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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