视觉里程计原理(一)特征提取(SURF算法)

MPIG Seminar0045

Feature Extraction

陈伟杰

Machine Perception and Interaction Group (MPIG)

cwj@

Feature Extraction

Refined based on the book:

Mastering OpenCV with Practical Computer Vision

Projects_full.pdf

and

Bay H, Tuytelaars T, Van Gool L. Surf: Speeded up robust features [M]. Computer vision–ECCV 2006. Springer. 2006: 404-417.

or F for [R|t]Drawing path

The main steps of Visual Odometry

images parameters

Feature Extraction

Feature

matching

Compute E

First

Feature Extraction What feature is?

Characteristics can be easily identified in images

Edges Corners Blobs

lines points

Harris SIFT SURF Commonly used algorithm:

•Corner extractor

•Fast operation •Poor resolution •Not applicable when scale

changes •Blobs extractor

•Slow operation

•Good resolution

•Scale invariance

•Upgrade from

SIFT

•Speed up

•More robust

SURF(Speed Up Robust Feature) opencv2/nonfree/features2d.hpp

SurfFeatureDetectordetector()

SurfDescriptorExtractor

SURF(Speed Up Robust Feature)

Integral image

ii x,y = i=0i≤x j=0

j≤y

I(i,j)

(x,y)

A

C

B D

123

4

ii 1= A ii 2= A + B

D =ii 1+ii 4−ii 2−ii 3

SURF(Speed Up Robust Feature)

Hessian matrix

H x,σ=L xx(x,σ)L xy(x,σ) L xy(x,σ)L yy(x,σ)

L x,σ=Gσ∗I(x,y)Gσ=ð2g(σ)

ðx2g(σ) is Gaussian function andσis variance

It’s the image conversion like frequency domain transform

Approximation of Hessian matrix

Det

H

approx

=D xx D yy−0.9D xy2

L yy L xy

D yy D

xy

We can use integral image to compute easily now

H x,σ=

L xx(x,σ)L xy(x,σ)

L xy(x,σ)L yy(x,σ) Filter template

SURF(Speed Up Robust Feature)

scale space (image pyramid)

SIFT SURF Change size of filter only Easier and faster

Positioning feature points

3×3 window

“x”is extreme point when it’s the

max or the min of 26 points around

Setting a threshold value t

if x>t, x is feature point

The larger t is, the less points will be

SURF feature descriptor

Main direction

Statistics harr wavelet feature around the feature point with the range of 60°in a circle of radius6s(s is the scale of the feature point)

The max value is main direction

SURF feature descriptor

main direction

feature point 20s Every descriptor has

4*4*4=64 dimensional vector