65 lines
1.8 KiB
Markdown
65 lines
1.8 KiB
Markdown
# CSE559A Lecture 20
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## Local feature descriptors
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Detection: Identify the interest points
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Description: Extract vector feature descriptor surrounding each interest point.
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Matching: Determine correspondence between descriptors in two views
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### Image representation
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Histogram of oriented gradients (HOG)
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- Quantization
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- Grids: fast but applicable only with few dimensions
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- Clustering: slower but can quantize data in higher dimensions
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- Matching
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- Histogram intersection or Euclidean may be faster
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- Chi-squared often works better
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- Earth mover’s distance is good for when nearby bins represent similar values
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#### SIFT vector formation
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Computed on rotated and scaled version of window according to computed orientation & scale
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- resample the window
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Based on gradients weighted by a Gaussian of variance half the window (for smooth falloff)
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4x4 array of gradient orientation histogram weighted by magnitude
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8 orientations x 4x4 array = 128 dimensions
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Motivation: some sensitivity to spatial layout, but not too much.
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For matching:
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- Extraordinarily robust detection and description technique
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- Can handle changes in viewpoint
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- Up to about 60 degree out-of-plane rotation
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- Can handle significant changes in illumination
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- Sometimes even day vs. night
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- Fast and efficient—can run in real time
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- Lots of code available
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#### SURF
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- Fast approximation of SIFT idea
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- Efficient computation by 2D box filters & integral images
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- 6 times faster than SIFT
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- Equivalent quality for object identification
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#### Shape context
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#### Self-similarity Descriptor
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## Local feature matching
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### Matching
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