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Panorama Creation by Image stitching

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

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Panorama Creation by Image stitching

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  1. Panorama Creation by Image stitching Ms. Sophea CHAN December 20, 2010

  2. TheoryCompositingSurface Image stitching processResultingReferences Over View Theory Idea Compositing Surface Reference Image Image stitching process Image Registration Overlapped Area Blending Resulting References Sophea Chan

  3. TheoryCompositing SurfaceImage stitching processResultingReferences Idea 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. Sophea Chan

  4. TheoryCompositing SurfaceImage stitching processResultingReferences Over View Theory Idea Compositing surface Reference Image Image stitching process Image Registration Overlapping Area Blending Resulting References Sophea Chan

  5. TheoryCompositing SurfaceImage stitching processResultingReferences Reference Image Reference Image We 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. Image to be warped Reference Image Sophea Chan

  6. TheoryCompositing SurfaceImage stitching processResultingReferences Image Registration Overlapping Area Blending Images Over View Theory Idea Training Compositing surface Image stitching process Image Registration Overlapping Area Blending Resulting References Sophea Chan

  7. TheoryCompositing SurfaceImage stitching processResultingReferences Image Registration Overlapping Area Blending Images Image registration The 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 feature 2- Feature matching 3- Align images (Compute Transformation Matrix) Sophea Chan

  8. TheoryCompositing SurfaceImage stitching processResultingReferences Image Registration Overlapped Area Blending Images Extract features Finding interest points of both images By using Harris Detector (Mathematics ). Intensity Window function Shifted intensity Sophea Chan

  9. TheoryCompositing SurfaceImage stitching processResultingReferences Image Registration Overlapped Area Blending Images Features matching Using SIFT to extract the frames (interest Points) and the descriptors from the image I. (SIFT function is provided in the solution of exercise 4). Sophea Chan

  10. TheoryCompositing SurfaceImage stitching processResultingReferences Image Registration Overlapped Area Blending Images - Overlapping area: Overlapping area between image1 and image 2 Sophea Chan

  11. TheoryCompositing SurfaceImage stitching processResultingReferences Image Registration Overlapping Area Blending Images -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 Removed outlier by RANSAC Shift Image Sophea Chan

  12. TheoryCompositing SurfaceImage stitching processResultingReferences Image Registration Overlapped Area Blending Images Technique 1: Featuring + Shift image + Reference Image = Mapped Image Sophea Chan

  13. TheoryCompositing SurfaceImage stitching processResultingReferences Image Registration Overlapped Area Blending Images Technique 1: Featuring The 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. Cutting Plan Median filtering n=m=5 Sophea Chan

  14. TheoryCompositing SurfaceImage stitching processResultingReferences Image Registration Overlapped Area Blending Images Technique 2: Central weighting Compute the average value of each pixel. + Sophea Chan

  15. TheoryCompositing SurfaceImage stitching processResultingReferences Image Registration Overlapped Area Blending Images Technique 2: Central weighting The 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. Average Median filtering n=m=5 Sophea Chan

  16. Theory Compositing Surface Image stitching processResultingReferences Out put Output 1: Featuring image1 image2 Average Sophea Chan

  17. Theory Compositing Surface Image stitching processResultingReferences Out put Output: image2 image1 Featuring Average Sophea Chan

  18. Theory Compositing Surface Image stitching processResultingReferences Resources Resources B. Ommer. Representation Feature. Object Recognition Lecture (Chapter 2), 2010. Richard Szeliski. Image Alignement and Stitching Technical Report MSR-TR-2004-92 Last Updated, December 10, 2006. Sophea Chan

  19. Theory Compositing Surface Image stitching processResultingReferences Questions Questions ? Sophea Chan

  20. Theory Compositing Surface Image stitching processResultingReferences End Thanks ! Sophea Chan

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