UNESCO module: Introduction to Computer Vision and Image Processing Department of Pattern...

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

[email protected]

Outline of the presentation

This presentation summarizes the content and organization of lectures in module Image Processing and Computer Vision.

Objectives,

Prerequisite

and Content

Brief

Introduction

to Lectures

Discussion

and

Conclusion

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

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-to- one mapping.

Vision:=Geometry+Measurement+Interpretation

What’s Computer Vision ?

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 1: Image presentation

Chapter 2: Statistic operations

Chapter 3: Spatial operations and transformations

Chapter 4: Segmentation and edge detection

Chapter 5: Morphological and other area area operations

Chapter 6: Finding basic shapes

Chapter 7: Reasoning, facts and inference

Chapter 8: Pattern recognition and training

Chapter 9: Frequency domain

Chapter 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

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 = Pixel1 + (1 - )Pixel2

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".


Basic operation: Templates and Convolution

1

0

1

0

),(),(),(n

i

m

j

jYiXIjiTYXIT

I(x,y) - image

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


3.3 Other window operations

– Median filtering

– k-closest averaging

– Interest point

– Moravec operator

– Correlation


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’)

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

Morphological and other area operations (1)

Morphological defined

- Morphology means the form and structure of an object, it’s related to shape

- Digital morphology is a way to describe or analyze the shape of a digital object.

Morphological operations (2)5.2 Basic morphological operations

– Binary dilation

– Binary erosion

5.3 Opening and closing operators

Example: The use of opening: (a) An image having many connected objects, (b) Objects can be isolated by opening using the simple structuring element, (c) An image that has been subjected to noise, (d) The noisy image after opening showing that the black noise pixels have been removed.

Finding basic shapes (1)

Previous chapters dealt with purely statistical and spatial operations.

Techniques:

- looking at and processing whole images

- uses information generated by the algorithms in the previous chapter.

- finding basic two-dimensional shapes or elements of shapes by putting edges together to form lines that are likely represent real

edges.

Finding basic shapes (2)

6.2 Hough transforms

6.3 Bresenham’s algorithms

6.4 Using interest point

6.5 Labeling lines and regions

r

(x,y)

One of many possiblelines through (x,y),e.g. y=ax+b

Shotest distance fromorigin to line defines theline in term of r and

x

y

Four cicles coincide hereonly

Reasoning, facts and inference (1)

- Moving from the standard IP approach to CV to make statement about the geometry of objects and allocate labels to them.

- Enhancing by making reasoned statements, by codifying facts, and making judgments based on past experience.

- Introducing to some concepts in logical reasoning that relate specifically to CV.

- Introducing training aspects of reasoning systems. The reasoning is the highest level of CV processing.


7.1 Facts and Rules

- Constructing a set of facts

- Constructing a rule base.

7.2 Strategic learning

Example: A pedestal training and a pedestal description


7.3 Networks and spatial descriptors

Example: Elementary network of spatial relationship

– L is all element of

– C is a subset of

– P with the visual property or

– R at this position with respect to

7.4 Rule orders

ShyniTop

Above

Table

Legs

Leg

P

RR

L

L

C

Pattern recognition and training (1)

Previous chapter presented some methods used in reasoning about facts from image: edges or textures, colours or surface positions.

Some problems are better described as problems of determining a high level fact from a pattern of some kind. The term "pattern" has a wide range of meanings,

We are particularly interested in sets of value that describe things, normally where the set of values is of a known size. This is different to looking at a scene of a flat surfaced object where we do not know how many corners there are, how many edges or how many surfaces.


8.1 General problem

Make a seriesofmeasurementsto give a setof values

Determinewhich objectthis set ofmeasurementssuggests is inthe image

Image

x1

xn

MAXMUM

O1

On

object =...

Decisionfunctiongenerator

Decisionmakingprocess

Pattern vectorScore vector(highest object scoreis choosen)


8.2 Approaches to the decision making process

8.3 Decision functions

8.4 Determining decision functions

8.5 Non-linear decision functions

8.6 Using cluster means

8.7 Supervised and unsupervised learning

- Statistical: Bayesian likelihood supervised learning

- Syntactical learning.


8.4 Determining decision function:

- Searching for islands of simplicity,

- Distance or similarity measure,

AG r o u pA

B

C

The frequency domain (1)

Most signal processing is done in a mathematical space known as the frequency domain.

In order to represent data in the frequency domain, some transform is necessary.

The signal frequency of an image refers to the rate at which the pixel intensities change.

- The high frequencies are concentrated around the axes dividing the image into quadrants.

- The corners have lower frequencies. Low spatial

frequencies are noted by large areas of nearly constant values.

The frequency domain (2)

Fourier Transform of a spot: (a) original image; (b) Fourier Transform.

9.1 The Harley transform

9.2 The Fourier transform

Compression of images: problem of storing them in a form that systems need to get the following benefits:

- speedily operation (both compression and unpacking),

- significant reduction in required memory, no significant loss of quality in the image,

- format of output suitable for transfer or storage.

Each of this depends on the user and the application.

Image Compression (1)

A typical data compression system.


Run Length Encoding Huffman Coding Modified Huffman Coding Modified READ Arithmetic Coding LZW JPEG Other state-of-the-art image compression methods:

Fractal and Wavelet compression.


Improvement Focus to recovering from 2D projection to create a object

model:

- Coordinate system and camera calibration

- Curve and surfaces

- Dynamic vision

Object recognition

Conclusion

UNESCO module: Introduction to Computer Vision and Image Processing Department of Pattern...

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