Scale-invariant Feature Transform (SIFT) - Lecture 5

Scale-invariant Feature Transform (SIFT) - Lecture 5

UCF CRCV via YouTube Direct link

Scale Space (Witkin, IJCAI 1983) • Apply whole spectrum of scales

6 of 19

6 of 19

Scale Space (Witkin, IJCAI 1983) • Apply whole spectrum of scales

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Classroom Contents

Scale-invariant Feature Transform (SIFT) - Lecture 5

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  1. 1 SIFT: David Lowe, UBC
  2. 2 SIFT - Key Point Extraction
  3. 3 Advantages
  4. 4 Invariant Local Features
  5. 5 Steps for Extracting Key Points
  6. 6 Scale Space (Witkin, IJCAI 1983) • Apply whole spectrum of scales
  7. 7 Approximation of LOG by Difference of Gaussians
  8. 8 Building a Scale Space
  9. 9 How many scales per octave?
  10. 10 Initial value of sigma
  11. 11 Scale Space Peak Detection
  12. 12 Key Point Localization
  13. 13 Initial Outlier Rejection
  14. 14 Further Outlier Rejection
  15. 15 Orientation Assignment
  16. 16 Similarity to IT cortex
  17. 17 Extraction of Local Image Descriptors at Key Points
  18. 18 Descriptor Regions (n by n)
  19. 19 Key point matching

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