Feature Detection. Image features Global features –global properties of an image, including...

Feature Detection

Image features

• Global features – global properties of an image, including intensity

histogram, frequency domain descriptors, covariance matrix and high order statistics, etc.

• Local features – local regions with special properties, including edges,

corners, lines, curves, regions with special properties, etc

• Depending on applications, various features are useful. We will focus on edges and corners in this lecture

Edges

• Edge points are pixels at or around which the image values undergo a sharp variation – pixels with large gradient

Canny hysteresis thresholding• Goal: find local maximums that are true edges• Assumption

– true edges should have large strength in general– pixels belong to true edges are connected to each other

• Problem: some edge pixels has lower values than false edge pixels

• Solution: hysteresis algorithm – a pixel belongs to a true edge if its edge strength is larger than a threshold t and is linked to some points with edge strength larger than t

Fitting Lines to Edges (Least Squares)

cmxy

cmxy ii

),( ii yx

y

x

Given: Many pairs

Find: Parameters

Minimize: Average square distance:

Using:

Note:

),( ii yx

),( cm

i

ii

N

cmxyE

2)(

0&0

c

E

m

E xmyc

ii

iii

xx

yyxxm

2)(

))((

N

yy i

i

N

xx i

i

Problem with Parameterization

y

x

Line that minimizes E!!

Solution: Use a different parameterization

(same as the one we used in computing Minimum Moment of Inertia)

Note: Error E must be formulated carefully!

i

ii yxN

E 2)sincos(1

Computer Vision - A Modern ApproachSet: Fitting

Slides by D.A. Forsyth

Line fitting can be max.likelihood - but choice ofmodel is important

Curve Fitting

y

x

Find Polynomial:

that best fits the given points

Minimize:

Using:

Note: is LINEAR in the parameters (a, b, c, d)

),( ii yx

dcxbxaxxfy 23)(

i

iiii dcxbxaxyN

223 )]([1

0,0,0,0

d

E

c

E

b

E

a

E

)(xf

Line Grouping Problem

Slide credit: David Jacobs

This is difficult because of:

• Extraneous data: clutter or multiple models– We do not know what is part of the model?– Can we pull out models with a few parts from much

larger amounts of background clutter?

• Missing data: only some parts of model are present

• Noise

• Cost:– It is not feasible to check all combinations of features

by fitting a model to each possible subset

Hough Transform

• Elegant method for direct object recognition

• Edges need not be connected

• Complete object need not be visible

• Key Idea: Edges VOTE for the possible model

Image and Parameter Spaces

cmxy

),( ii yx

y

x

Equation of Line:

Find:

Consider point:

cmxy

),( cm

),( ii yx

iiii ymxcorcmxy m

c

),( cm

Image Space

Parameter Space

Parameter space also called Hough Space

Line Detection by Hough Transformy

x

),( cm

Parameter Space

1 1

1 1

1 1

2

1 1

1 1

1 1

),( cmA

Algorithm:

• Quantize Parameter Space

• Create Accumulator Array

• Set

• For each image edge increment:

• If lies on the line:

• Find local maxima in

),( cm

),( cmA

cmcmA ,0),(

),( ii yx

1),(),( cmAcmA

),( cm

),( cmAii ymxc

Better Parameterization

NOTE:

Large Accumulator

More memory and computations

Improvement:

Line equation:

Here

Given points find

m

sincos yx

max0

20

(Finite Accumulator Array Size)

),( ii yx ),(

),( ii yx

y

xImage Space

Hough Space

?

Hough Space Sinusoid

Image space Votes

Horizontal axis is θ, vertical is rho.

Image space

votes

Mechanics of the Hough transform

• Difficulties– how big should the cells

be? (too big, and we merge quite different lines; too small, and noise causes lines to be missed)

• How many lines?– Count the peaks in the

Hough array– Treat adjacent peaks as

a single peak

• Which points belong to each line?– Search for points close to

the line– Solve again for line and

iterate

Fewer votes land in a single bin when noise increases.

Adding more clutter increases number of bins with false peaks.

Real World Example

Original Edge Detection

Found Lines

Parameter Space

Finding Circles by Hough Transform

Equation of Circle:

222 )()( rbyax ii

If radius is known:

),( baAAccumulator Array

(2D Hough Space)

Finding Circles by Hough Transform

Equation of Circle:

222 )()( rbyax ii

If radius is not known: 3D Hough Space!

Use Accumulator array ),,( rbaA

What is the surface in the hough space?

Using Gradient Information• Gradient information can save lot of computation:

Edge Location

Edge Direction

Need to increment only one point in Accumulator!!

i),( ii yx

Assume radius is known:

sin

cos

ryb

rxa

Real World Circle Examples

Crosshair indicates results of Hough transform,bounding box found via motion differencing.

Finding Coins

Original Edges (note noise)

Finding Coins (Continued)

Penny

Quarters

Finding Coins (Continued)

Coin finding sample images from: Vivek Kwatra

Note that because the quarters and penny are different sizes, a different Hough transform (with separate accumulators) was used for each circle size.

Generalized Hough Transform

• Model Shape NOT described by equation

Generalized Hough Transform

Find Object Center given edges

Create Accumulator Array

Initialize:

For each edge point

For each entry in table, compute:

Increment Accumulator:

Find Local Maxima in

),( cc yxA

),(0),( cccc yxyxA

),,( iii yx

1),(),( cccc yxAyxA

),( cc yxA

ik

ikic

ik

ikic

ryy

rxx

sin

cos

ikr

),( cc yx ),,( iii yx

Hough Transform: Comments

• Works on Disconnected Edges

• Relatively insensitive to occlusion

• Effective for simple shapes (lines, circles, etc)

• Trade-off between work in Image Space and Parameter Space

• Handling inaccurate edge locations:

• Increment Patch in Accumulator rather than a single point

Corner features

• Sources: intersection of image lines, corner patterns in the images, etc

• Stable across sequence of images

An introductory example:

Harris corner detector

C.Harris, M.Stephens. “A Combined Corner and Edge Detector”. 1988

The Basic Idea

• We should easily recognize the point by looking through a small window

• Shifting a window in any direction should give a large change in intensity

Harris Detector: Basic Idea

“flat” region:no change in all directions

“edge”:no change along the edge direction

“corner”:significant change in all directions

Harris Detector: Mathematics

2

,

( , ) ( , ) ( , ) ( , )x y

E u v w x y I x u y v I x y

Window-averaged change of intensity for the shift [u,v]:

IntensityShifted intensity

Window function

orWindow function w(x,y) =

Gaussian1 in window, 0 outside


( , ) ,u

E u v u v Mv

Expanding E(u,v) in a 2nd order Taylor series expansion, we have,for small shifts [u,v], a bilinear approximation:

2

2,

( , ) x x y

x y x y y

I I IM w x y

I I I

where M is a 22 matrix computed from image derivatives:


( , ) ,u

E u v u v Mv

Intensity change in shifting window: eigenvalue analysis

1, 2 – eigenvalues of M

direction of the slowest change

direction of the fastest change

(max)-1/2

(min)-1/2

Ellipse E(u,v) = const

Selecting Good Features

l1 and l2 are large


large l1, small l2


small l1, small l2


1

2

“Corner”

1 and 2 are large, 1 ~ 2;E increases in all directions

1 and 2 are small;E is almost constant in all directions

“Edge”

1 >> 2

“Edge”

2 >> 1

“Flat” region

Classification of image points using eigenvalues of M:


Measure of corner response:

2det traceR M k M

1 2

1 2

det

trace

M

M

(k – empirical constant, k = 0.04-0.06)


1

2“Corner”

“Edge”

“Edge”

“Flat”

• R depends only on eigenvalues of M

• R is large for a corner

• R is negative with large magnitude for an edge

• |R| is small for a flat regionR > 0

R < 0

R < 0|R| small

Harris Detector

• The Algorithm:– Find points with large corner response function R (R

> threshold)– Take the points of local maxima of R

Harris Detector: Workflow

Harris Detector: WorkflowCompute corner response R

Harris Detector: WorkflowFind points with large corner response: R>threshold

Harris Detector: WorkflowTake only the points of local maxima of R

Harris Detector: Workflow

Harris Detector: Summary

• Average intensity change in direction [u,v] can be expressed as a bilinear form:

• Describe a point in terms of eigenvalues of M:measure of corner response

• A good (corner) point should have a large intensity change in all directions, i.e. R should be large positive

( , ) ,u

E u v u v Mv

2

1 2 1 2R k

Harris Detector: Some Properties

• Rotation invariance?


• Rotation invariance

Ellipse rotates but its shape (i.e. eigenvalues) remains the same

Corner response R is invariant to image rotation


• Invariance to image intensity change?


• Partial invariance to additive and multiplicative intensity changes

Only derivatives are used => invariance to intensity shift I I + b

Intensity scale: I a I

R

x (image coordinate)

threshold

R

x (image coordinate)


• Invariant to image scale?


• Not invariant to image scale!

All points will be classified as edges

Corner !

We want to:

detect the same interest points regardless of image changes

Models of Image Change

• Geometry– Rotation– Similarity (rotation + uniform scale)

– Affine (scale dependent on direction)valid for: orthographic camera, locally planar object

• Photometry– Affine intensity change (I a I + b)

Rotation Invariant Detection

• Harris Corner Detector

C.Schmid et.al. “Evaluation of Interest Point Detectors”. IJCV 2000

Scale Invariant Detection

• Consider regions (e.g. circles) of different sizes around a point

• Regions of corresponding sizes will look the same in both images


• The problem: how do we choose corresponding circles independently in each image?


• Solution:– Design a function on the region (circle), which is

“scale invariant” (the same for corresponding regions, even if they are at different scales)

Example: average intensity. For corresponding regions (even of different sizes) it will be the same.

scale = 1/2

– For a point in one image, we can consider it as a function of region size (circle radius)

f

region size

Image 1 f

region size

Image 2


• Common approach:

scale = 1/2

f

region size

Image 1 f

region size

Image 2

Take a local maximum of this function

Observation: region size, for which the maximum is achieved, should be invariant to image scale.

s1 s2

Important: this scale invariant region size is found in each image independently!


• A “good” function for scale detection: has one stable sharp peak

f

region size

bad

f

region size

bad

f

region size

Good !

• For usual images: a good function would be a one which responds to contrast (sharp local intensity change)


• Functions for determining scale

2 2

21 22

( , , )x y

G x y e

2 ( , , ) ( , , )xx yyL G x y G x y

( , , ) ( , , )DoG G x y k G x y

Kernel Imagef Kernels:

where Gaussian

Note: both kernels are invariant to scale and rotation

(Laplacian)

(Difference of Gaussians)

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)

Methods:

1. Harris-Laplacian [Mikolajczyk, Schmid]: maximize Laplacian over scale, Harris’ measure of corner response over the image

2. SIFT [Lowe]: maximize Difference of Gaussians over scale and space

Point Descriptors

• We know how to detect points• Next question: How to match them?

?

Point descriptor should be:1. Invariant2. Distinctive

SIFT – Scale Invariant Feature Transform1

• Empirically found2 to show very good performance, invariant to image rotation, scale, intensity change, and to moderate affine transformations

1 D.Lowe. “Distinctive Image Features from Scale-Invariant Keypoints”. Accepted to IJCV 20042 K.Mikolajczyk, C.Schmid. “A Performance Evaluation of Local Descriptors”. CVPR 2003

Scale = 2.5Rotation = 450

CVPR 2003 Tutorial

Recognition and Matching Based on Local Invariant Features

David Lowe

Computer Science Department

University of British Columbia

Invariant Local Features• Image content is transformed into local feature

coordinates that are invariant to translation, rotation, scale, and other imaging parameters

SIFT Features

Advantages of invariant local features

• Locality: features are local, so robust to occlusion and clutter (no prior segmentation)

• Distinctiveness: individual features can be matched to a large database of objects

• Quantity: many features can be generated for even small objects

• Efficiency: close to real-time performance

• Extensibility: can easily be extended to wide range of differing feature types, with each adding robustness

Scale invariance

Requires a method to repeatably select points in location and scale:

• The only reasonable scale-space kernel is a Gaussian (Koenderink, 1984; Lindeberg, 1994)

• An efficient choice is to detect peaks in the difference of Gaussian pyramid (Burt & Adelson, 1983; Crowley & Parker, 1984 – but examining more scales)

• Difference-of-Gaussian with constant ratio of scales is a close approximation to Lindeberg’s scale-normalized Laplacian (can be shown from the heat diffusion equation)

B l u r

R e s a m p l e

S u b t r a c t

B l u r

R e s a m p l e

S u b t r a c t

Scale space processed one octave at a time

Key point localization

• Detect maxima and minima of difference-of-Gaussian in scale space

• Fit a quadratic to surrounding values for sub-pixel and sub-scale interpolation (Brown & Lowe, 2002)

• Taylor expansion around point:

• Offset of extremum (use finite differences for derivatives):

B l u r

R e s a m p l e

S u b t r a c t

Select canonical orientation

• Create histogram of local gradient directions computed at selected scale

• Assign canonical orientation at peak of smoothed histogram

• Each key specifies stable 2D coordinates (x, y, scale, orientation)

0 2

Example of keypoint detection

Threshold on value at DOG peak and on ratio of principle curvatures (Harris approach)

(a) 233x189 image(b) 832 DOG extrema(c) 729 left after peak value threshold(d) 536 left after testing ratio of principle curvatures

SIFT vector formation• Thresholded image gradients are sampled over

16x16 array of locations in scale space• Create array of orientation histograms• 8 orientations x 4x4 histogram array = 128

dimensions

Feature stability to noise• Match features after random change in image

scale & orientation, with differing levels of image noise

• Find nearest neighbor in database of 30,000 features

Feature stability to affine change• Match features after random change in image

scale & orientation, with 2% image noise, and affine distortion

• Find nearest neighbor in database of 30,000 features

Distinctiveness of features• Vary size of database of features, with 30 degree

affine change, 2% image noise• Measure % correct for single nearest neighbor

match

A good SIFT features tutorial

http://www.cs.toronto.edu/~jepson/csc2503/tutSIFT04.pdf

By Estrada, Jepson, and Fleet.



Other References

• Harris & Stephens (1988)http://www.bmva.org/bmvc/1988/avc-88-023.pdf

• Lowe (2004)http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf

• Mikolajczyk & Schmid (2005)http://lear.inrialpes.fr/pubs/2005/MS05/mikolajczyk_pami05.pdf

• Brown & Lowe (2007)http://cvlab.epfl.ch/~brown/papers/ijcv2007.pdf

http://www.bmva.org/bmvc/1988/avc-88-023.pdf



http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf





http://lear.inrialpes.fr/pubs/2005/MS05/mikolajczyk_pami05.pdf




http://cvlab.epfl.ch/~brown/papers/ijcv2007.pdf

http://cvlab.epfl.ch/~brown/papers/ijcv2007.pdf

Feature Detection. Image features Global features –global properties of an image, including...

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