1. Machine Vision (Introduction)
TECHNICAL UNIVERSITY OF CRETE DEPARTMENT OF ELECTRONIC AND COMPUTER ENGINEERING
MACHINE VISION
Euripides G.M. Petrakis
Michalis Zervakis
Chania 2015
E.G.M. Petrakis
Machine Vision (Introduction)

2. Machine Vision (Introduction)
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 (Introduction)
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)

4. Machine Vision (Introduction)
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)

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

6. Machine Vision Stages Analog to digital conversion
Image Acquisition
(by cameras, scanners etc)
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 Processing
Image Enhancement
Image Restoration
Image Segmentation
Image Analysis
(Binary Image Processing)
Model Matching
Pattern Recognition
E.G.M. Petrakis
Machine Vision (Introduction)

7. Machine Vision (Introduction)
Image Processing
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)

8. Machine Vision (Introduction)
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)

9. Machine Vision (Introduction)
Image Segmentation
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)

10. Machine Vision (Introduction)
Image Analysis
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)

11. Machine Vision (Introduction)
Pattern Recognition
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)

12. 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)

13. 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)

14. Structure of PR Systems
input
Sensor
Processing
Measurements
Classification
class
E.G.M. Petrakis
Machine Vision (Introduction)

15. Example of Statistical PR
Two classes:
W1 Basketball players
W2 jockeys
Description: X = (X1, X2) = (height, weight) X1 W1
. … ..
… …
.. …… + W2
. . .
.. ..
D(X) = AX1 + BX2 + C = 0
Decision function - X2
E.G.M. Petrakis
Machine Vision (Introduction)

16. 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)

17. Machine Vision (Introduction)
Primitives
G1,L(G1) : submedian Grammar
G2,L(G2) : telocentric Grammar
E.G.M. Petrakis
Machine Vision (Introduction)

18. Machine Vision (Introduction)
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)

19. 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)

20. 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)

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. Machine Vision (Introduction)
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. Machine Vision (Introduction)
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. Machine Vision (Introduction)
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. Machine Vision (Introduction)
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. Machine Vision (Introduction)
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. Machine Vision (Introduction)
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. Machine Vision (Introduction)
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. Machine Vision (Introduction)
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. Machine Vision (Introduction)
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. Machine Vision (Introduction)
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.
Depth Estimation, 3D vision
Video, MPEG-2, MPEG-4, video segmentation
E.G.M. Petrakis
Machine Vision (Introduction)

37. Machine Vision (Introduction)
Bibliography
“Ψηφιακή Επεξεργασία Εικόνας” Rafale Gonzalez, Richard E. Woods, Εκδόσεις Τζιόλα
“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.
Web courses (
E.G.M. Petrakis
Machine Vision (Introduction)

38. Machine Vision (Introduction)
Grading Scheme
Final Exam (F): 60%, min 5 
Assignments (Α): 40% 
Two assignments 
Obligatory
E.G.M. Petrakis
Machine Vision (Introduction)