OBJECT RECOGNITION

OBJECT RECOGNITION

The next step in Robot Vision is the Object Recognition. This problem is accomplished using the extracted feature information.

The object recognition algorithm is to be powerful and fast so that the required object is uniquely recognized.

Image Features

Image feature refers either to

– a global property of an image or a part of it– for example: average grey level, area in pixels – recognition properties important

or

– part of image with some special properties– for example: line, circle, textured region– location important

Good Features

● Meaningful– associated to interesting scene elements typically sharp intensity variations (contours of objects), or– regions of uniform intensity (planar surfaces)

● Detectable– we must be able to locate the features algorithmically– different algorithms for different features

● We can not get perfect features

Feature types

• Geometrical entities

– points• for example: edge points, corner points, interest

points

• regions

• lines

• circles, ellipses

• contours

Edge Detection – Problem

Edges

• Edge points are pixels where image values undergo a sharp variation

Corner Detection

• Corners are well defined in location

– useful features to track in image sequences

– should be understood as corner structures in

image intensity, not corners of objects

● Image gradient tells us about changes

– How to calculate the gradient?

– How does gradient behave near corners?

There are mainly two methods for object recognition

(a)Template matching technique

A template is provided to computer and the computer is trained to match the object with the template irrespective of object orientation.

(b)Structural technique

Several structural techniques are available. These may take the features also in to account. We discuss a common method known as

CHAIN CODE. Here there are two approaches

(a) 4-Directional Chain Code

(b) 8 Directional Chain code

We shall describe (a) first; (b) is an extension of (a).

Each object is coded in terms of this 4-directional chain code

STARTStart with the start point and go along the arrows

Comparing the contour of object with respect to 4-directional chain code converter, we get

Chain code of Object: 03032211

4-directional converter

8-directional converter

0

1

2

3

0

12

3

4

5

67

OBJECT

CHAIN CODE : 03032211

We then get the difference;

Difference between 0 and 3 is 3 (ref 4 direc. converter)

Difference between 3 and 0 is 1

Difference between 0 and 3 is 3 and so on

Difference between 1 and 1 is 0

Difference between 1 and 0 is 3.

Difference Code is : 31330303

Take the minimum (decimal)value of Difference code as

SHAPE NUMBER: 03033133

This SHAPE number is for the object, uniquely recognized, independent of rotation (by 900)

Another Object

Verify:

Chain Code:

0330011033323333221112111

Difference code:

3010103300310003030013003

Shape Number:

0003030013003301010330031

If the object edges are of slopes 450, 1350 , - 450 and - 1350, then we can use the 8-Directional converter. The procedure of getting the shape number is the same.

This method of object recognition is fast and can be used for different shapes of objects to be recognized if they are coming in a random sequence.

Advantage of Chain Coding

• Reduce storage space as number and value of pixels are not required.

• It can be use to compute the values of some features of objects such as area, perimeter, width etc

FEATURE EXTRACTION:In robot vision, it is often necessary to

distinguish one object from another. This is accomplished by mean of features that uniquely characterize the object.

Some features of objects that can be used in Robot Vision are:

(a) Area (b) Minimum Enclosing rectangle

(c) Diameter (d) center of gravity

(e)Perimeter (f) eccentricity (g) Aspect Ratio

(h) Number of holes (i) Moments

We shall illustrate the features through an example:

• Image Area

• Center of Gravity (COG)

Or centroid for xc and yc. The area and COG is used to identify the position of the object

m

x

n

y

yxIA1 1

),(

m

x

n

yc yxxIA

x1 1

),(1

m

x

n

yc yxyIA

y1 1

),(1

• Moments- A sequence of numbers characterzing the shape of an object

• The sum of power (j+k) is the order of the moment

0,0),(,

kjyxIyxMOyx

kjjk

m

x

n

y

yxIAM1 1

00 ),(

m

x

n

yc yxxIA

xM1 1

10 ),(1

m

x

n

yc yxyIA

yM1 1

01 ),(1

• If the COG is known we can determine the central of moment

• Because object is balanced at COG, the first order moment is zero

0,0),()()(,

kjyxIyyxxOyx

kc

jcjk

m

x

n

yc

m

x

n

yc

yxIyy

yxIxx

1 101

1 110

0),()(

0),()(

• The second order moment give the moment inertia of the image

m

x

n

ycc

m

x

n

yc

m

x

n

yc

yxIyyxx

yxIyy

yxIxx

1 111

1 1

202

1 1

220

),())((

),()(

),()(

• Orientation-the angle of inclination ),2(2tan

2

1022011 A

• Eccentricity- maximum chord length is along the principal axis or major axis of object and minimum chord length is perpendicular to major axis

lengthchordMinimum

lengthchordMaximumtyEccentrici

22

,)()(max ijij

jiyyxx

lengthchordMaximum

• Roundness,

• Aspect Ratio=Length of Rectangle enclosing object» Width of rectangle enclosing Object

A

yxR cc

22

EXAMPLE: Let an original image of an object undergone several image processing techniques and finally available to us as a pixel pattern shown below:

Some of features can be computed as:

(a) Moment ( M00 ) = = 24

(b)Eccentricity = (Max x-length) / (Max y-length) = 9/4

(c) Perimeter = 26

(d)Area = 24

(e) Diameter = 9

(f) Thinness = {Diameter / area }

= ( 9 / 24 ) = 0.375

(g) compactness =

{ (perimeter)2 / area } = ( 26 2 /24 ) = 28.17

(a) No: of holes = 0

Objects having these features belong to one category

yxyx

,

00

Example

Matching

• Recognition techniques based on matching represent each class by a proto-type pattern vector.

• An unknown pattern is assigned to the class to which it is closest in terms of a predetermine metric.

• The common approach is by minimum distance classifier

Some of the edge identified products

Minimum distance classifier

• In image processing system, an object is described by a pattern vector

• Pattern recognition is to develop or use some techniques or algorithms to recognize objects in images.

• Minimum distance classifier is commonly used and based decision functions.

Decision Function

• The function is to determine decision boundary to separate different pattern classes.

• Example:

• Solution:

Optimum Statistical Classifier

• Probability consideration become important in pattern recognition under random pattern classes

• For the above equation to hold we must know the probability function.

• Thus, the Gaussian probability density function is considered

Bayes Classifier for Gausssian pattern classes