11

Internal architecture of distributed real Internal architecture of distributed real time system of image processing and time system of image processing and

pattern recognitionpattern recognition

Gostev I. M. Sevastianiv L. AGostev I. M. Sevastianiv L. AMIEM-PFUR MoscowMIEM-PFUR Moscow

20052005

22

BasedBased supposition(1)supposition(1)

• Pattern – is some description of objectPattern – is some description of object! ! • Pattern recognition – separation of input object in predetermined Pattern recognition – separation of input object in predetermined

class under its features or characteristicsclass under its features or characteristics..

• We use We use contour of objectcontour of object at the heart of recognized objects. (It is at the heart of recognized objects. (It is base of gestalt psychology, and base of human perception of base of gestalt psychology, and base of human perception of object). object).

• Contour have considerable proportion of information about Contour have considerable proportion of information about graphical object.graphical object.

33

BasedBased supposition supposition (2)(2)

Development methodology of graphical pattern Development methodology of graphical pattern recognition to invariance to 2D affine transform recognition to invariance to 2D affine transform (translation, scaling and rotation(translation, scaling and rotation ) with receiving as ) with receiving as result object’s coordinates and its angle of rotation result object’s coordinates and its angle of rotation relatively sample. relatively sample.

44

Plan of based steps from methodology of Plan of based steps from methodology of image processing and pattern recognitionimage processing and pattern recognition

Preliminary processing

Pattern recognition

process

Input criteria and samples

Binarisation

Receive sample contour

Contour tracing Clusterisation

I II III

55

DeltaDelta--segmentation principlessegmentation principles

• Using fly window in which calculated statistical Using fly window in which calculated statistical parameters of signals on based which is assignment parameters of signals on based which is assignment value of cutting level.value of cutting level.

• UsingUsing Delta – modulation with only two resulting value Delta – modulation with only two resulting value of signalof signal..

Input Image

Output image

Differentlevel

of intensity

66

Delta segmentation resultsDelta segmentation results ( (11))

Input image and image after delta - segmentation

PS. Intermediate image processing is absent

77

Compare delta-segmentation Compare delta-segmentation to another methodsto another methods

a) Input Image.

b) Image after processing SUSAN method.

c) Image after processing Delta segmentation method.

d) Image after processing Canny method.

a) b)

c) d)

88

Image contour tracing.

Two fragment of image after step of

contour tracing

N.B. Characteristic feature is receive closed contours of object always.

Zooming of fragment ofimage

99

Step conclude is:

Clusters construction – building verbal description of isolated closed contour of objects and saved its to a file. Any of objects may use as sample for process of pattern recognition

Clustering and samples

Contour of recognition object(Cluster)

Noise’s object (Cluster).

Noise on image (Clusters)

1010

Common method of image processingCommon method of image processing..

0Im 1Im Imn

1f

2f

nf

1f 2f nf

1Im Im , , 1,i ifi i i ff i k

1 2

1 2

1, 2, ,

( ) { , ,... }

{ , ,... }

{ ... }

i m

i m

f f f f

f f fl

f F f f f

Im { , , }, 1,f fI N

1111

Understanding of sample Understanding of sample

Sample – this is verbal description of aggregate of groups of parameters, which Sample – this is verbal description of aggregate of groups of parameters, which unambiguously described a object.unambiguously described a object.

On such ofOn such of groups are: groups are: • Processing image’s methods and condition of refinement of object;Processing image’s methods and condition of refinement of object;• Coordinate parts of objects;Coordinate parts of objects;• Pattern recognition’s methods;Pattern recognition’s methods;• Classification thresholds in pattern recognition’s methods;Classification thresholds in pattern recognition’s methods;

Example of Samples

( , ( ))s s s sx I

( )

0

;n

i

i

I I

( ) ( 1)( ), 1,i iiI I i n

( ) ( ) , 1,i ili I l k

1212

Example of implementation subset of sampleExample of implementation subset of sample

1, , ,i ix y i kThe set 0( )I

primary vectors of properties is as set of points in 2d space:

0 0 1( ) ( ){ ( , ), , }l l lI i x y l k

The set of secondary properties is 1 0 1

0 0 ( ) ( ) ( )( )i I I

Let as named “centre of mass” element of set

10

1 1

1 1( ) ( , );k k

c l c ll l

i x x y yk k

is

1( )I1( )I

1313

Understanding “contour function”Understanding “contour function”

2.

3.

4.

0 13 2 1 ( ) ( )( ( ( , )))r I I

0 0 11

( ') ( ) ( )( , )I I I1

2 2 2( , ) {(( ) ( ) ) , }c c

xR x x y y arctg

y

0 1 ( ') ( , ), ,l lI R l k1.

0 02

( '') ( ')( )I I - sorted function of set 0( ')I on angle

in order its increasing. 0

3( '')( )r I - function of interpolation of points of object

max1/ r /í î ðr r - normalization of contour function.

1414

Third step of methodologyThird step of methodology - - recognition processrecognition process

Input clusters for pattern recognition

System identification is based on

consecutive compare clusters whith

preload sample in set of methods. Each

next method is more calculation

difficult and more precision.

Recognized objects

1

2

3

4

““sieve of the sieve of the methods”methods”

1515

MethodMethod of consecutive weighingof consecutive weighing..

0Im

0Im

1

2

n

1 2 n

1Im Im( , , , ), 1, ;

q q

q qq sI q Q

1 0Im Im Imq q

1Im

Imn

1 2, , ,q m

1Im Im

, , , , , , , ;

s ssI I N

1 2{ , , ..., }Q MCWMCW – – this isthis is::

1818

Geometric correlation #Geometric correlation # 1 ( 1 (GCGC1)1)

Let us ( , )xy as function if “difference values” ( )x ( )yand

( , ) ( ) ( )xy x y where , 0, 360

Let us “function of deviation” as ( )xy

360

1

1( ) ( , )

360xy xy

where 0,360

The function of recognition on based of geometric correlationThe function of recognition on based of geometric correlation ##11 is is

1

11

1, min ( );

0, min ( ) .

Ý Oí î ð í î ð

Ý Oí î ð í î ð

Gr r

GGr r

0,360 where

1919

Geometric correlation #Geometric correlation # 22 ( (GC2GC2))

, 0, 360

Let us function

where

The function of recognition on based of geometric correlationThe function of recognition on based of geometric correlation #2 is#2 is

where

360

1

1( ) | ( ) ( , ) |

360xy xy xy

( , )xy

2

22

1, min ( )

0, min ( )

Ý Oí î ð í î ð

Ý Oí î ð í î ð

Gr r

GGr r

0,360

as mean deviation ( , )xy ( )xy

from

2020

Example of pattern recognition (1)

Sample of objects (zoomed)

Image in real size

Zooming fragment

Message about result of recognition

2121

Example of pattern recognition (2)

Sample of objects.

Recognized objects

2222

Example of pattern recognition (3)

Sample of objects

2323

Parallel processing of input Parallel processing of input graphical flowgraphical flow

V

Data General

Data General

Data General

Data General

Data General

Cutting

Parallelprocessing

Result of image processing and pattern recognition

Input information flow is cutting on part and each part is processing into parallel process.

2424

Conveyer processing image and recognition inConveyer processing image and recognition in STIPR2000STIPR2000

k

l

m

n

1-st Method

2-nd method 3-d

Method

N- method

Input image

k, l, m, n – required numbers of line in a method

Outputresults

Conveyer of methods image

2525

Controller of flowController of flow

Controller of flow

Method 1 Method 1 Method 1Buffer Buffer ...Input

graphicflow

Result ofprocessing

andrecognition

2626

Work diagram of the system for dynamic Work diagram of the system for dynamic identification graphic object (identification graphic object (11))

(x, y ), α

1-st host 2-nd host

2727

Work diagram of the system for dynamic Work diagram of the system for dynamic identification graphic object (2)identification graphic object (2)

(x, y ), α

1-st host

2-nd host

2828

Address:

Moscow 117419

Ordzhonikidze 3

Тел: |(095) 916 -8886

mob 7 916-610-7801

Fax: (095) 952-2823

E-mail: igostev@gmail.com igostev@list.ru

The work is carried out by

the associated professor

Gostev Ivan Michailovich

and his post-graduates students.

Thank You!