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IBMR Assignment 1

IBMR Assignment 1. Stitching Photo Mosaics. What is Photo Mosaic?. Stitching photos to construct a wild-view scene. Part1: CORNER DETECTION Part2: PERSPECTIVE MAPPING and MOSAICING Handout after Part2 Finished. PERSPECTIVE MAPPING and MOSAICING. Requirement.

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IBMR Assignment 1

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  1. IBMR Assignment 1 Stitching Photo Mosaics

  2. What is Photo Mosaic? • Stitching photos to construct a wild-view scene. • Part1: CORNER DETECTION • Part2: PERSPECTIVE MAPPING and MOSAICING • Handout after Part2 Finished

  3. PERSPECTIVE MAPPING and MOSAICING

  4. Requirement • Read n>2 images, and create an image mosaic by registering, projective warping, resampling, and compositingthem. • (bonus) multiband blending, SIFT ,panorama or other methods mentioned in class.

  5. Just overlapping

  6. Steps • Shoot the Pictures • Recover Homographies • Warp the Images/ Image Rectification • Gain Compensation • Blend the images into a mosaic

  7. Shoot the Pictures • You may use the photos on the webpage, but shoot your own photos and mosaic them will get bonus credit. • Shoot photos as: • Overlap the fields of view significantly. 40% to 70% overlap is recommended.

  8. Recover Homographies • Construct a linear system as: p’=Hp, where p’ and p are correspondence points. • Follow the Lecture 8 page 6~9. You may try Affine mappings(DOF=6) or Projective mappings(DOF=8). • Solve Ax=0

  9. Warp the Images/Image Rectification • Source scanning(forward mapping) or destination scanning(inverse mapping). • You will need to avoid aliasing when resampling the image. • Be careful of the size of the resulting image.

  10. Gain Compensation • Find the optimize gains of giaccording to means of overlapping regions between image pair i and j.

  11. Blend the images into a mosaic • Linear blending by the weights: where w(x) varies linearly from 1 at the centreof the image to 0 at the edge. • Multi-band blending (bonus): A B

  12. Multi-band blending • Band 1 scale 0 to σ • Band 2 scale σ to 2σ • Band 3 lower than 2σ

  13. Support • Your own project1a code. • A C called matlab library. • to calculate inverse matrix , SVD or etc.

  14. Result

  15. Grading • Basic: 75% • Harris Corner Detection + KNN (Hw1a) • RANSAC • Projection Mapping / Affine Mapping • Image Warping • Bonus: • Non-Maximum Suppression 5% • KD Tree 5% • SIFT 15% • Gain Compensation 10% • Linear Blending 5% • Multi Blending 10% • Stitching your own photos 5% • Others

  16. Deadline • 11/22 11:59:59pm • Upload your program & report to: • host : caig.cs.nctu.edu.tw • port : 30021 • username : IBMR10 • password : IBMR10 • and create your own folder with your ID.

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