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Lecture 07. 13/12/2011 Shai Avidan. הבהרה: החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת. . Today. SIFT Recognition by Alignment. SIFT. Main questions. Where will the interest points come from? What are salient features that we ’ ll detect in multiple views?

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lecture 07

Lecture 07

13/12/2011

ShaiAvidan

הבהרה: החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת.

today
Today
  • SIFT
  • Recognition by Alignment
main questions
Main questions
  • Where will the interest points come from?
    • What are salient features that we’ll detect in multiple views?
  • How to describe a local region?
  • How to establish correspondences, i.e., compute matches?
scale invariant detection summary
Scale Invariant Detection: Summary

Given:two images of the same scene with a large scale difference between them

Goal:find the same interest points independently in each image

Solution: search for maxima of suitable functions in scale and in space (over the image)

key point localization with dog
Key point localization with DoG

Detect maxima of difference-of-Gaussian (DoG) in scale space

Then reject points with low contrast (threshold)

Eliminate edge responses

Candidate keypoints: list of (x,y,σ)

example of keypoint detection
Example of keypoint detection
  • (a) 233x189 image
  • (b) 832 DOG extrema
  • (c) 729 left after peak
  • value threshold
  • (d) 536 left after testing
  • ratio of principle
  • curvatures (removing edge responses)
main questions1
Main questions
  • Where will the interest points come from?
    • What are salient features that we’ll detect in multiple views?
  • How to describe a local region?
  • How to establish correspondences, i.e., compute matches?
local descriptors
Local descriptors
  • Simplest descriptor: list of intensities within a patch.
  • What is this going to be invariant to?
feature descriptors

p

2

0

Feature descriptors
  • Disadvantage of patches as descriptors:
    • Small shifts can affect matching score a lot
  • Solution: histograms

Source: Lana Lazebnik

feature descriptors sift
Feature descriptors: SIFT
  • Scale Invariant Feature Transform
  • Descriptor computation:
    • Divide patch into 4x4 sub-patches: 16 cells
    • Compute histogram of gradient orientations (8 reference angles) for all pixels inside each sub-patch
    • Resulting descriptor: 4x4x8 = 128 dimensions

David G. Lowe. "Distinctive image features from scale-invariant keypoints.”IJCV 60 (2), pp. 91-110, 2004.

Source: Lana Lazebnik

main questions2
Main questions
  • Where will the interest points come from?
    • What are salient features that we’ll detect in multiple views?
  • How to describe a local region?
  • How to establish correspondences, i.e., compute matches?
keypoint matching
Keypoint Matching
  • Nearest Neighbor algorithm based on L2 distance
  • How to discard bad matches?
    • Threshold on L2 => bad performance
    • Solution: threshold on ratio
feature descriptors sift1
Feature descriptors: SIFT
  • Extraordinarily robust matching technique
    • Can handle changes in viewpoint
      • Up to about 60 degree out of plane rotation
    • Can handle significant changes in illumination
      • Sometimes even day vs. night (below)
    • Fast and efficient—can run in real time
    • Lots of code available
      • http://people.csail.mit.edu/albert/ladypack/wiki/index.php/Known_implementations_of_SIFT

Steve Seitz

working with sift descriptors
Working with SIFT descriptors
  • One image yields:
    • n 128-dimensional descriptors: each one is a histogram of the gradient orientations within a patch
      • [n x 128 matrix]
    • n scale parameters specifying the size of each patch
      • [n x 1 vector]
    • n orientation parameters specifying the angle of the patch
      • [n x 1 vector]
    • n 2d points giving positions of the patches
      • [n x 2 matrix]
recognition with local features
Recognition with Local Features
  • Image content is transformed into local features that are invariant to translation, rotation, and scale
  • Goal: Verify if they belong to a consistent configuration

Local Features, e.g. SIFT

K. Grauman, B. Leibe

Slide credit: David Lowe

finding consistent configurations
Finding Consistent Configurations
  • Global spatial models
    • Generalized Hough Transform [Lowe99]
    • RANSAC [Obdrzalek02, Chum05, Nister06]
    • Basic assumption: object is planar
  • Assumption is often justified in practice
    • Valid for many structures on buildings
    • Sufficient for small viewpoint variations on 3D objects

K. Grauman, B. Leibe

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