machine vision - alpen-adria-universität klagenfurt vision 1.pdf · 10/8/2011 2 what's our goal?...

10/8/2011 1

Machine Vision

Transportation Informatics Group

University of Klagenfurt

Transportation Informatics Group, ALPEN-ADRIA University of Klagenfurt

Dr. Ing- Alireza Fasih, WS 2011

Address: L4.2.03, Lakeside Park, B04

10/8/2011 2

What's our Goal?

Image Processing Fundamental and Basic

Machine Vision Techniques

Programming Methods and Implementation in

Matlab and OpenCV

Application of smart sensors and laser scanners

in ADAS (Advance Driver Assistance Systems)

Basics about Embedded Systems and FPGA-

Based Image Processing


10/8/2011 3

Assessment

50% Homework

50% Final Project

Attending on Class


Digital Image Processing

Introduction to Machine Vision

Processing of Images which are Digital in

nature by Digital Computer

4


Why do we need Image Processing?

The main motivations are:

Enhancement of Images and Improvement of pictorial

information for human perception

Autonomous Machine Application

Efficient Storage and Transmission

5


Human Perception

Employ method capable of enhancing digital images

Typical applications:

Noise filtering

Content enhancement

Contrast Enhancement

Deblurring

Remote sensing

6


Noise Filtering

7


Noisy Image Filtered Image

Contrast Enhancement

8


Low Contrast Image Enhanced Contrast

Deblurring

9


Motion blurred image Deblurred Image

Image Ref: http://yuwing.kaist.ac.kr/

http://yuwing.kaist.ac.kr/

Aerial Images

10


Tehran, Iran

Satellite Image Map

Population: 14,000,000

Medical Image Processing

11


Snapshot from Neurosurgery Tool showing 3-D

segmentation of tumor and ventricle

Image Ref:

http://ub2020.buffalo.edu/ict/faculty/profile.php?fid=294&sid=25

Vessels Inspection before Angiography

http://ub2020.buffalo.edu/ict/faculty/profile.php?fid=294&sid=25

Automated Inspection

12


Boundary and Surface Inspection

13


Object Detection and Recognition

14


Surveillance Systems

15


Cinema & Entertainment

16


Machine Vision

17


Image

Acquisition

Pre-

Processing

Feature

Extraction

&

Analysis

AI

Decision

Unit

Digital Camera Processing Unit in the Computer Display or Actuator

Are you ready ?

18


19

What is an Image ?

A digital representation of a real-world scene.

Composed of discrete elements, generally called picture

element (Pixle).

Pixels are parameterized by

Position (x,y)

Intensity

Time


Image Intensity and human eye sensitivity


In this image patch A and Path B have the same Pixels intensity!

How to represent the color from reality into the

digital image?

We should know about depth color!

21


22

True Color

A real or True-Color image contains some accurate

representation of the original image obtained at several

or a broad range of wavelengths


Qazvin-kolah ferangi

23

Gray Scale Images

A Gray Scale image contains only intensity information

8 bit : 0~255 level

Is it sufficeint to have an 8 bit depth for gray images?


Tehran 1930

24

False Color


25

Variety of Images Format


Binary Representation

Gray Scale

True Color False Color

Color Depth

26


1 bit (2 colors) 2 bit (4 colors) 4 bit (16 colors)

8 bit (256 colors) 24 bit (16,777,216 colors)

27

Color space


http://raph.com/3dartists/artgallery/artistPage?aid=319

28

RGB, HSV and HSL Color Model


The RGB color model is an additive color model in which red, green,

and blue light are added together in various ways to reproduce a

broad array of colors.

29

RGB Color Space

Red Layer Green Layer Blue Layer

ColorSeparate


http://raph.com/3dartists/artgallery/artistPage?aid=319

30

RGB to Gray Scale


Gray = 0.2125 * Red + 0.7154 * Green + 0.0721 * Blue

Minimundus, Klagenfurt- Austria

0.2125 + 0.7154 + 0.0721 = 1

31

Exercise-1

Write a simple program in Matlab to convert a color

image to the gray scale image. Dont use built-in

function.


for x

for y

{

R = getRed (img, x,y);

G = getRed (img, x,y);

B = getRed (img, x,y);

.

gray = 0.3*R + 0.59*G + 0.11*B;

}

32

Comparison of RGB, HSL and HSV


RGB Color Space HSL Color Space HSV Color Space

33

Pseudo Code (RGB to HSV)


function RGBtoHSV(rgb)

{

r = getWord(rgb, 0);

g = getWord(rgb, 1);

b = getWord(rgb, 2);

var_Min = minimum3(r, g, b);

var_Max = maximum3(r, g, b);

del_Max = var_Max - var_Min; // Delta RGB

Value

v = var_Max;

if (del_Max == 0)

{

h = 0;

s = 0;

}

else

{

del_R = (((var_Max - r) / 6) + (del_Max / 2)) / del_Max;

del_G = (((var_Max - g) / 6) + (del_Max / 2)) / del_Max;

del_B = (((var_Max - b) / 6) + (del_Max / 2)) / del_Max;

s = del_Max / var_Max;

// S = {V min(R,G,B)} / V

if (r == var_Max)

h = del_B - del_G;

else if (g == var_Max)

h = (1 / 3) + del_R - del_B;

else if (b == var_Max)

h = (2 / 3) + del_G - del_R;

if (h < 0)

h += 1;

else if (h > 1)

h -= 1;

}

return (h @ " " @ s @ " " @ v);

}

34

Matlab Guide-3 (RGB to HSV)


RGB to HSV in Matlab

>> RGB = imread(image1.bmp);

[H S V] = rgb2hsv(RGB);

subplot(2,2,1), imshow(H)

subplot(2,2,2), imshow(S)

subplot(2,2,3), imshow(V)

subplot(2,2,4), imshow(RGB)

35

What is Image Processing ?

Digital Image processing is the method and technology that allows

scientists to manipulate images in order to bring out features and

properties that had been previously difficult or impossible to

distinguish.


36

What is Computer Vision ?

Computer vision is the science and technology of

machines that see. By this technology we can obtain the

information from the image.

Application of Machine Vision:

Medical Image Processing

Autonomous Vehicles

Automation in Factories

Surveillance and Security

Movie Special Effects (Augmented Reality, Video Post

Processing, )

Space and Astronomy


37

Matlab Guide-1 (Image Info)

k=imfinfo('autobahn_gray.jpg');


38

Matlab Guide-2

I = imread ('autobahn_gray.jpg'); % load the image into array

imshow (I); % display image


39

Matlab Guide-3

I = imread ('autobahn_gray.jpg'); % load the image into array

I2 = I (110:120,130:170); % crop image

imshow (I2); % display image


130 170

110

170

40

Gray Level Threshold


for (int i=0; i

41

Threshold Problems


If Pixle[i,j] >= thr1 then Pixle[i,j] := 255 else Pixle[i,j] := 0;

thr1 := 87;

42

Local Thresholding Method


Mean value

Max Value

Min Value

If Pixle[i,j] >= thr1 then Pixle[i,j] := 255 else Pixle[i,j] := 0;

thr1 := mean k (Max-mean);

43

Finding the ball by color threshold


R

G

B

44

Image Histogram


1. What is an Image Histogram ?

2. Whats a good Histogram ?

3. How to Interpret a Histogram ?

Effect of Rescaling Image Histogram

Effect of Changing the Contrast, Brightness on Histogram

4. Histogram Equalization.

45

What is an Image Histogram ?


72

15 4 9

12X Zoom

It plots the number of pixels for each tonal value.

By looking at the histogram for a specific image a viewer will be able to judge

the entire tonal distribution at a glance.

46

Whats a good Histogram ?


Histograms are Image dependents

There is no such thing as a good or right

histogram

47

RGB Histogram


Mathematica 7

48

Effect of Rescaling Image Histogram


49

Effect of Rotation on Image Histogram


50

How to Interpret a Histogram ?


Shift Right

Shift Left

Shrink

Scale

Hi Brightness

Low Brightness

Low Contrast

High Contrast

Original Image

51

Histogram in Matlab


>> I = imread('pout.tif');

>> [counts,x] = imhist(I);

>> imhist(I);

Gamma Correction and Image Adjustment

52


Gamma Correction is a Function that allows to adjust level of lightness or darkness

Of Image.

G(x) = x (1 / Gamma_Value)

Gamma Curves

Color Gamma Correction

53


R

G

B

54

LUT-Based Adjustment Function


LUT

55

LUT-Based Adjustment Function


Gamma Correction in Matlab

56


[X,map] = imread('forest.tif');

I = ind2gray(X,map);

J = imadjust(I,[ ],[ ],0.5);

imshow(I) figure, imshow(J)

57

Histogram Equalization


>> I = imread('tire.tif');

J = histeq(I);

imshow(I)

figure, imshow(J)

58

Histogram Equalization


Consider a discrete grayscale image {x} and let ni be the number of occurrences of gray level i. The probability of an

occurrence of a pixel of level i in the image is

L being the total number of gray levels in the image, n being the total number of pixels in the image, and px being in fact

the image's histogram, normalized to [0,1]. Let us also define the cumulative distribution function corresponding to px as

which is also the image's accumulated normalized histogram. We would like to create a transformation of the form y = T(x)

to produce a new image {y}, such that its CDF will be linearized across the value range, i.e.

for some constant K. The properties of the CDF allow us to perform such a transform it is defined as :

Notice that the T maps the levels into the range [0,1]. In order to map the values back into their original range, the following

simple transformation needs to be applied on the result

59

AppendixAppendix


60

Image Quality !


61

Fundamental parameters of Imaging System


62

CCD

A Charge-Coupled Device (CCD) is an analog

shift register that enables the transportation of

analog signals (electric charges) through

successive stages (capacitors), controlled by a

clock signal.


63

CCD Properties

CCD Dimension

There are several standard CCD sensor sizes:1/4", 1/3", 1/2", 2/3" and 1".

CCD Cell Dimension

http://www.itcnewsletter.com/2004/2004-10.htm


7 um

7 um

http://www.itcnewsletter.com/2004/2004-10.htmhttp://www.itcnewsletter.com/2004/2004-10.htmhttp://www.itcnewsletter.com/2004/2004-10.htmhttp://www.itcnewsletter.com/2004/2004-10.htmhttp://www.itcnewsletter.com/2004/2004-10.htmhttp://www.itcnewsletter.com/2004/2004-10.htmhttp://www.itcnewsletter.com/2004/2004-10.htm

64

CCD Exposure Time

In photography, shutter speed is a common term used to

discuss exposure time, the effective length of time a

shutter is open; the total exposure is proportional to this

exposure time, or duration of light reaching the film or

image sensor.


The agreed standards for shutter speeds are:

1/1000 s

1/500 s

1/250 s

1/125 s

1/60 s

1/30 s

1/15 s

1/8 s

1/4 s

1/2 s

1 s

65

CCD Exposure Time


A picture that captured with 30 sec

Exposure Time

Sparklers moved in a circular motion

with a exposure time of 4 seconds

http://upload.wikimedia.org/wikipedia/commons/f/f3/M3_at_Night_1.jpghttp://upload.wikimedia.org/wikipedia/commons/7/74/Sparklers_with_a_slow_shutter_speed.JPG

66

CCD Exposure Time


67

Field/Angle of View

What makes a lens wide angle or telephoto?

The relationship between the focal length of the

lens and the size of the sensor array determines

the field of view of the camera. If the focal length

is smaller, the field of view is wider and vice

versa.

http://www.itcnewsletter.com/2004/2004-10.htm


http://www.itcnewsletter.com/2004/2004-10.htmhttp://www.itcnewsletter.com/2004/2004-10.htmhttp://www.itcnewsletter.com/2004/2004-10.htm

68

Field of View


69

Field of View


Focal length, (5mm)

CCD Size

(ex: )

Angle of View

Angle of View = 2 * ArcTan( CCD / 2 * FOL )

CCD/2

FOL

70

Light and Illumination


http://www.ak3d.de/

71

Illumination Examples


72



73



74



http://www.washington.edu/newsroom/news/images/David.jpg

75

Machine Vision

Thank you for your attention


machine vision - alpen-adria-universität klagenfurt vision 1.pdf · 10/8/2011 2 what's our goal?...

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