panorama creation by image stitching
DESCRIPTION
Panorama Creation by Image stitching. Ms. Sophea CHAN December 20, 2010. Theory Compositing Surface Image stitching process Resulting References. Over View Theory Idea Compositing Surface Reference Image Image stitching process Image Registration Overlapped Area Blending Resulting - PowerPoint PPT PresentationTRANSCRIPT
Panorama Creation by Image stitching
Ms. Sophea CHAN
December 20, 2010
Sophea Chan Page 2 / 17
Theory Compositing Surface
Image stitching processResulting
References
Over ViewTheory
Idea
Compositing SurfaceReference Image
Image stitching processImage RegistrationOverlapped AreaBlending
ResultingReferences
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TheoryCompositing Surface
Image stitching processResulting
References
Idea:Choose 2 images I1(n1xm1), I2(n2xm2) with overlapping fields of view.
The main idea is to create a panorama images out of the input images.
The approaches to create image stitching (panorama) require nearly exact overlaps between images and identical exposures to produce seamless results.
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Idea
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TheoryCompositing Surface
Image stitching processResulting
References
Over ViewTheory
Idea
Compositing surface Reference Image
Image stitching processImage RegistrationOverlapping AreaBlending
ResultingReferences
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TheoryCompositing Surface
Image stitching processResulting
References
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Image to be warped Reference Image
Reference ImageWe select one of the images as a reference . It is the one that is geometrically most center. Other image is warped into the reference coordinate system.
Reference Image
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TheoryCompositing Surface
Image stitching processResulting
References
Over ViewTheory
Idea
Training Compositing surface
Image stitching processImage RegistrationOverlapping AreaBlending
ResultingReferences
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Image RegistrationOverlapping AreaBlending Images
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TheoryCompositing Surface
Image stitching processResulting
References
Image registrationThe purpose is to first extract distinctive features from each images, to match these features to a global correspondence, and to estimate the geometric transformation between the images.
1- Extract feature2- Feature matching3- Align images (Compute Transformation Matrix)
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Image RegistrationOverlapping AreaBlending Images
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TheoryCompositing Surface
Image stitching processResulting
References
- Extract featuresFinding interest points of both images
By using Harris Detector (Mathematics ).
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Image RegistrationOverlapped AreaBlending Images
𝐄(𝐮,𝐯) = 𝑾ሺ𝒙,𝒚ሻ[𝑰ሺ𝒙+ 𝒖,𝒚+ 𝒗ሻ− 𝑰(𝒙,𝒚)]𝟐𝒙,𝒚
Window function Shifted intensity Intensity
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TheoryCompositing Surface
Image stitching processResulting
References
Features matchingUsing SIFT to extract the frames (interest Points) and the descriptors
from the image I. (SIFT function is provided in the solution of exercise 4).
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Image RegistrationOverlapped AreaBlending Images
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TheoryCompositing Surface
Image stitching processResulting
References
- Overlapping area:
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Overlapping area between image1 and image 2
Image RegistrationOverlapped AreaBlending Images
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TheoryCompositing Surface
Image stitching processResulting
References
-Geometric Transformation (Homography)RANSAC is used to remove outliers.Compute Transformation Matrix T by projective transformation ( or homography).
Shift Img = Original Image * T
RANSAC function is Provided in the solution of exe. 4
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Image RegistrationOverlapping AreaBlending Images
Removed outlier by RANSACShift Image
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TheoryCompositing Surface
Image stitching processResulting
References
Technique 1: Featuring
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Shift image + Reference Image = Mapped Image
Image RegistrationOverlapped AreaBlending Images
+
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TheoryCompositing Surface
Image stitching processResulting
References
Technique 1: FeaturingThe median filter is an effective method that can suppress isolated noise without blurring sharp edges.y[m,n]=median{ x[i,j], (i,j) w } , w is represented a neighborhood centered around location in the image.
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Median filtering n=m=5
Image RegistrationOverlapped AreaBlending Images
Cutting Plan
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TheoryCompositing Surface
Image stitching processResulting
References
Technique 2: Central weightingCompute the average value of each pixel.
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Image RegistrationOverlapped AreaBlending Images
+
Cሺxሻ= 𝑤𝑘 𝐼ሚ𝑘ሺ𝑥ሻ 𝑘 / 𝑤𝑘 (𝑥)𝑘
wk(x) = |หargminy ൛หa&ya&ห Ikෙ� ሺx+ yሻis invalidൟห|
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TheoryCompositing Surface
Image stitching processResulting
References
Technique 2: Central weightingThe median filter is an effective method that can suppress isolated noise without blurring sharp edges.y[m,n]=median{ x[i,j], (i,j) w } , w is represented a neighborhood centered around location in the image.
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Median filtering n=m=5
Image RegistrationOverlapped AreaBlending Images
Average
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Theory Compositing Surface
Image stitching processResulting
References
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Out put
Output 1:
Featuring
Average
image1 image2
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Theory Compositing Surface
Image stitching processResulting
References
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Out put
Output:
Featuring
image2
Average
image1
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Theory Compositing Surface
Image stitching processResulting
References
ResourcesB. Ommer.
Representation Feature.Object Recognition Lecture (Chapter 2), 2010.
Richard Szeliski.Image Alignement and StitchingTechnical Report MSR-TR-2004-92Last Updated, December 10, 2006.
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Resources
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Theory Compositing Surface
Image stitching processResulting
References
Questions ?
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Questions
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Theory Compositing Surface
Image stitching processResulting
References
Thanks !
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End