1

Machine Vision

2

VISION

the most powerful sense

3

OUTLINE

1. Properties of Machine Vision Systems

Methods in Machine Vision

2. Example: Fingerprint Recognition System

4

Machine Vision System

• creates a model of the real world from images

• recovers useful information about a scene from its two dimensional projections

5

A typical control system:

Scene Image Description

Application feedback

Imaging device

MACHINE VISION

Illumination

6

Components of a M.V. system

HARDWARESOFTWARE

• Optics (lenses, lighting)• Cameras • Interface (frame grabber)• Computer

7

Components of a M.V. system

HARDWARESOFTWARE

-There is no universal vision system-One system for each application

• Libraries with custom code developed using Visual C/C++, Visual Basic, or Java

• Graphical programming environments

8

Application Fields

• Medical imaging• Industrial automation • Robotics • Radar Imaging • Forensics • Remote Sensing • Cartography • Character recognition

9

Machine Vision StagesImage Acquisition

Image Processing

Image Segmentation

Image Analysis

Pattern Recognition

Analog to digital conversion

Remove noise,improve contrast…

Find regions (objects) in the image

Take measurements of objects/relationships

Match the description withsimilar description of known objects (models)

10

Image Formation

• Perspective projection

• Orthographic projection

3D 2DProjection

11

Digital Image Representation

Image: 2D array of gray level or color values

Pixel: Array element Picture Element

•It`s 2D•It`s a square•It has one color•It may be any color

Pixel value: Arithmetic value of gray level or color intensity

Gray level image: f = f(x,y) Color image: f = {Rred(x,y), Ggreen(x,y), Bblue(x,y)}

12

Digital Image Formation

• Sampling rate (resolution)

• Quantization level

Sampling Quantization

13

Different Sampling Rates

14

Different Quantization Levels

15

Image Processing (IP)

Input Image Output Image

Image Processing

FilteringSmoothingThinning

ExpendingShrinking

Compressing…

16

Fundementals• Neigborhood

• Histogram: gray levels vs number of pixels

4-Neighbors 8-Neighbors

17

IP Examples

Removal of noise with median filter

18

IP Examples (2)

Improvement of contrast by histogram modification

19

Symethricalinversion of the image

IP Examples (3)

20

IP Examples (4)

Smoothing

Original Uniform Gaussian

21

Binary Image ProcessingWHY?

better efficiency in acquiring, storage,

processing and transmission

TRESHOLDING

22

Different tresholds

23

Image Segmentation

Input ImageRegionsObjects

Segmentation

-Clasify pixels into groups having similar characteristics

-Two techniques: Region segmentation

Edge detection

24

Region Segmentation

• Histogram Based

25

Region Segmentation (2)

• Split & Merge

26

Edge Detection

• Find the curves on the image where rapid changes occur

27

Image Analysis

Input ImageRegions, objects

Measurements

Image Analysis

Measurements:-Size -Position-Orientation-Spatial relationship-Gray scale or color intensity-Velocity

28

Pattern Recognition (PR)

- Measurements- Stuctural descriptions

Class identifier

PatternRecognition

feature vectorset of information data

29

Approaches to PR

Statistical

Structural

Neural

30

• 3D VISION

• Dynamic Vision

31

Fingerprint Recognition

- most precise identification biometric

- has many applications- has the largest database

32

33

Fingerprint recognition system

Fingerprintsensor

Fingerprintsensor

Feature Extractor

Feature Extractor

Feature Matcher

ID

Enrollment

Identification

Templatedatabase

34

Fingerprint Representation

Local Ridge Characteristics

•Ridge Ending•Ridge Bifurcation•Enclosure•Ridge Dot

35

Fingerprint Representation (2)

Singular PointsCoreDelta

Major Central Pattern

Arch Loop Whorl

36

Image Processing & Analysis for Fingerprint Recognition

Pre-processing Minutiae Extraction

Post-processing

Input Image

•Binarization

•Noise Removal

•Smoothing

•Thinning

•Ridge break removal

•Bridge removal

•Spike removal

Processed Image

+Minutiae

Description

Outputs

37

Pre-Processing• Binarization

search through array pixel by pixel;

if current byte colour value is above threshold then

change value to white;

else

change colour to black

• Noise Removal if the pixel is white and all immediate surrounding pixels are

black

then

change pixel to black;

else if the pixel is black and all immediate surrounding pixels are white then

change pixel to white;

38

Pre-Processing(2)

• Smoothingfor each pixel do

add the values of surrounding pixels;

divide by the number of surrounding pixels (usually 8 unless

at the edge);

assign current pixel the calculated result;

• Thinning

39

Extractionif pixel has exactly one neighbour then

pixel is a ridge ending;calculate x value of pixel;calculate y value of pixel;

calculate angle of ridge ending;write data to database;

else if pixel has exactly three neighbours thenpixel is a bifurcation;

calculate x value of pixel;calculate y value of pixel;

calculate angle of bifurcation;write data to database;

40

Post-Processing• Ridge break removalIf A and B are facing each other and are less then a minimum

distance (D) then

A and B are false minutia points;

remove A and B from ridge end point set;

update database;

Bridge removal

• Spike removalFor each ridge ending point (A)

For each bifurcation point (B)

If A and B are less than minimum distance (D)apart and they are connected then

A and B are false minutia points;

remove A and B from their sets;

41

Matching

42

References[1] MACHINE VISION

Jain-Kasturi-Schunck[2] EE 701 Robot Vision Lecture Notes

A. Aydin Alatan – METU[3] Tutorial on Machine Vision

Petrakis[4] Multimodel User Interfaces

Anil Jain[5]  Automatic Fingerprint Recognition System

Tony Walsh

43

THANK YOU!