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Transcript image-mosaic
Image Mosaic Techniques
for the Restoration of Virtual Heritage
2003. 8. 28
Yong-Moo Kwon, Ig-Jae Kim,
Tae-Sung Lee, Se-Un Ryu, Jae-Kyung Seol
KIST
KOREA
Contents
Revisiting Image Mosaic Technique
Our Researches for Image Mosaic
IR Reflectography Image Mosaic
X-Ray Image Mosaic
Summary
Revisiting Image Mosaic Technique
Image Mosaicing
Panorama Image
Image Based Rendering (IBR)
Basic Algorithm
Registration using Features
Image Warping based on Homography Matrix
Blending Images
Target Dimension in view of Image Mosaicing
2D Target
Planar Paintings Image
Homography Technique
Feature-Based Image Mosaicing
3D Target
3D Real World Image
Limitation using Homography
Due to Depth Difference b/w Features in Target
Our Research for Image Mosaic
2D Target
IR Reflectography
Special 3D Target
X-Ray Imaging
Mural Underdrawings Mosaic
Old Sword X-Ray Image Mosaic
Research Topics
How to extract and use Features
Imaging Media (IR, X-Ray)
Feature’s characteristics are different from the previous ones
IR Image Mosaic
IR Reflectography System
IR Source
IR Filter
IR Camera
Murals
IR Reflectography Principle
Visible Light
Reflection
UnderDrawing
IR
Color Painting,
Reflection
Dust
Absorbed
Back Frame
IR Reflectography Camera
IR Camera : Super eye C2847 (~1.9㎛)
Hamamatsu
IR Source : ~1.9㎛
IR Filter : CVI Laser Corp.
NIR bandwidth filter
Bandpass Filter
800nm ~ 2000nm Pass
every 100 nm bandpass filter (800nm, 900, …, 2000nm)
IR Characteristics to Mural according to WL
IR Camera
HAMAMATSU Super eye
C2847
WL Range : 0.4㎛ ~ 1.9㎛
IR Source
HAMAMATSU C1385-02
Filter
HAMAMATSU IR-D80A : 0.8㎛ ~ 1.9㎛
CVI Laser corporation
Near IR Interference BP filter
800nm, 900nm, … 2000nm
Sony PC-115
Digital Image Capture
Night Shot
IR Image Mosaic for Mural Underdrawing
Basic Method
Automatic Feature Extraction
Registration Using Features
Image Warping
Image Blending
Main Considerations
IR Wavelength Characteristics
Penetration Ratio into Paintings
Color (Red, Green, Blue etc)
Color Painting Depth
Our Approach
▶ Automatic Feature Extraction & Registration
- Cross Points in IR Underdrawing Image
- Grid Pattern for Blank Space
▶ Adaptive Overlapping Area For Image Blending
- Trade-Off between Registration and Blending
* Large Overlapping Area: Good for Registration
* Small Overlapping Area: Good for Blending
▶ Use feature of IR Spectrum
- Use Different IR Wavelength according to paining color
Automatic Feature Extraction
Feature of Korea Murals
- Many Blank Space
- Not so much good features
1> Visible Light Pattern
2> Twice Captures
- w/o IR Filter
- w/i IR Filter
IR Image Mosaic
-
Homography Estimation using Grid Image & IR Image
- Apply Homography to IR Image
X-RAY Image Mosaic
Why we use X-ray Technique ?
Old Sword
Old Sword is inside Sword Cover
Weak for Touch & Manipulation
Can’t Open Sword Cover
Use X-Ray Technique for the restoration of
Old Sword inside Sword Cover
Sword for experiment
Schema of a x-ray imaging using a linear X-Ray Camera
1.
X-Ray Image
2.
X-Ray Tube
3.
X-Rays
4.
X-Ray Detector
5.
PC
6.
Object
Why X-Ray Image Mosaic ?
For High Resolution Imaging
Multiple X-Ray Imaging
Setting Object
X-Ray Image Capture
Move Object Upward or Downward Step-By-Step
Stitching X-Ray Images into High Resolution Image
X-Ray Imaging Principle
Basic Principle
X-Ray Particle Penetrates through Target
One Point Depth -> Grey Value Pixel
Dependency
Target Depth
Target Material
X-Ray Image Characteristics: 2D or 3D ?
Target Dimension in view of Image Mosaic
Well Controlled Penetration Angle
Image Pixel Depends on Penetration Angle
Usually Same Penetration Angle for Each Capture
Just Planar 2D Image Using CCD Camera
Orthogonal axis Movement according to X-Ray Beam
Object -> X-Ray Camera -> CCD Camera
2D Target: Homography Technique
X-RAY Image Equipment
X-TEK X-Ray System
X-Ray Source & Object (Sword)
X-RAY Image Capture
For High-Resolution Restoration
Multiple X-Ray Imaging
Image Stitching Technique
Feature-based Registration
Problem ?
Difficult to use features in X-ray Image
Using Feature Pattern
Feature Extraction
Feature Extraction From Known Pattern
Circle Type & Rectangular Type
Circle
Type -> Pattern Matching
Rectangular -> Feature Points
Feature
Ex traction
Binary pattern
for feature ID
Feature Extraction
Method
Circle Type Pattern -> Apply Image Labeling
Rectangular Type Pattern -> Corner Detection
D x2
C
D x D y
D x D y
2
- For every pixel of image, computes
first derivatives Dx and Dy.
- The eigenvalues are found by solving
det(C- λI )= 0
DxDy
2
D
y
2
( D x D y ) 4( D x
2
2
2
2
2
If λ1, λ2 > t, where t is some threshold, then a
corner is found at that location
D y ( D x D y ) )
2
2
Feature Point Matching
Semi-Auto(Present)
Automatic Matching
(On-going)
Automatic Feature
Extraction of Rectangle
Type pattern
Manual Matching
Classify the features using
pattern ID from Circle Type
Pattern
Homography Matrix
Apply LS-Method(Least
Square Method) using
Matched feature Points
Semi-auto Demo
Implemented S/W
X-ray
Image File
Handling
Feature
Extraction
&
Select
Points
Homograph
y Matrix
Estimation
& Stitching
Generated HighResolution X-ray
Image
More Experimentation
Summary
Application of Image Mosaicing Techniques
Infrared Image
X-Ray Image
Our Approach
Feature Pattern
Automatic Feature Extraction & Registration
Homography Technique
Imaging Media (IR, X-Ray) & Feature’s Characteristics
Thank You !
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