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Lecture 5: Feature detection and matching

CS4670 / 5670: Computer Vision. Noah Snavely. Lecture 5: Feature detection and matching. Reading. Szeliski: 4.1. Feature extraction: Corners and blobs. Motivation: Automatic panoramas. Credit: Matt Brown. Motivation: Automatic panoramas. HD View.

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Lecture 5: Feature detection and matching

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  1. CS4670 / 5670: Computer Vision Noah Snavely Lecture 5: Feature detection and matching

  2. Reading • Szeliski: 4.1

  3. Feature extraction: Corners and blobs

  4. Motivation: Automatic panoramas Credit: Matt Brown

  5. Motivation: Automatic panoramas HD View http://research.microsoft.com/en-us/um/redmond/groups/ivm/HDView/HDGigapixel.htm Also see GigaPan: http://gigapan.org/

  6. Why extract features? • Motivation: panorama stitching • We have two images – how do we combine them?

  7. Why extract features? • Motivation: panorama stitching • We have two images – how do we combine them? Step 1: extract features Step 2: match features

  8. Why extract features? • Motivation: panorama stitching • We have two images – how do we combine them? Step 1: extract features Step 2: match features Step 3: align images

  9. Image matching by Diva Sian by swashford TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAAA

  10. Harder case by Diva Sian by scgbt

  11. Harder still?

  12. Answer below (look for tiny colored squares…) NASA Mars Rover images with SIFT feature matches

  13. Feature Matching

  14. Feature Matching

  15. Invariant local features Find features that are invariant to transformations • geometric invariance: translation, rotation, scale • photometric invariance: brightness, exposure, … Feature Descriptors

  16. Advantages of local features Locality • features are local, so robust to occlusion and clutter Quantity • hundreds or thousands in a single image Distinctiveness: • can differentiate a large database of objects Efficiency • real-time performance achievable

  17. More motivation… Feature points are used for: • Image alignment (e.g., mosaics) • 3D reconstruction • Motion tracking • Object recognition • Indexing and database retrieval • Robot navigation • … other

  18. What makes a good feature? Snoop demo

  19. Want uniqueness Look for image regions that are unusual • Lead to unambiguous matches in other images How to define “unusual”?

  20. Local measures of uniqueness Suppose we only consider a small window of pixels • What defines whether a feature is a good or bad candidate? Credit: S. Seitz, D. Frolova, D. Simakov

  21. Local measure of feature uniqueness • How does the window change when you shift it? • Shifting the window in any direction causes a big change “corner”:significant change in all directions “flat” region:no change in all directions “edge”: no change along the edge direction Credit: S. Seitz, D. Frolova, D. Simakov

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