TECHNICAL UNIVERSITY OF CRETE
DEPARTMENT OF ELECTRONIC AND COMPUTER
ENGINEERING
Euripides G.M. Petrakis
Michalis Zervakis http://www.intelligence.tuc/~petrakis http://courses.ece.tuc.gr
Chania 2010
E.G.M. Petrakis Machine Vision (Introduction) 1

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

Illumination
Scene
Image
Acquisition
2D
Digital Image
Machine
Vision System
Image
Description
Feedback
The goal of a machine vision system is to compute a meaningful description of the scene (e.g., object)

Image Acquisition
(by cameras, scanners etc)
Image Processing
Image Enhancement
Image Restoration
Image Segmentation
Image Analysis
(Binary Image Processing)
Model Matching
Pattern Recognition
E.G.M. Petrakis
• 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)
Machine Vision (Introduction) 5

Image Processing
Input Image Output Image
• 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).
E.G.M. Petrakis Machine Vision (Introduction) 6

Image Segmentation
Input Image Regions/Objects
• 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.
E.G.M. Petrakis Machine Vision (Introduction) 7

Image Analysis
Input Image
Segmented Image
(regions, objects)
Measurements
• Take useful measurements from pixels, regions, spatial relationships, motion etc.
– Grey scale / color intensity values;
– Size, distance;
– Velocity;
E.G.M. Petrakis Machine Vision (Introduction) 8

Model Matching
Pattern Recognition
Image/regions

•Measurements, or
•Structural description
Class identifier
• 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).
E.G.M. Petrakis Machine Vision (Introduction) 9

• 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 = {R red
(x,y), G green
(x,y), B blue
(x,y)}
E.G.M. Petrakis Machine Vision (Introduction) 10

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) 11

• Image Processing (IP)
• Pattern Recognition (PR)
• Computer Graphics (CG)
• Artificial Intelligence (AI)
• Neural Networks (NN)
• Psychophysics
E.G.M. Petrakis Machine Vision (Introduction) 12

• 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) 13

• 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) 14

•
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) 15

E.G.M. Petrakis
input
Sensor
Processing
Measurements
Classification class
Machine Vision (Introduction) 16

•
Two classes:
I.
W
1
Basketball players
II.
W
2 jockeys
• Description: X = (X
1
, X
2
) = (height, weight)
X
1 W
1
W
2
.. ..
. . .
. . .. .
+
.. ……
. … ..
… …
-
D(X) = AX
1
+ BX
2
+ C = 0
Decision function
X
2
E.G.M. Petrakis Machine Vision (Introduction) 17

• 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) 18

•Primitives
•G
1
,L(G
1
) : submedian Grammar
•G
2
,L(G
2
) : telocentric Grammar
E.G.M. Petrakis Machine Vision (Introduction) 19

•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) 20

• 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) 21

• 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) 22

• 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) 23

• 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) 24

• Robotics
• Medicine
• Remote Sensing
• Cartography
• Meteorology
• Quality inspection
• Reconnaissance
E.G.M. Petrakis Machine Vision (Introduction) 25

• 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) 26

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

E.G.M. Petrakis
• Analyze the image
– Generate a description
– Match this descriptions with the descriptions of empty docs
• There are four ships
– Marked by “+”
Machine Vision (Introduction) 28

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

• 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) 30

• 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) 31

• Photoreceptors take measurements of light signals.
– About 10 6 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) 32

• 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) 33

• 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) 34

• 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) 35

• 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) 36

• “ 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) 37

•
"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) 38

• Final Exam (F): 40%, min 5
• Assignments (Α): 40%
• Two assignments
– Obligatory
E.G.M. Petrakis Machine Vision (Introduction) 39