Scale invariant feature transform sift
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Scale Invariant Feature Transform (SIFT). Outline. What is SIFT Algorithm overview Object Detection Summary. Overview. 1999 Generates image features, “keypoints” invariant to image scaling and rotation partially invariant to change in illumination and 3D camera viewpoint

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Scale Invariant Feature Transform (SIFT)

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Scale invariant feature transform sift

Scale Invariant Feature Transform (SIFT)


Outline

Outline

  • What is SIFT

  • Algorithm overview

  • Object Detection

  • Summary


Overview

Overview

  • 1999

  • Generates image features, “keypoints”

    • invariant to image scaling and rotation

    • partially invariant to change in illumination and 3D camera viewpoint

    • many can be extracted from typical images

    • highly distinctive


Algorithm overview

Algorithm overview

  • Scale-space extrema detection

    • Uses difference-of-Gaussian function

  • Keypoint localization

    • Sub-pixel location and scale fit to a model

  • Orientation assignment

    • 1 or more for each keypoint

  • Keypoint descriptor

    • Created from local image gradients


Scale space

Scale space

  • Definition:

    where


Scale space1

Scale space

  • Keypoints are detected using scale-space extrema in difference-of-Gaussian function D

  • D definition:

  • Efficient to compute


Relationship of d to

Relationship of D to

  • Close approximation to scale-normalized Laplacian of Gaussian,

  • Diffusion equation:

  • Approximate ∂G/∂σ:

    • giving,

  • When D has scales differing by a constant factor it already incorporates the σ2 scale normalization required for scale-invariance


Scale space construction

Scale space construction

2k2σ

2kσ

σ

2kσ

σ


Scale space images

Scale space images

first octave

second octave

third octave

fourth octave


Difference of gaussian images

Difference-of-Gaussian images

first octave

second octave

third octave

fourth octave


Frequency of sampling

Frequency of sampling

  • There is no minimum

  • Best frequency determined experimentally


Prior smoothing for each octave

Prior smoothing for each octave

  • Increasing σ increases robustness, but costs

  • σ = 1.6 a good tradeoff

  • Doubling the image initially increases number of keypoints


Finding extrema

Finding extrema

  • Sample point is selected only if it is a minimum or a maximum of these points

Extrema in this image

DoG scale space


Localization

Localization

  • 3D quadratic function is fit to the local sample points

  • Start with Taylor expansion with sample point as the origin

    • where

  • Take the derivative with respect to X, and set it to 0, giving

  • is the location of the keypoint

  • This is a 3x3 linear system


Localization1

Localization

  • Derivatives approximated by finite differences,

    • example:

  • If X is > 0.5 in any dimension, process repeated


Filtering

Filtering

  • Contrast (use prev. equation):

    • If | D(X) | < 0.03, throw it out

  • Edge-iness:

    • Use ratio of principal curvatures to throw out poorly defined peaks

    • Curvatures come from Hessian:

    • Ratio of Trace(H)2 and Determinant(H)

    • If ratio > (r+1)2/(r), throw it out (SIFT uses r=10)


Orientation assignment

Orientation assignment

  • Descriptor computed relative to keypoint’s orientation achieves rotation invariance

  • Precomputed along with mag. for all levels (useful in descriptor computation)

  • Multiple orientations assigned to keypoints from an orientation histogram

    • Significantly improve stability of matching


Keypoint images

Keypoint images


Descriptor

Descriptor

  • Descriptor has 3 dimensions (x,y,θ)

  • Orientation histogram of gradient magnitudes

  • Position and orientation of each gradient sample rotated relative to keypoint orientation


Descriptor1

Descriptor

  • Weight magnitude of each sample point by Gaussian weighting function

  • Distribute each sample to adjacent bins by trilinear interpolation (avoids boundary effects)


Descriptor2

Descriptor

  • Best results achieved with 4x4x8 = 128 descriptor size

  • Normalize to unit length

    • Reduces effect of illumination change

  • Cap each element to 0.2, normalize again

    • Reduces non-linear illumination changes

    • 0.2 determined experimentally


Object detection

Object Detection

  • Create a database of keypoints from training images

  • Match keypoints to a database

    • Nearest neighbor search


Pca sift

PCA-SIFT

  • Different descriptor (same keypoints)

  • Apply PCA to the gradient patch

  • Descriptor size is 20 (instead of 128)

  • More robust, faster


Summary

Summary

  • Scale space

  • Difference-of-Gaussian

  • Localization

  • Filtering

  • Orientation assignment

  • Descriptor, 128 elements


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