computer vision a hand-held “scanner” for large-format images comp 256 adrian ilie steps towards
Post on 21-Dec-2015
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ComputerVision 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
ComputerVision Extracting the Features
• Use SIFT features– Location: peaks in DoG pyramids– Descriptors: gradient orientation
histograms
ComputerVision Matching Features
• Look for closest 2 descriptors in a k-d tree (logarithmic speed)
• If distance(descriptor, 1st closest) < 0.36*distance(descriptor, 2nd closest), descriptor is a good match
ComputerVision 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
ComputerVision 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)
ComputerVision 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
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