1. Computer Vision A Hand-Held “Scanner” for Large-Format Images COMP 256 Adrian Ilie Steps Towards

2. Computer Vision Previous Work: Panoramas Feature extracting: use SIFT, since they are scale-invariant and partially invariant to affine illumination changes - done Feature matching: approximate nearest neighbor - done Image matching: probabilistic model using RANSAC inliers/outliers - N/A Bundle adjustment: add images one by one and iterate using Levenberg- Marquardt - N/A Blending: multi-band - not done

3. Computer Vision Extracting the Features Use SIFT features –Location: peaks in DoG pyramids –Descriptors: gradient orientation histograms

4. Computer Vision Matching Features Look for closest 2 descriptors in a k-d tree (logarithmic speed) If distance(descriptor, 1 st closest) < 0.36*distance(descriptor, 2 nd closest), descriptor is a good match

5. Computer Vision Computing the Homography MLESAC

6. Computer Vision Warping the Images Use bilinear interpolation

7. Computer Vision Algorithm Take an “overview” image Extract its features and build a k-d tree Take N “detail” images For each image i  Extract the features  Match the features against the ones in the k-d tree  Use MLESAC to compute the homography  Warp the image  Blend the image into the current estimate  Update the k-d tree

8. Computer Vision Blending the Warped Images Detail image has higher resolution! Resample the current estimate so that the area corresponding to the warped image is equal to the area of the unwarped image Can blend using some weights, or just use the detail image pixel (since it is of higher quality)

9. Computer Vision Results

10. Computer Vision Scanning My Favorite Poster ;)

11. Computer Vision Scanning My Favorite Poster ;)

12. Computer Vision Scanning My Favorite Poster ;)

13. Computer Vision Scanning My Favorite Poster ;)

14. Computer Vision Issues and Future Work Issues –Radial distortion in the “overview” image –Numerical instability of the homography computation –Illumination changes across images Future work –Super-resolution would be nice to have –It would be nice to have a nice viewer that would take images and homographies as input, then blend and render them at the appropriate level of detail, depending on the zoom level