M. Wu: ENEE631 Digital Image Processing (Spring'09)
Digital Image and Video Processing –Digital Image and Video Processing –
An IntroductionAn Introduction
Spring ’09 Instructor: Min Wu
Electrical and Computer Engineering Department
University of Maryland, College Park
bb.eng.umd.edu (select ENEE631 S’09) [email protected]
ENEE631 Spring’09ENEE631 Spring’09Lecture-1 (1/26/2009)Lecture-1 (1/26/2009)
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec1 – Introduction [2]
ENEE631 Logistics – Spring 2009ENEE631 Logistics – Spring 2009
Lectures– Monday and Wednesday 11am-12:15pm, CSI 2120
Assignments and Projects– Matlab will be used for many assignments; C/C++ may also be involved
in some. – Kim Lab #2107 ~ image/video related software installed for EE408G
students are encouraged to make use of them in public lab hours.
Office Hours– Dr. Min Wu ([email protected])
Wednesday 12:30 – 2:30pm @ Kim 2142, or by appointment
Regularly check the course web pagebb.eng.umd.edu
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec1 – Introduction [3]
Scope of ENEE631Scope of ENEE631
First graduate course on image/video processing
Prerequisites: ENEE620 and 630, or by permission
– Not assume you have much exposure on image processing at undergraduate level
– Require and build on background in random process and DSP
Emphasis on fundamental concepts– Provide theoretical foundations on multi-dimensional signal
processing built upon pre-requisites
– Coupled with assignments and projects for hands-on experience and reinforcement of the concepts
– Follow-up courses image analysis, computer vision, pattern recognition multimedia communications and security
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec1 – Introduction [4]
Textbooks and ReferencesTextbooks and References
R.C. Gonzalez and R.E. Woods: Digital Image Processing, Prentice Hall, 3rd Edition, 2008. (yellow cover)
Related technical publications (will be announced in class)
Other related textbooks
– Y. Wang, J. Ostermann, Y-Q. Zhang: Digital Video Processing and Communications, Prentice Hall, 2001.
– A.K. Jain: Fundamentals of Digital Image Processing, Prentice Hall, 1989.
– John W. Woods: Multidimensional Signal, Image, and Video Processing and Coding, Academic Press, 2006.
– A.Bovik: Handbook Of Image & Video Processing, 2nd Edition, Academic Press, 2005.
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec1 – Introduction [5]
ENEE631 Course Organization ENEE631 Course Organization
Grading– Assignments and class participation 20%– Projects 45%– Exams 35%
Assignments: theoretical problems + computer components – Involves Matlab or C/C++ programming and tasks with image/video tools
to reinforce concepts– Grading is based mainly on completeness; encourage further explorations
and discussions
Projects– Put theories and principles in use and learn from doing; critical thinking
Exams– In-class mid-term exam: on basic concepts, theories, and approaches– Final exam: apply theories and principles to image/video proc tasks
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ENEE631 Course PoliciesENEE631 Course Policies
No late submission will be accepted– Start early! Plan wisely and prepare for unforeseen hurdles– Inform instructor of special circumstances with documentation
Independent work vs. discussions– Write up your solutions INDIVIDUALLY– Discussions with classmates on assignments and projects are encouraged
(unless otherwise noted)
Computer codes– You should write your own codes unless otherwise stated– DO NOT COPY other students’ codes– Clearly state the code modules obtained elsewhere and consult instructor
for permission to use in your project
Academic integrity: cheating, plagiarism, fabrication of results, …
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Image and Video Processing: Image and Video Processing:
An Introduction and Overview An Introduction and Overview
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A picture is worth 1000 words.A picture is worth 1000 words.
Rich info. from visual data
Examples of images around usnatural photographic images; artistic and engineering drawingsscientific images (satellite, medical, etc.)
“Motion pictures” => videomovie, TV program; family video; surveillance and highway/ferry camera
A video is worth 1000 sentences?A video is worth 1000 sentences?
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http://marsrovers.jpl.nasa.gov/gallery/press/opportunity/20040125a.htmlJPL Mars’ Panorama captured by the Opportunity
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Increasing Use of Images – Increasing Use of Images – A Glimpse from Encyclopedia BritannicaA Glimpse from Encyclopedia Britannica
First Edition (1768-1771)
“A dictionary of arts and sciences” by“a Society of Gentlemen in Scotland”– 3 volumes, ~ 2600 pages– illustrated with 160 copperplates
11th Edition (1911)– “last time to encapsulate ALL human
knowledge”– one picture every 4 pages
1999 Edition – 32 volumes; in CD and DVD– 73,000 articles; 30,000 photos and
illustrations Now online: http://www.britannica.com/
(From B. Liu EE488 F’06 at Princeton)
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Why Do We Process Images?Why Do We Process Images?
Enhancement and restoration– Remove artifacts and scratches from an old photo/movie– Improve contrast and correct blurred images
Composition (for magazines and movies), Display, Printing …
Transmission and storage– images from oversea via Internet, or from a remote planet
Information analysis and automated recognition– Providing “human vision” to machines
Medical imaging for diagnosis and exploration
Security, forensics and rights protection– Encryption, hashing, digital watermarking, digital fingerprinting …
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Why Digital?Why Digital?
“Exactness”
– Perfect reproduction without degradation– Perfect duplication of processing result
Convenient & powerful computer-aided processing
– Can perform sophisticated processing through computer hardware or software
– Even kindergartners can do some!
Easy storage and transmission
– 1 CD can store hundreds of family photos!– Paperless transmission of high quality photos through network
within seconds
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Examples of Digital Image & Video ProcessingExamples of Digital Image & Video Processing
Compression
Manipulation and Restoration
– Restoration of blurred and damaged images– Noise removal and reduction– Morphing
Applications
– Visual mosaicing and virtual views– Face detection– Visible and invisible watermarking– Error concealment and resilience in video transmission
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CompressionCompression
Color image of 600x800 pixels– Without compression
600*800 * 24 bits/pixel = 11.52K bits = 1.44M bytes
– After JPEG compression (popularly used on web)
only 89K bytes compression ratio ~ 16:1
Movie ~ Image Sequence– 720x480 per frame, 30 frames/sec,
24 bits/pixel– Raw video ~ 243M bits/sec– DVD ~ about 5M bits/sec– Compression ratio ~ 48:1 “Library of Congress” by M.Wu (600x800)
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DenoisingDenoising
From X.Li http://www.ee.princeton.edu/~lixin/denoising.htm
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DeblurringDeblurring
http://www.mathworks.com/access/helpdesk/help/toolbox/images/deblurr7.shtml
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Special Effects: MorphingSpecial Effects: Morphing
Princeton CS426 face morphing exampleshttp://www.cs.princeton.edu/courses/archive/fall98/cs426/assignments/morph/morph_results.html
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Visual MosaicingVisual Mosaicing– Stitch photos together without thread or scotch tape
R. Radke – Princeton thesis 5/2001
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– Image enhancement, feature extractions, and statistical modeling are often important steps in computer vision tasks
See more image understanding examples by Prof. Chellappa’s research group (http://www.cfar.umd.edu/~rama/research.html)
Face DetectionFace Detection
Face detection in ’98 @ CMU CS, http://www.cs.cmu.edu/afs/cs/Web/People/har/faces.html
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““General Illumination Correction and IGeneral Illumination Correction and Its Application to Face Normalization”, Jts Application to Face Normalization”, J. Zhu . Zhu et al, ICASSP 2003et al, ICASSP 2003
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Visible Digital WatermarksVisible Digital Watermarks
from IBM Watson web page“Vatican Digital Library”
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Invisible Watermark Invisible Watermark
– Original, marked, and their amplified luminance difference– human visual model for imperceptibility: protect smooth areas and sharp edges
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Data Hiding for Annotating Binary Line DrawingsData Hiding for Annotating Binary Line Drawings
originaloriginal marked w/ marked w/ “01/01/2000”“01/01/2000”
pixel-wise pixel-wise differencedifference
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Error ConcealmentError Concealment
25% blocks in a checkerboard pattern are corrupted
corrupted blocks are concealed via edge-directed interpolation
(a) original lenna image (c) concealed lenna image
(b) corrupted lenna image
Examples were generated using the source codes provided by W.Zeng.
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2009 International Conf. on Image Processing (ICIP)2009 International Conf. on Image Processing (ICIP)
According to the Call-for-Paper (Cairo, Egypt, Nov. 2009)
16th in the series (since 1994) http://icip2009.org/
Research frontiers ranging from traditional image processing applications to evolving multimedia and video technologies
Areas of interest include but are not limited to:– Image/Video Coding and Transmission: Still image coding, video coding,
stereoscopic and 3-D coding, distributed source coding, . . .– Image/Video Processing: filtering, restoration, enhancement, segmentation,
video segmentation and tracking, morphological processing, stereoscopic and 3-D processing, feature extraction and analysis, interpolation and super-resolution, motion detection and estimation, . . .
– Image Formation: Biomedical imaging, remote sensing, geophysical and seismic imaging, optimal imaging, synthetic-natural hybrid image systems
– Image Scanning, Display, and Printing: Scanning, sampling, quantization and halftoning, color reproduction, image representation and rendering, …
– Image/Video Storage, Retrieval, and Authentication: Image/video databases, image/video indexing and retrieval, multimodality image/video indexing and retrieval, authentication and watermarking
– Applications: biomedical sciences, geosciences and remote sensing, . . .
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So What’s a Digital Image After All?So What’s a Digital Image After All?
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What is an Image? What is an Image?
What we perceive as a grayscale image is a pattern of light intensity over a 2-D plane (aka “image plane”)
– Described by a nonnegative real-valued function I(x,y) of two continuous spatial coordinates on an image plane.
– I(x,y) is the intensity of the image at the point (x,y).– An image is usually defined on a bounded rectangle for processing
I: [0, a] [0, b] [0, inf )
Color image– Can be represented by three functions:
R(x,y) for red, G(x,y) for green, B(x,y) for blue.
x
y
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Different Ways to View an Image Different Ways to View an Image
(More generally, to view a 2-D real-valued function)
Example from B. Liu – EE488 F’06 Princeton
In 3-D (x,y, z) plot with z=I(x,y);red color for high value and blue for low
Equal value contour in (x,y) plane
Intensity visualization over 2-D (x,y) plane
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Sampling and QuantizationSampling and Quantization
Computer handles “discrete” data.
Sampling– Sample the value of the image at the nodes of a
regular grid on the image plane.
– A pixel (picture element) at (i, j) is the image intensity value at grid point indexed by the integer coordinate (i, j).
Quantization– Is a process of transforming a real valued sampled
image to one taking only a finite number of distinct values.
– Each sampled value in a 256-level grayscale image is represented by 8 bits.
=> Stay tuned for the theories on these in future weeks.
0 (black)
255 (white)
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Recall: 1-D Sampling TheoremRecall: 1-D Sampling Theorem
1-D Sampling Theorem
– A 1-D signal x(t) bandlimited within [-B,B] can be uniquely determined by its samples x(nT) if s > 2B (i.e. sample fast enough).
– Using the samples x(nT), we can reconstruct x(t) by filtering the impulse version of x(nT) by an ideal low pass filter
Sampling below Nyquist rate (2B) cause Aliasing
=> Will extend sampling theorem to 2-D later in the course
Xs() with s < 2B Aliasing
s=2/T
B
Xs() with s > 2B
Perfect Reconstructable
s=2/T
B-s
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Examples of SamplingExamples of Sampling
256x256
64x64
16x16
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Examples of QuantizaionExamples of Quantizaion
8 bits / pixel
4 bits / pixel
2 bits / pixel
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An Ancient Example of Digital ImageAn Ancient Example of Digital Image
An Old “Digital” Picture (from a small church in Crete Island, Greece)
=> Colored tiles as “pixels”
Slide from B. Liu – EE488 F’06 Princeton
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Summary of Today’s LectureSummary of Today’s Lecture
Course organization and policies
Background and examples of digital image processing
Sampling and quantization concepts for digital image
Next time– Color and Human Visual System
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Readings and AssignmentReadings and Assignment
Introductory sections in Matlab Image Processing Toolbox– http://www.mathworks.com/access/helpdesk/help/toolbox/images/images.shtml
Gonzalez-Wood book, Chapter 1
Bovik’s Handbook – Section 1 Introduction (see course web)
Go over mathematical preliminaries– Linear system and basics of 1-D signal processing– FT and ZT– Matrix and linear algebra– ProbabilityU
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec1 – Introduction [42]
Color of LightColor of Light
Perceived color depends on spectral content (wavelength composition)
– e.g., 700nm ~ red.– “spectral color”
A light with very narrow bandwidth
A light with equal energy in all visible bands appears white
“Spectrum” from http://www.physics.sfasu.edu/astro/color.html
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Perceptual Attributes of Color Perceptual Attributes of Color
Value of Brightness (perceived luminance)
Chrominance– Hue
specify color tone (redness, greenness, etc.)
depend on peak wavelength
– Saturation describe how pure the color is depend on the spread
(bandwidth) of light spectrum reflect how much white light is
added
RGB HSV Conversion ~ nonlinear
HSV circular cone is from online documentation of Matlab image processing toolbox
http://www.mathworks.com/access/helpdesk/help/toolbox/images/color10.shtml
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Questions for Today (QFT)Questions for Today (QFT)
Why “seeing yellow without yellow”?
– mix green and red light to obtain the perception of yellow, without shining a single yellow light
520nm 630nm570nm
=
“Seeing yellow” figure is from B.Liu ELE330 S’01 lecture notes @ Princeton; primary color figure is from Chapter 6 slides at Gonzalez/ Woods DIP book website