E.G.M. Petrakis Machine Vision (Introduction) 1

TECHNICAL UNIVERSITY OF CRETEDEPARTMENT OF ELECTRONIC AND COMPUTER

ENGINEERING

MACHINE VISIONEuripides G.M. Petrakis

Michalis Zervakis

http://www.intelligence.tuc/~petrakis

http://courses.ece.tuc.grChania 2010

E.G.M. Petrakis Machine Vision (Introduction) 2

Machine Vision

• The goal of Machine Vision is to create a model of the real world from images– A machine vision system recovers useful

information about a scene from its two dimensional projections

– The world is three dimensional– Two dimensional digitized images

E.G.M. Petrakis Machine Vision (Introduction) 3

Machine Vision (2)

• Knowledge about the objects (regions) in a scene and projection geometry is required.

• The information which is recovered differs depending on the application– Satellite, medical images etc.

• Processing takes place in stages:– Enhancement, segmentation, image analysis

and matching (pattern recognition).

Illumination

Scene2D

Digital ImageImage

Description

Image Acquisition

Machine Vision System

Feedback

The goal of a machine vision system is to compute a meaningful description of the scene (e.g., object)

E.G.M. Petrakis Machine Vision (Introduction) 5

Machine Vision Stages• Analog to digital

conversion• Remove noise/patterns,

improve contrast

• Find regions (objects) in the image

• Take measurements of objects/relationships

• Match the above description with similar description of known objects (models)

Image Acquisition(by cameras, scanners etc)

Model MatchingPattern Recognition

Image Analysis (Binary Image Processing)

Image Segmentation

Image Processing Image EnhancementImage Restoration

E.G.M. Petrakis Machine Vision (Introduction) 6

Image Processing

• Image transformation

– image enhancement (filtering, edge detection, surface detection, computation of depth).

– Image restoration (remove point/pattern degradation: there exist a mathematical expression of the type of degradation like e.g. Added multiplicative noise, sin/cos pattern degradation etc).

Image Processing

Input Image Output Image

E.G.M. Petrakis Machine Vision (Introduction) 7

Image Segmentation

• Classify pixels into groups (regions/objects of interest) sharing common characteristics.– Intensity/Color, texture, motion etc.

• Two types of techniques:– Region segmentation: find the pixels of a region.– Edge segmentation: find the pixels of its outline contour.

Image Segmentation

Input Image Regions/Objects

E.G.M. Petrakis Machine Vision (Introduction) 8

Image Analysis

• Take useful measurements from pixels, regions, spatial relationships, motion etc.– Grey scale / color intensity values;

– Size, distance;

– Velocity;

Image Analysis

Input ImageSegmented Image(regions, objects)

Measurements

E.G.M. Petrakis Machine Vision (Introduction) 9

Pattern Recognition

• Classify an image (region) into one of a number of known classes– Statistical pattern recognition (the measurements form vectors

which are classified into classes);– Structural pattern recognition (decompose the image into primitive

structures).

Model MatchingPattern Recognition

Image/regions •Measurements, or•Structural description

Class identifier

E.G.M. Petrakis Machine Vision (Introduction) 10

Digital Image Representation

• Image: 2D array of gray level or color values– Pixel: array element;

– Pixel value: arithmetic value of gray level or color intensity.

• Gray level image: f = f(x,y)

- 3D image f=f(x,y,z)

• Color image (multi-spectral)

f = {Rred(x,y), Ggreen(x,y), Bblue(x,y)}

E.G.M. Petrakis Machine Vision (Introduction) 11

What a computer “sees” is very different from what a human sees. A computer sees pixels (arithmetic values) while a human sees shapes, structures etc.

E.G.M. Petrakis Machine Vision (Introduction) 12

Relationships to other fields

• Image Processing (IP)

• Pattern Recognition (PR)

• Computer Graphics (CG)

• Artificial Intelligence (AI)

• Neural Networks (NN)

• Psychophysics

E.G.M. Petrakis Machine Vision (Introduction) 13

Image Processing (IP)

• IP transforms images to images – Image filtering, compression, restoration

• IP is applied at the early stages of machine vision.– IP is usually used to enhance particular

information and to suppress noise.

E.G.M. Petrakis Machine Vision (Introduction) 14

Pattern Recognition (PR)

• PR classifies numerical and symbolic data.– Statistical: classify feature vectors. – Structural: represent the composition of an

object in terms of primitives and parse this description.

• PR is usually used to classify objects but object recognition in machine vision usually requires many other techniques.

E.G.M. Petrakis Machine Vision (Introduction) 15

Statistical Pattern Recognition

• Pattern: the description of an an object– Feature vector – (size, roundness, color, texture)

• Pattern class: set of patterns with similar characteristics.

• Take measurements from a population of patterns.

• Classification: Map each pattern to a class.

E.G.M. Petrakis Machine Vision (Introduction) 16

Structure of PR Systems

input

Sensor

Measurements

Processing

Classification

class

E.G.M. Petrakis Machine Vision (Introduction) 17

Example of Statistical PR

• Two classes:I. W1 Basketball playersII. W2 jockeys

• Description: X = (X1, X2) = (height, weight)X1

X2

. . . . . .. .

. … ..… … .. ..

.. ……W2

W1

D(X) = AX1 + BX2 + C = 0 Decision function

-

+

E.G.M. Petrakis Machine Vision (Introduction) 18

Syntactic Pattern Recognition

• The structure is important • Identify primitives

– E.g., Shape primitives

• Break down an image (shape) into a sequence of such primitives.

• The way the primitives are related to each other to form a shape is unique.– Use a grammar/algorithm– Parse the shape

E.G.M. Petrakis Machine Vision (Introduction) 19

•Primitives

•G1,L(G1) : submedian Grammar

•G2,L(G2) : telocentric Grammar

E.G.M. Petrakis Machine Vision (Introduction) 20

•Each digit is represented by a waveform representing black/white, white/black transitions (scan the image from Left to right.

E.G.M. Petrakis Machine Vision (Introduction) 21

Computer Graphics (CG)

• Machine vision is the analysis of images while CG is the decomposition of images: – CG generates images from geometric primitives

(lines, circles, surfaces). – Machine vision is the inverse: estimate the

geometric primitives from an image.

• Visualization and virtual reality bring these two fields closer.

E.G.M. Petrakis Machine Vision (Introduction) 22

Artificial Intelligence (AI)

• Machine vision is considered to be sub-field of AI.• AI studies the computational aspects of

intelligence. • CV is used to analyze scenes and compute

symbolic representations from them.• AI: perception, cognition, action

– Perception translates signals to symbols;– Cognition manipulates symbols;– Action translates symbols to signals that effect the

world.

E.G.M. Petrakis Machine Vision (Introduction) 23

Psychophysics

• Psychophysics and cognitive science have studied human vision for a long time.

• Many techniques in machine vision are related to what is known about human vision.

E.G.M. Petrakis Machine Vision (Introduction) 24

Neural Networks (NN)

• NNs are being increasingly applied to solve many machine vision problems.

• NN techniques are usually applied to solve PR tasks.– Image recognition/classification.

• They have also applied to segmentation and other machine vision tasks.

E.G.M. Petrakis Machine Vision (Introduction) 25

Machine Vision Applications

• Robotics• Medicine• Remote Sensing• Cartography• Meteorology• Quality inspection• Reconnaissance

E.G.M. Petrakis Machine Vision (Introduction) 26

Robot Vision

• Machine vision can make a robot manipulator much more versatile.– Allow it to deal with variations in parts position and

orientation.

E.G.M. Petrakis Machine Vision (Introduction) 27

Remote Sensing

• Take images from high altitudes (from aircrafts, satellites).

• Find ships in the aerial image of the dock.– Find if new ships have

arrived.

– What kind of ships?

E.G.M. Petrakis Machine Vision (Introduction) 28

Remote Sensing (2)

• Analyze the image– Generate a description

– Match this descriptions with the descriptions of empty docs

• There are four ships – Marked by “+”

E.G.M. Petrakis Machine Vision (Introduction) 29

Medical Applications

• Assist a physician to reach a diagnosis.

• Construct 2D, 3D anatomy models of the human body.– CG geometric models.

• Analyze the image to extract useful features.

E.G.M. Petrakis Machine Vision (Introduction) 30

Machine Vision Systems

• There is no universal machine vision system– One system for each application

• Assumptions:– Good lighting;

– Low noise;

– 2D images

• Passive - Active environment– Changes in the environment call for different actions

(e.g., turn left, push the break etc).

E.G.M. Petrakis Machine Vision (Introduction) 31

Vision by Man and Machine

• What is the mechanism of human vision?– Can a machine do the same thing?– There are many studies;– Most are empirical.

• Humans and machines have different– Software– Hardware

E.G.M. Petrakis Machine Vision (Introduction) 32

Human “Hardware”

• Photoreceptors take measurements of light signals. – About 106 Photoreceptors.

• Retinal ganglion cells transmit electric and chemical signals to the brain– Complex 3D interconnections;

– What the neurons do? In what sequence?

– Algorithms?

• Heavy Parallelism.

E.G.M. Petrakis Machine Vision (Introduction) 33

Machine Vision Hardware

• PCs, workstations etc.

• Signals: 2D image arrays gray level/color values.

• Modules: low level processing, shape from texture, motion, contours etc.

• Simple interconnections.

• No parallelism.

E.G.M. Petrakis Machine Vision (Introduction) 34

Course Outline

• Introduction to machine vision, applications, Image formation, color, reflectance, depth, stereopsis.

• Basic image processing techniques (filtering, digitization, restoration), Fourier transform.

• Binary image processing and analysis, Distance transform, morphological operators.

E.G.M. Petrakis Machine Vision (Introduction) 35

Course Outline (2)

• Image segmentation (region segmentation, edge segmentation).

• Edge detection, edge enhancement and linking.  Thresholding, region growing, region merging/splitting.

• Relaxation labeling, Hough transform.• Image analysis, shape analysis. Polygonal

approximation, splines, skeletons. Shape features, multi-resolution representations.

E.G.M. Petrakis Machine Vision (Introduction) 36

Course Outline (3)

• Image representation, image - shape recognition and classification. Attributed relational graphs, semantic nets. 

• Image - shape matching (Fourier descriptors, moments, matching in scale space).

• Texture representation and recognition, statistical and structural methods.

• Motion, motion detection, optical flow.• Video

E.G.M. Petrakis Machine Vision (Introduction) 37

Bibliography

• “Machine Vision”, Ramesh Jain, Rangachar Kasturi, Brian G. Schunck, Mc Graw-Hill, 1995 (highly recommended!).

• "Image Processing, Analysis and Machine Vision", Milan Sonka, Vaclav Hlavac, Roger Boyle, PWS Publishing, Second Edition.

• "Machine Vision, Theory, Algorithms, Practicalities'', E. R. Davies, Academic Press, 1997.

E.G.M. Petrakis Machine Vision (Introduction) 38

• "Practical Computer Vision Using C'', J. R. Parker, John Wiley & Sons Inc., 1994.

• Selected articles from the literature.

• Lecture notes (http://www.intelligence.tuc/~petrakis)

• Webcourses (http://courses.ece.tuc.gr)

E.G.M. Petrakis Machine Vision (Introduction) 39

Grading Scheme

• Final Exam (F): 40%, min 5 

• Assignments (Α): 40% 

• Two assignments – Obligatory