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Machine Vision pdf
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1Machine VisionSUMMER ACADEMY 2004
by
Maya akmak
Middle East Technical University
Electrical and Electronics Engineering
2VISION
the most powerful sense
3OUTLINE
1. Properties of Machine Vision Systems
Methods in Machine Vision
2. Example: Fingerprint Recognition
System
4Machine Vision System
creates a model of the real world from images
recovers useful information about a scene from its two dimensional
projections
5A typical control system:
Scene Image Description
Application
feedback
Imaging
device
MACHINE
VISION
Illumination
6Components of a M.V. system
HARDWARE
SOFTWARE Optics (lenses, lighting)
Cameras
Interface (frame grabber)
Computer
7Components of a M.V. system
HARDWARE
SOFTWARE
-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
8Application Fields
Medical imaging
Industrial automation
Robotics
Radar Imaging
Forensics
Remote Sensing
Cartography
Character recognition
9Machine Vision Stages
Image 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 with
similar 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 2DIt`s a squareIt has one colorIt 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
Filtering
Smoothing
Thinning
Expending
Shrinking
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
Symethrical
inversion 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
Pattern
Recognition
feature vector
set 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
Fingerprint
sensor
Fingerprint
sensor
Feature Extractor
Feature Extractor
Feature Matcher
ID
Enrollment
Identification
Template
database
34
Fingerprint Representation
Local Ridge Characteristics
Ridge EndingRidge BifurcationEnclosureRidge Dot
35
Fingerprint Representation (2)
Singular Points
Core
Delta
Major Central Pattern
Arch Loop Whorl
36
Image Processing & Analysis for
Fingerprint Recognition
Pre-processing Minutiae
ExtractionPost-processing
Input Image
Binarization
Noise Removal
Smoothing
Thinning
Ridge break removal
Bridge removal
Spike removal
Processed
Image
+
Minutiae
Description
Outputs
37
Pre-Processing
Binarizationsearch 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
Extraction
if 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 then
pixel 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!