Lecture 12 - Stanford Vision Lab

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Orientation Normalization: Computation[Lowe, SIFT, 1999]• Compute orientation histogram• Select dominant orientation• Normalize: rotate to fixed orientation0 2Slide adapted from David LoweFei-Fei Li<strong>Lecture</strong> <strong>12</strong> -55‐Nov‐11
Page 1 and 2: Lecture 12:Local Descriptors, SIFT&
Page 3: Local Descriptors• We know how to
Page 7 and 8: • Problem:Affine Adaptation- Dete
Page 9 and 10: Affine Normalization/Deskewingrotat
Page 11 and 12: Affine Adaptation ExampleAffine‐a
Page 16 and 17: Feature Descriptors• Disadvantage
Page 18 and 19: Overview: SIFT• Extraordinarily r
Page 21 and 22: Other local descriptors:Gray‐scal
Page 23 and 24: Other local descriptors:Gray‐scal
Page 25 and 26: Other local descriptors:Shape conte
Page 27 and 28: Other local descriptors:Geometric B
Page 29 and 30: • AdvantagesValue of Local Featur
Page 31 and 32: Recognition with Local Features•
Page 33 and 34: Reminder: Hough Transform• Origin
Page 35 and 36: View Interpolation• Training- Tra
Page 37 and 38: Location RecognitionTrainingFei-Fei
Page 39 and 40: Introduction• Are you getting the
Page 41 and 42: Introduction• Are you getting the
Page 43 and 44: Why “Recognising Panoramas”?•
Page 49 and 50: Algorithm (Brown & Lowe, 2003)1. Fe
Page 51 and 52: Algorithm (Brown & Lowe, 2003)1. Fe
Page 53 and 54: Slide credit: Matthew BrownFei-Fei
Page 55 and 56:
Probabilistic model for verificatio
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Slide credit: Matthew BrownFei-Fei
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Slide credit: Matthew BrownFei-Fei
Page 61 and 62:
Algorithm (Brown & Lowe, 2003)1. Fe
Page 63 and 64:
Bundle Adjustment• New images ini
Page 65 and 66:
Algorithm (Brown & Lowe, 2003)1. Fe
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2‐band BlendingLow frequency ( >
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2‐band BlendingFei-Fei LiLecture
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ResultsiPhone versionavailablehttp:
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Wide‐Baseline StereoImage from T.
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Recognition of CategoriesConstellat
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What we have learned today• Local
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Recognition with Local Features•
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Basic 2D Transformations• Basic 2
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What Can be Represented by a 22 Mat
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Homogeneous Coordinates• Q: How c
Page 87 and 88:
Let’s Start with Affine Transform
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Fitting an Affine Transformation•
Page 91 and 92:
Fitting a Projective Transformation
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Page 95 and 96:
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Page 99 and 100:
Page 101:
Analyzing Patterns and ShapesSlide
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Lecture 12 - Stanford Vision Lab

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