UNESCO Module: Introduction To Computer Vision And Image Processing

UNESCO module:
Introduction to Computer Vision
and Image Processing
Department of Pattern Recognition and Knowledge Engineering
Institute of Information Technology
Hanoi, Vietnam
Represented by LUONG CHI MAI



Outline of the presentation
Objectives,

Brief

Discussion

Prerequisite

Introduction

and

and Content

to Lectures

Conclusion

This presentation summarizes the content and
organization of lectures in module Image

Processing and Computer Vision.


Objectives
The course provides fundamental techniques
of Image Processing and Computer Vision as
well issues in practical use.


Prerequisite
✦

A basic background in mathematics and
computers is necessary,

✦

Knowledge of the C programming language
will enhance the usefulness of the algorithms
used in programming,

✦

Understanding of signal and system theory is
helpful in mastering transforms and
compression.


Target audience
✦

✦

Engineers, programmers, graphics
specialists, multimedia developers, and
imaging professionals will all appreciate
Computer Vision and Image Processing's
solid introduction
Anyone who uses computer imaging.


What’s the Image Processing?
Image Processing (IP) is used for two
somewhat different purposes:
a. improving the visual appearance of images
to a human view, and
b. preparing images for measurement of the
features and structures present.
Image Processing:= Image → Image
Transformation
➊


What’s Computer Vision ?
❷

Computer Vision (CV): to create a model of
the real word from images. A CV system
recovers useful information about a scene
from its two-dimensional projections. This

recover requires the inversion of a many-toone mapping.

Vision:=Geometry+Measurement+Interpretatio
n


Relationships between subjects (1)
Many fields are related to Computer Vision
Image Processing (IP): techniques usually transform images into
other images, (enhancement, correcting blurred, out-of-focus,
compression → better 2D projection image for CV).The task of
information recovery is left to human user.
Computer Graphics (CG): generates images from geometric
primitives such as lines, circles, and free-form surfaces. CV is
the inverse problem: estimating the geometric primitives and
other features from images.
CG: Synthesis of images.
CV: Analysis of images.


Relationships between subjects (2)
Pattern Recognition (PR): classifies numerical and symbolic
data. Techniques: statistical and syntactical. PR techniques play
an important role in CV for recognizing objects. Object
recognition in CV usually requires many other techniques.
Artificial Intelligence (AI): is concerned with designing systems
that are intelligent and with studying computational aspects of
intelligent. CV is often considered as a sub-field of AI
Psyochophysics: along with cognitive science, studies human
vision for a long time. Many techniques in CV are related to

what is known abut human vision.


Content of the course
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter

1: Image presentation
2: Statistic operations
3: Spatial operations and transformations
4: Segmentation and edge detection
5: Morphological and other area area operations
6: Finding basic shapes
7: Reasoning, facts and inference
8: Pattern recognition and training
9: Frequency domain
10: Image compression


About the Chapters
Chapters
✦

1, 2, 3, 4, 5, 9, 10 related to Image
Processing: well known techniques to
enhancement images.

✦

6, 7, 8 related to Computer Visions


Image presentation (1)
1.1 Image capture,
representation, and
storage:
digital image, DPI,
pixel...
Example: Variouse
quantizing level: (a)
6 bits; (b) 4 bits; (c)
2 bits; (d) 1 bit.


Image presentation (2)
✦

1.2 Color representation:
Color systems: RGB, CMY/CMYK, HSI, YCbCr


Content of the course

Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter

1: Image presentation
2: Statistic operations
3: Spatial operations and transformations
4: Segmentation and edge detection
5: Morphological and other area area operations
6: Finding basic shapes
7: Reasoning, facts and inference
8: Pattern recognition and training
9: Frequency domain
10: Image compression


Statistical operations (1)
The algorithms are independent
of the position of the pixels.
Basic operation: Histogram
transformation

2.1 Gray-level transformation

- Intensity transformation
- Look-up-table techniques
- Gamma correction function
- Contrast streching End-in-search

2.2 Histogram equalization


Statistical operations (2)
2.3 Multi-image operations
–Background substraction
–Multi-image averaging

New-Pixel = α Pixel1 + (1 - α )Pixel2


Content of the course
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter

1: Image presentation
2: Statistic operations

3: Spatial operations and transformations
4: Segmentation and edge detection
5: Morphological and other area area operations
6: Finding basic shapes
7: Reasoning, facts and inference
8: Pattern recognition and training
9: Frequency domain
10: Image compression


Spatial operations and
transformations (1)
Combining the techniques and operations that deal with
pixels and their neighbors (spatial operations).
- Spatial filters (normally removing noise by reference to
the neighboring pixel values),
- Weighted averaging of pixel areas (convolutions),
- Comparing areas on an image with known pixel area
shapes so as to find shapes in images (correlation).
- Edge detection and on detection of "interest point".


Spatial operations and
transformations (2)
Basic operation: Templates and Convolution
n −1 m −1

T ∗ I ( X , Y ) = ∑∑ T (i, j ) I ( X + i, Y + j )
i =0 j =0

I(x,y) - image
T(i,j) - template of the size n x m


Spatial operations and
transformations (3)
✦

3.3 Other window operations
– Median filtering
– k-closest averaging
– Interest point
– Moravec operator
– Correlation


Spatial operations and
transformations (4)
✦3.4

Two dimensional geometric transformations

Frequently it is useful to zoom in on a part of an
image, rotate, shift, skew or zoom out from an image.
If (x’,y’) - the new coordinates and (x, y) - original
coordinates
– Forward Transformation

(x’,y’) = f(x, y) for all (x, y) is created.
– Invest Transformation

I(x, y) = F(old image, x’, y’)


Content of the course
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter
Chapter

1: Image presentation
2: Statistic operations
3: Spatial operations and transformations
4: Segmentation and edge detection
5: Morphological and other area area operations
6: Finding basic shapes
7: Reasoning, facts and inference
8: Pattern recognition and training
9: Frequency domain
10: Image compression


Segmentation and edge detection (1)
Segmentation: basic requirement for the identification

and classification of objects in scene.
✦

Techniques: splitting an image up into segments (also call
regions or areas), each holds some property distinct from
their neighbor.
✦

✦

Approaches :

- identifying the edges (or lines) that run through an image
- identifying regions (or areas) within an image.
Region operations is the dual of edge operations. Ideally
edge and region operations should give the same
segmentation result, however, in practice the two rarely
correspond.


Segmentation and edge detection (2)
✦

4.1 Region operations
– Crudge edge detection
– Region merging
– Region spliting

✦

4.2 Basic edge detection


Segmentation and edge detection (3)
✦

4.3 First order derivative for edge detection
Hc = y_differ(x, y) = value(x, y) – value(x, y+1)
Hr = X_differ(x, y) = value(x, y) – value(x-1, y)

✦

4.3 Second-order edge detection

✦

4.4 Pyramid edge detection

✦

4.5 Crack edge detection

✦

4.6 Edge following