Embed Size (px)
Citation preview
EEM 463 Introduction to Image Processing
Week 1: Introduction and Fundamentals
Fall 2013
Instructor: Hatice Çınar Akakın, Ph.D.
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!
