wherex and y are spatial coordinates.

• The amplitude of f is called intensity orgray level at the given point (x,y).

• If x,y and and intensity (f )values are allfinite and discrete quantities then it is called digital image.

• In a digital image, point = pixel

Origins of Digital Image Processing

Newspaper Industry: Pictures were sent by Bartlane cable picture between London and New York in early 1920.

The introduction of the Bartlane Cable reduced the transmission time from a weekto three hours

Specialized printing equipment coded picturesfor transmission and then reconstructed them at the receiving end.Sent by submarine cable

between London and New York

Origins of Digital Image Processing

This image, based on photographic

Reproduction, made from tapes perforated at the

telegraph receiving terminal was used. Five distinct

levels of gray were coded.

Origins of Digital Image Processing

Geometric correction and image enhancement applied to Ranger 7 pictures of the moon. Work conducted at the Jet Propulsion Laboratory.

Sources of Images

Major uses

Gamma-ray imaging: nuclear medicine and astronomical observations

X-rays: medical diagnostics, industry, and astronomy, etc.

Ultraviolet: lithography, industrial inspection, microscopy, lasers, biological imaging,and astronomical observations

Visible and infrared bands: light microscopy, astronomy, remote sensing, industry, and law enforcement

Microwave band: radar

Radio band: medicine (such as MRI) and astronomy

• The principal energy source for images is theelectromagnetic energy spectrum.• EM waves = stream of massless (proton) particles,each traveling in a wavelike pattern at the speed oflight. Spectral bands are grouped by energy/photon- Gamma rays, X-rays, UV, Visible, Infrared, Microwaves, radio waves• Other sources: acoustic, ultrasonic, electronic

Applications

• Gamma-Ray Imaging• Nuclear medicine: inject

radioactive isotope thatemits gamma-ray as it decays

• Images are producedfrom the emissionscollected by gamma-ray detectors

• Used to locate infectionswithin the bone pathology

Cygnus

Loop

Applications

• X-Ray Imaging

X-rays are among the oldest sources

of EM radiation used for imaging

Main usage is in medical imaging (X-rays, CAT scans, angiography)

ApplicationsUltraviolet Imaging• Used for lithography, industrialinspection, flourescencemicroscopy, lasers, biologicalimaging, and astronomy• Photon of UV light collides withelectron of fluorescent materialto elevate its energy. Then, itsenergy falls and it emits redlight.

Corn

Applications

• Satellite Infrared Imaging• LANDSAT

Remote sensing

Applications

• Infrared Imaging

Night-time lights, provides a global

inventory of humansettlement

Applications

• Visible and Infrared Imaging

Inspection of manufacturedgoods

• Detecting the missingcomponents,

• Missing pills

• Anomalies in product coloror in shape

• visiual defects

Applications

• Finger print matching

• Automated license platereading

Applications

• Microwave Imaging• Radar is dominant

application

• Microwave pulses are sent out to illuminate scene

• Antenna receives reflected microwave energy

Applications

• Radio-Band Imaging (e.g. MRI)

• places patient in a powerful magnet

• passes radio waves through body in short pulses

• each pulse causes a responding pulse of radio waves to

• be emitted by patient’s tissues

• Location and strength of signal is recorded to form image

In medicine

In astronomy

Other Modalities: Ultrasound

Used in geological exploration, industry and medicine:

• transmit high-freq (1-5 MHz) sound pulses into body

• record reflected waves

• calculate distance from probe to tissue/organ using the

• speed of sound (1540 m/s) and time of echo’s return

• display distance and intensities of echoes as a 2D image

Other Modalities: Scanning ElectronMicroscope (SEM)

Electron microscopy: use of focused beam of

electrons instead of light to image a specimen

Other Modalities: Images generated bycomputer

Applications of computergenerated images:medical training, criminalforensics, special effects…

Fundamental Steps in Digital Image Processing

Digital Image Fundamentals

Human Visual Perception

32 steps in gray level

64 steps in gray level

How many different gray levels can humans see?People can distinguish more than 5 bits but less than 6 bits.

Human Visual Perception

• Is our perception of gray level affected by surrounding

brightness?

Is the gray level the same at the left sideof each panel as it is at the right side?

Human Brightness Perception

A region’s perceived brightness

does not depend simply on its

intensity. It is also related to the

surrounding background.

Visible Spectrum

► Monochromatic light: void of color

Intensity is the only attribute, from black to white

Monochromatic images are referred to as gray-scale images

► Chromatic light bands: 0.43(violet) to 0.79(red) µm (wavelength)

The quality of a chromatic light source:

Radiance: total amount of energy that flows from the light source (Watts, W)Luminance (lm): the amount of energy an observer perceives from a light sourceBrightness: a subjective descriptor of light perception that is impossible to measure. It embodies the achromatic notion of intensity and one of the key factors in describing color sensation.

ExampleLight emitted from a far infrared source have high

radiance, but almost no luminance

Typical values for illumination

• Illumination Lumen — A unit of light flow or luminous flux

Lumen per square meter (lm/m2) — The metric unit of measure for illuminance of a surface

• On a clear day, the sun may produce in excess of 90,000 lm/m2 of illumination on the surface of the Earth

• On a cloudy day, the sun may produce less than 10,000 lm/m2 of illumination on the surface of the Earth

• On a clear evening, the moon yields about 0.1 lm/m2 of illumination

• The typical illumination level in a commercial office is about 1000 lm/m2

Typical values for reflectance

• Reflectance

• 0.01 for black velvet

• 0.65 for stainless steel

• 0.80 for flat-white wall paint

• 0.90 for silver-plated metal

• 0.93 for snow

Image Acquisition Process• Individual sensors are arranged in the form of a 2D array.

• Used in digital cameras and camcorders.

• Entire image formed at once; no motion necessary.

A simple Image Formation Model

• f(x,y)=i(x,y) * r(x,y),where

f(x,y) : intensity at point (x,y)

i(x,y) : the amount of illumination

incident to the scene

r(x,y) : reflectance/transmissivity

from the objects

Note that transmissivity term t(x,y)is used for chest X-ray.

1),(0

),(0

yxr

yxi

• For monochrome images:• l = f(x,y)where

• L_min < l < L_max• L_min >0• L_max should be finite

The interval [L_min, L_max] is called the gray (intensity) scaleIn practice:The gray scale interval is in the range of [0, L-1], where l=0 is considered black and l=L-1 is considered white and all intermediate values are different shades of gray varyingfrom black to white.

• Note that, in practice, the interval is shifted to the [0, 255] range so that intensity can be represented in one byte (unsigned char).

Sampling and Quantization• Digital computers cannot process parameters that vary in continuum.

• We have to discretize:

• x, y xi, yj elements of (i = 0,…,N-1, j = 0:…,M-1) : Sampling (quantization of spatial coordinates)

• f(xi, yj) f’(xi, yj) : Quantization (digitize intensity level L)

Sampling &Quantization

Continuous Discrete

Note that, quantization refers to the mapping of

real numbers onto a finite set: a many-to-one

mapping.

Akin to casting from double precision to an integer.

Sampling and Quantization

Digitizing the coordinate values

Digitizing the amplitude values

Representing Digital Images

Representing Digital Images

(0,0) (0,1) ... (0, 1)

(1,0) (1,1) ... (1, 1)( , )

... ... ... ...

( 1,0) ( 1,1) ... ( 1, 1)

f f f N

f f f Nf x y

f M f M f M N

The representation of an M×N numerical array as:

Used forprocessing andalgorithmdevelopment

(1,1) (1,2) ... (1, )

(2,1) (2,2) ... (2, )( , )

... ... ... ...

( ,1) ( , 2) ... ( , )

f f f N

f f f Nf x y

f M f M f M N

Matlabrepresentation

• Recalling the image formation operations we have discussed, note that

the image f (x,y) is an MxN matrix with integer entries in the range 0, . . ., 255.

• In MATLAB, we usually denote an image as a matrix “A” (or B, . . . , etc.) with elements A(x,y) {0, . . .,255} for x = 1, . . ., M and y = 1, . . ., N

• We will be processing matrices!

• Warning: Some processing we will do will take an image A with integerentries and convert it into a new matrix B which may not have integerentries!

• In these cases we must suitably scale and round the elements of B in order to display it as an image.

Representing Digital Images

• Discrete intensity interval [0, L-1], L=2k

• The number b of bits required to store a M × N digitized image

b = M × N × k

where k is the number of bits/pixel

Example : The size of a 1024 x 1024 8bits/pixel image is 220 bytes = 1 MBytes

Spatial Resolution

• Defined as the smallest discernable detail in an image.

• Widely used definition: smallest number of discernable line pairs per unit distance (100 line pairs/millimeter).

• A line pair consists of one line and its adjacent space.

• When an actual measure of physical resolution is not necessary, it is common to refer to an MxN image as having spatial resolution of MxN pixels.

Image Data Formats

Binary: 0 & 1 (mainly line drawing ordocuments)Gray Scale (Levels): 0 (black), shadesof gray, 2^m - 1 (white).Color: three primary color components,e.g. Red (R), Green (G), Blue (B).Resolution: 1024x1024, 512x512,256x256, 352x240, ...

Effect of Sampling

A 1024x1024 image is sub-sampled to 32x32. Number of gray levels is the same

Images up-sampled to 1024x1024Starting from 1024, 512,256,128,64, and 32

Intensity (Gray-level) Resolution

• Defined as the smallest discernable change in gray level.

• Highly subjective process.

• The number of gray levels is usually a power of two:• k bits of resolution yields 2k gray levels.

• When k=8, there are 256 gray levels ← most typical case

• Black-and-white television uses k=6, or 64 gray levels.

Effect of Quantization• Number of gray levels reduced by dropping bits from k=8 to k=1

• Spatial resolution remains constant.

Notice falsecontouring incoarselyquantizedimages.

Project requirements: 1. One-page project proposal: October 8, 20132. Two-page project progress write-up: November 26, 2013 3. Project presentation with a short program demo: 2nd week of Finals, January, 20144. Project report with a short program description: due same day of Project Presentation 5. Matlab program delivery: due same day of Project Presentation

Course PoliciesDiscussions among students for course assignments and projects are encouraged. However, what you submit should be output of your own efforts and work!