Region description Information that lets you recognise a region

Region description

Information that lets you recognise a region.

Image Processing and Computer Vision: 5 3

Introduction Region detection isolates regions

that differ from neighbours Description identifies property

values Labelling identifies regions


Contents Features derived from binary

images Structure Region (CCA) Shape

Texture Surface shape


Features derived from binary images Connected component analysis Perimeter Area


Connected Component Analysis To identify groups of connected

pixels To label separate regions


Algorithm

First passIf zero neighbours have a label

Pixel receives the next free labelIf one or more neighbours have same label

Pixel receives same label;If two or more neighbours have different labels

Pixel receives one label, equivalence is recorded

Second passRelabel all equivalent labels

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321

4


Borders Straight lines

Chain codes Polylines

Curved lines Splines Circles

Phi-S Snakes


Chain Codes

0

1

2

3

45

6

7

Trace the object outline - follow pixels on boundaryCode directions of movement

Description is position independent, orientation dependent

Can use differential chain codes


Perimeter From Chain Code

Even codes have length 1Odd codes have length 2

Perimeter length = #even + 2 #odd


Area From Chain Code

0 1 2 3 4 5 6 7

h

0 h+1/2 h h-1/2 0 -h-1/2 -h -h+1/2

h is measured from an arbitrary datum,e.g. y co-ordinate of start of codes.


Crack Codes These follow pixel boundaries

Not pixel centres Same representation of

displacement Longer coding More accurate



Demo


Polyline Representation Represent the line by a set of joined

line segments Polyline and original endpoints

coincide Segments interpolate edge points Computed by curve splitting or

segment merging Decomposing initial curve Combining curve segments


Polyline Splitting(cf Hopalong last week)For each curve segment

D = maximum distance of segment to line between endpoints

If D > thresholdInsert a vertex


Segment Merging May be necessary between

endpoints of adjacent segments Use edge following techniques


Curved Line Sections Polyline representation is suitable

for linear sections Curved sections are inefficiently

represented Alternatives

Splines Circles


B-Splines A curve represented by

control points Endpoints fixed by two

control points Shape controlled by two

control points


If control points can be found Curve is compactly represented


Fourier Descriptors Represent co-ordinates of boundary

points as complex numbers They can be Fourier transformed Coefficients of transform are the

Fourier descriptors Retain more or fewer according to

desired accuracy


Example






Phi-S Curves

s(i, si) • characteristic of the object’s shape• independent of location• dependent on orientation



Snakes, Active/Dynamic Contours Borders follow outline of object Outline obscured? Snake provides a solution


Algorithm Snake computes smooth,

continuous border Minimises

Length of border Curvature of border

Against an image property Gradient?

EEEE imagecurvaturelengthtot


Minimisation Initialise snake Integrate energy along it Iteratively move snake to global

energy minimum



Texture Two definitions

A pseudoregular arrangement of a primitive element

A pseudorandom distribution of brightness values


Examples


Classification A useful property for identifying

surfaces Aerial photographs Medical imagery


Structural Texture Representations

Require Texture primitive - texel Placement rule

Ideal for regular - man-made - textures


Fourier Descriptors Placement rule periodicity Can use

Autocorrelation Fourier transform

To recognise it


Fourier Descriptor Compute modulus

of transform Energy in

different regions is characteristic of texture


Markov Random Field Representations Each pixel value a combination of

neighbours plus noise Find coefficients of model

Characterise texture Least squares minimisation

crujihjcirIcrINji

,,,,0,0,


Statistical Descriptions Better suited to pseudorandom,

natural textures First Order statistics Second order statistics


First Order Statistics Statistical descriptions of grey level

distribution Mean grey value Deviation of grey values Coefficient of variation etc.

Can give useful results Generally too sensitive to factors other than

identity of surface


Second Order Statistics Measures involving multiple pixels

Joint difference histogram Histogram of differences between

adjacent pixels Co-Occurrence matrices

Measure frequency of specific pairs of grey values


Co-Occurrence Matrices Define a relative separation vector

e.g. 3 pixels across, 2 up Use each pair of pixels separated by the

vector as matrix indices Increment this matrix element Shape of matrix characterises the

texture Can be characterised by factors derived

from it.


Edge Frequency Density of microedges is

characteristic of texture Apply an edge detector

Sobel is suitable Threshold result Compute density of edge elements



Shape from … To recover shapes of objects in a

scene By identifying spatial properties of

surface patches


Shape from Motion From

4 views Of

3 non-colinear points Can compute

motion and relative locations of points


Shape from Photometric Stereo Capture images of a scene in two

cameras Must know

Cameras’ separation Cameras’ relative orientation (parallel

in example) Co-ordinates of corresponding points

in images


Plan view of cameras’ optical paths.

camera 1

camera 2

Imageplane

Scene Opticalcentres

d

dx

x+d

z f

(x’, y’, f)

(x’’, y’’, f)

(x, y,z)

centreline


'''2

'''2

'''

'''

:

xxdf

z

or

fzx

fzx

d

gsubtractin

fzx

dxandfzx

dx

rearrange

fx

zdx

andfx

zdx

trianglessimilarUse


Provided that cameras are alignedseparation is knowncorresponding points are identified

The point’s depth can be computed.

Correspondence problem examined later.


Shape from Shading For matt surfaces, proportion of

incident light reflected depends on Surface reflectance Surface orientation with respect to

light source

cos.0

kI

I


If k can be estimated Image value for = 0

Can estimate cos , hence throughout image.

Surface orientation is not determined uniquely Two angles are needed


Shape from Texture

Apparent texture of a surface is dependent on the surface’s Orientation Range


Method Must be able to identify

fundamental texture elements Assume they are invariant Compute mapping to transform

each element to a standard appearance

Mapping determines surface orientation.


Summary Binary image features

Skeleton Boundaries

Texture Shape from …


There is no reason why anyone would want a computer in their homeKen Olsen, chairman DEC, 1977

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Region description Information that lets you recognise a region