Digital image stabilization - Computer Graphics Laboratory

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Harris Detector ✦ Average intensity change in direction [u,v] can be expressed as a bilinear Taylor form: ✦ Describe a point in terms of eigenvalues of M: measure of corner response ✦ A good (corner) point should have a large intensity change in all directions, i.e. R should be large positive ✦ Variation: Shi-Tomasi: Pretty much same as Harris, but use min(λ1,λ2) instead of R Tuesday, February 23, 2010
Recap ✦ Brute force : dense, model based ✦ Optical flow: dense, non-parametric • nearby pixels, same color • uses all pixels • can be unstable ✦ Feature tracking: sparse, non-parametric Tuesday, February 23, 2010 • find corners • then apply optical flow to them • problem: what if the motion is too big?
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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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Page 43 and 44: Wire removal Tuesday, February 23,
Page 53 and 54: Image alignment goals ✦ Multiple-
Page 55 and 56: Approaches: model or not ✦ Model
Page 57 and 58: Brute force: dense & model-based
Page 59 and 60: Optical flow: dense, non-parametr.
Page 61 and 62: 1D brightness constancy ✦ Goal: E
Page 63 and 64: Same in 2D ✦ If It is the time de
Page 65 and 66: Aperture problem Tuesday, February
Page 73 and 74: Small motion problem ✦ The first
Page 75 and 76: Coarse-to-fine optical flow image H
Page 77 and 78: Coarse-to-fine optical flow image H
Page 79 and 80: Coarse-to-fine optical flow image J
Page 81 and 82: Coarse-to-fine optical flow image J
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: High textured region Tuesday, Febru
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 and 128: RE- PHOTOGRAPHY Tuesday, February 2
Page 129 and 130: s found in Step 2 and estimates cam
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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