Digital image stabilization - Computer Graphics Laboratory

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Computational Re-Photography ✦ Bae, Agarwala & Durand, to appear ✦ Goal: given reference (old) photo, take new photo at same viewpoint Tuesday, February 23, 2010 4 • Soonmin Bae et al. feature points Reference The first frame structure The second frame feature points feature points Estimate focal length and pose Estimate structure and pose reference focal length + scene structure + [ R10 | T10 ] Fig. 4. Our procedure to calibrate and register the reference camera.
s found in Step 2 and estimates camera iteration. f the translation. ight estimation result passes sanity testing. e next step.Otherwise repeat from Step 7. on to move. until Thread A nishes Step 5 and updates Guidance visualization ✦ Camera tethered to laptop ow chart of our interleaved scheme. ✦ Arrows tell user where to go . For each resulting pose, we perform three e our visualization is reliable. We compare ructed from each frame with our initial 3D first two <strong>image</strong>s. We measure the 3D error the pose estimation if the median of the 3D . Typically, the median error is less than ✦ Overlay edges for finer grain Computational Re-Photography • 3 k if the current camera pose result is cones. We found that a simple filter works, alr [Kalman 1960] would likely generate a measure the mean and the standard deviaions at the previous ten frames and confirm d camera location is within 4 standard de- . We assume the camera motion is smooth small. The above two tests typically detect once in 100 frames. structure degeneracy caused when all the ingle plane in the scene. We find the bestg RANSAC with 1.5 pixel average mapterations. If the number of homography inof Tuesday, the February epipolar 23, 2010 geometry inliers, we ignore . 3. In our prototype implementation, a laptop is connected to a camera. laptop computes the relative camera pose and visualizes how to transthe camera with two 2D arrows. Our alignment visualization, which sists of edges detected from the reference <strong>image</strong> composited onto the translation to the user. We automatically compute the best camera rotation between the current and reference views, and apply this rotation as a warp before displaying the current frame. This rotation alignment allows the user to focus on translating the camera in the right direction without striving to hold the camera in the right orientation. The effect of a 3D camera rotation and zoom can be described with an infinite homography [Hartley and Zisserman 2000]. The infinite homography is a subclass of the general homography, as it is restricted to rigid camera rotations and zooms. We use the algorithm of Brown et al. [2007] to compute the infinite homography that fits all the epipolar geometry inliers with the least squared error. 5. VISUALIZATION Fig. 9. A screen capture of our visualization, including our primary visual
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Tuesday, February 23, 2010 Image al
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DENOISING Tuesday, February 23, 201
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Single frame Tuesday, February 23,
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EXTENDED DEPTH of FIELD Tuesday, Fe
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Focal stack Tuesday, February 23, 2
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Macro montage • 55 images here Tu
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Focal stack & plenoptic cameraStanf
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References • http://www.janrik.ne
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Interactive Digital Photomontage
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Family portrait challenge Tuesday,
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Family portrait challenge Tuesday,
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Digital photomontage Tuesday, Febru
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Tourist removal Tuesday, February 2
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Wire removal Tuesday, February 23,
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Image alignment goals ✦ Multiple-
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Approaches: model or not ✦ Model
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Brute force: dense & model-based
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Optical flow: dense, non-parametr.
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1D brightness constancy ✦ Goal: E
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Same in 2D ✦ If It is the time de
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Aperture problem Tuesday, February
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Small motion problem ✦ The first
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Coarse-to-fine optical flow image H
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Page 83 and 84: Image alignment: translation Tuesda
Page 85 and 86: Other application: retiming ✦ Gen
Page 87 and 88: Good features to track ✦ Idea: so
Page 89 and 90: Condition for solvability Tuesday,
Page 91 and 92: Edge Tuesday, February 23, 2010 - l
Page 93 and 94: High textured region Tuesday, Febru
Page 95 and 96: Recap ✦ Brute force : dense, mode
Page 97 and 98: MODEL FITTING Tuesday, February 23,
Page 99 and 100: Fitting a model ✦ e.g. affine: x
Page 101 and 102: VIEWFINDER ALIGNMENT Tuesday, Febru
Page 103 and 104: Idea: 1D matches of gradient ✦ Co
Page 105 and 106: Application: denoising Pulli / View
Page 107 and 108: Video stabilization : 3 steps ✦ E
Page 109 and 110: Content-Preserving Warps ✦ Liu et
Page 111 and 112: Solving for warp ✦ Grid over imag
Page 113 and 114: Results Tuesday, February 23, 2010
Page 115 and 116: Video Tuesday, February 23, 2010
Page 117 and 118: Video matching ✦ Sand and Teller
Page 119 and 120: Match move ✦ For compositing with
Page 121 and 122: Live action ✦ Note orange balls t
Page 123 and 124: Miniature construction Tuesday, Feb
Page 125 and 126: 5 degrees of freedom camera robot T
Page 127: RE- PHOTOGRAPHY Tuesday, February 2
Page 131 and 132: Bae et al. Tuesday, February 23, 20
Page 133 and 134: Video stabilization ✦ http://www.
Page 135 and 136: Tracking ✦ http://citeseerx.ist.p
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Digital image stabilization - Computer Graphics Laboratory

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