cs 4763 fundamentals of multimedia systems -introduction to image
TRANSCRIPT
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CS 4763 Fundamentals of Multimedia Systems - Introduction to Image Processing
Qi Tian
Computer Science Department University of Texas at San Antonio
[email protected]://www.cs.utsa.edu/~qitian/
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Image Processing
Manipulation of multidimensional signals− image (photo)− video− CT, MRI− Fluid flow
),( yxf),,( tyxf),,,( tzyxf),,,( tzyxv
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A Typical Image Processing System
object observe digitize store process Refresh/store
Record
output
DisplayImaging systems
Sample and quantize
Digital storage (disk)
Digital computer
On-line buffer
X-ary, radar imaging, infrared imaging, ultrasound imaging, medical imaging, geophysical imaging
A/D
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Fundamentals of Image Processing
Representation– acquisition, digitization, and display to mathematical
characterization of images for subsequent processing– a prerequisite for an efficient processing techniques such as
enhancement, filtering, and restoration.
Processing Techniques– Image compression, image restoration, and image reconstruction– Statistical image processing techniques
Communications
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Multimedia Processing Techniques
– Coding/compressionStorage and communications
JPEG, JPEG2000MPEG-1 (CD, mp3), MPEG-2 (HDTV, DVD) H.261, H.263
– Enhancement, restoration, reconstructionfeature extraction for image analysis and visual information displayremoval of degradation in an image, LS, ML, Max entropy, MAP2D -> 3D image MRI, CT, Radon transform
– Analysis, detection, recognition, understandingquantitative measurements from an image to produce a description on it
– Visualization
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Advanced Processing Techniques
Statistical processing techniques– Hidden Markov model (HMM)
– Probabilistic graphical models
– Bayesian networks
– Markov random field
Many applications to speech recognition, pattern classification, data
compression, and channel coding, etc.
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History of Image/Video Coding
1950
1960
1970
1980
1990
2000+
Math PR, CV, CG
Fractal 3-D Model based coding
Signal ProcessingBased
PCM DPCM
Transform CodingVQ
Subband Coding Wavelets
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Reference:– F. Nebeker, Signal Processing: The Emergency of a Discipline,
1948-1998– IEEE History Center, 1998
Broadband TV (NTSC)500 × 500 × 8 × 3 × 30 bits/sec≈100 Mb/sec (compression is necessary!)Modem: 56Kb/sec
Picture Element– Pixel West coast people in USC– Pel East people in MIT
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Image/Video Compression
Signal-Processing Based:Encoder
),( yxfH
),( yxgSignal Proc.
Representation ),( yxg
Decoder1H− ),(ˆ yxf),( yxg
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Image/Video Compression
3D Model-Based:Encoder
Representation P
Decoder
),( yxfH
Analysis Model Parameter P
Model
),(ˆ yxfP 3D
Model
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Image/Video Compression
Fractal-Based:Encoder
Representation S
Decoder
),( yxf System S ),( yxf
Find S for which is an Attractor.),( yxf
SAny signal
),(ˆ yxf
Iteration
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Image/Video Compression Standard
Facsimile: Fax Group 1, 2, 3, 4JBIG (Joint Bi-level Image Expert Group)
Images: JPEG (http://www.jpeg.org/)
JPEG2000
Video: H.261, H.263 P × 64 Kb/s (P =1 ~ 30)MPEG 1 1.2 Mb/s Video, CD, MP3MPEG 2 1.2 – 20 Mb/s, sports, HDTV, DVDMPEG 4 1 kb/s → 1Mb/s, very low speed video
coding, MultimediaMPEG 7 Multimedia description, audio/video
MPEG 21 Multimedia framework
Based on Wavelet Transform
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A de facto image for the past three decade for its rich texture
Lena
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What are Challenging Problems in Multimedia Processing?
Multimedia Processing is taken in a broad sense, including:Image/Video compression, enhancement, restoration, reconstruction, analysis, recognition, understanding, visualization, and synthesis/animation.
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Examples
Face modeling, detection, and recognitionEmotion recognitionGesture recognitionGender/age/ethnicity recognitionAudio-visual speech recognitionImage/video superresolutionImage/video browsing, indexing, and retrievalBiometrics
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Face Related Research
Face modelingFace detectionFace recognitionFacial expression recognition
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Generic Face Model
Texture mapping
Face model morphing
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Generic Face Model
The generic face model is generated from a MRI data set
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Customize A Genetic Face on An Individual
Polygon Mesh: 2240 Vertices + 3946 Triangles.Polygon Mesh: 2240 Vertices + 3946 Triangles.NonNon--Uniform Rational BUniform Rational B--Splines (NURBS): 63 control points.Splines (NURBS): 63 control points.
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The iFACE system in a distributed collaborative environment. (a) Avatar in the head mounted display, (b) avatar in the desk screen of
MIC3E, (c) avatar in the main screen of MIC3E
Avatar – talking head
University of Illinois at Urbana-Champaign
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Text-Driven Face Animation
“We strive to make the meter on animation production, and are always looking for new technology that will enable faster, more appealing character creation,”
said Joel Kransove, Digital Director of Nickelodeon. (Source: Digital Producer)
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Speech-Driven Face Animation
“Game characters have become synthetic actors and dialogue is an essential element of the effect we create. The quality of the lip-synching can make or break the sense of reality,”
said Scott Cronce, vice president and CTO at Electronic Art (Source: Gamepro)
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Video-Driven Face Animation
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Emotion Recognition
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Emotion Recognition
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Emotion Recognition
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Face Detection Techniques
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Face Detection Techniques
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Face Recognition: Why it is easy?
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Face Recognition: Why it is hard?
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Beauty Check
What Are the Causes and Consequences of Human Facial Attractiveness?
Babyfaceness
Symmetry
Social perception
Universities of Regensburg, Germany
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Which is more attractive?
Universities of Regensburg, Germany
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BabyfacenessLarge head
Large curved forehead
Facial elements (eyes,
nose, mouth) located
relatively low
Large, round eyes
Small, short nose
Round cheeks
Small chinKate Moss4-year old girl
Include mature female features: high, prominent cheek bones and concave cheeks
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Which one is cuter?
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Miss Germany (2002)
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A selection of the 22 contestants of the final round of the contest
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Real vs. Virtual Miss Germany
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Image Analysis
Texture synthesis and transferImage Super-resolutionImage RepairsIllumination/Lighting changes and transfer
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Texture Synthesis and Transfer
+
SIGGRAPH’01 Effros & Freeman, MIT, 2001
synthesis
transfer
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Texture Synthesis and Transfer
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Image Superresolution
True Sub-sampled
Intelligent guess about details of texture
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Image Superresolution
Gaussian filter Bicubic interpolation
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Image Superresolution
Median filter Wiener filter
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Image Superresolution
Dynamic resolution enhancement Amos Storkey
True
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Image Repairs
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Image Repairs
Original Image
Result
Segmentation
Image synthesis based on Tensor Voting
Curve connection
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Image Repairs
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Illumination Effects on Images
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Relighting – Basic Algorithm
Step 2: Approximate radiance environment map
Step 3: Synthesize novel appearance by adjusting the 9 spherical harmonic coefficients
Step 1: Align image with generic 3D face model
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Lighting Transfer
input target results
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Image/Video Retrieval
Image database
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CBIR based on color, texture, shape/structure
MARS: Multimedia Analysis and Retrieval System
metadata
User Interface
Similarity ranking
memory
Feature weighting
Visual C++
Feature Extraction
C/C++ Color
Texture
structure
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State-of-the-artCBIR Systems
QBIC (IBM), PhotoBook (Media Lab), Netra (UCSB), VisualSeek (Columbia), PicHunter (NEC-NJ), Amore (NEC-CA), EI Niňo (Praja), MARS (UIUC), Virage (Virage Inc.), CORE, PictoSeek, Piction, InfoScope …
Research CommunitiesComputer Vision, Image/Video Processing, Library and Information Science, Database and Management Systems
Leading Journals & StandardPAMI, ACM Multimedia, IJCV, CVIU
MPEG-7
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MARS using global features
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Biometrics
Security Threats:We now live in a global society of increasing desperate and dangerous people whom we can no longer trust based on identification documentswhich may have been compromised.
A challenging Pattern Recognition ProblemEnabling technology to make our society safer, reduce fraud and offer user convenience.
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Too many passwords to remember
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Identification Problems
Identity Theft: Identity thieves steal PIN (e.g., date of birth) to open credit card account, withdraw money from accounts and take out loans
3.3 million identity thefts in U.S. in 2002; 6.7 million victims of credit card fraud
Surrogate representations of identity such as password and ID cards no longer suffice
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Biometrics
Automatic recognition of people on their distinctive anatomical (e.g., face, fingerprint, iris, retina, hand geometry) and behavioral (e.g., signature, gait) characteristics.
Person identification is now an integral part of the infrastructure needed for diverse business sectors such as banking, border control, law enforcement…
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Biometric Applications
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Biometric Applications
There are ~500 million border crossing/year (each way) in the US
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Want to charge it?
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Biometric Characteristics
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Biometric Market Growth
International Biometric Group
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“State-of-the-art” Error Rate
False accept rate (FAR):Proportion of imposters accepted
False reject rate (FRR):Proportions of genuine users rejected
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Multibiometrics
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Soft Biometrics
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Privacy Concerns
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Tracking
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