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IBMR Assignment 1 Stitching Photo Mosaics

IBMR Assignment 1

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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. - PowerPoint PPT Presentation

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Page 1: IBMR Assignment 1

IBMR Assignment 1Stitching Photo Mosaics

Page 2: IBMR Assignment 1

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

MOSAICING• Handout after Part2 Finished

Page 3: IBMR Assignment 1

PERSPECTIVE MAPPING and MOSAICING

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Requirement• Read n>2 images, and create an image mosaic

by registering, projective warping, resampling, and compositing them.

• (bonus) multiband blending, SIFT ,panorama or other methods mentioned in class.

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Just overlapping

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

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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.

Page 8: IBMR Assignment 1

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

Page 9: IBMR Assignment 1

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.

Page 10: IBMR Assignment 1

Gain Compensation• Find the optimize gains of gi according to means

of overlapping regions between image pair i and j.

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Blend the images into a mosaic• Linear blending by the weights:

where w(x) varies linearly from 1 at the centre of

the image to 0 at the edge.

• Multi-band blending (bonus):

A B𝐼 (𝑥 , 𝑦 )=

𝐼𝑎∗ (𝑊 −𝐷𝑎)+𝐼𝑏∗(𝑊 −𝐷𝑏)(𝑊 −𝐷𝑎 )+(𝑊 −𝐷𝑏)

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Multi-band blending• Band 1 scale 0 to σ

• Band 2 scale σ to 2σ

• Band 3 lower than 2σ

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

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Result

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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 Blending10%• Stitching your own photos 5%• Others

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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.