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Stitching Photo Mosaics
Stitching photos to construct a wild-view scene. Part1: CORNER DETECTION Part2: PERSPECTIVE MAPPING and MOSAICING Handout after Part2 Finished
PERSPECTIVE MAPPING and MOSAICING
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.
Shoot the Pictures Recover Homographies Warp the Images/ Image Rectification Gain Compensation Blend the images into a mosaic
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.
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
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.
Find the optimize gains of gi according to means of overlapping regions between image pair i and j.
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): AB
Band 1 scale 0 to σ Band 2 scale σ to 2 σ Band 3 lower than 2 σ
Your own project1a code. A C called matlab library. to calculate inverse matrix, SVD or etc.
Basic: 75% Harris Corner Detection + KNN (Hw1a) RANSAC Projection Mapping / Affine Mapping Image Warping Bonus: Non-Maximum Suppression5% KD Tree5% SIFT15% Gain Compensation10% Linear Blending5% Multi Blending10% Stitching your own photos5% Others
11/22 11:59:59pm Upload your program & report to: host : caig.cs.nctu.edu.tw port : username : IBMR10 password : IBMR10 and create your own folder with your ID.
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