Digital Image Processing Lecture 1: Introduction February 21, 2005 Prof. Charlene Tsai...

Digital Image Processing Lecture 1: Introduction

February 21, 2005

Digital Image Processing Lecture 1: Introduction

February 21, 2005

Prof. Charlene [email protected]

Prof. Charlene [email protected]

http://www.cs.ccu.edu.tw/~tsaic/teaching/spring2005_undgrad/main.html

Digital Image Procession Lecture 1 2

Why do we need digital image processing?Why do we need digital image processing?

Image is better than any other information form for human being to perceive.

Humans are primarily visual creatures – above 90% of the information about the world (a picture is better than a thousand words)

However, vision is not intuitive for machines projection of 3D world to 2D images =>

loss of information interpretation of dynamic scenes, such as

a moving camera and moving objects

Image is better than any other information form for human being to perceive.

Humans are primarily visual creatures – above 90% of the information about the world (a picture is better than a thousand words)

However, vision is not intuitive for machines projection of 3D world to 2D images =>

loss of information interpretation of dynamic scenes, such as

a moving camera and moving objects


What is digital image processing?What is digital image processing?

Image understanding, image analysis, and computer vision aim to imitate the process of human vision electronically Image acquisition Preprocessing Segmentation Representation and description Recognition and interpretation

Image understanding, image analysis, and computer vision aim to imitate the process of human vision electronically Image acquisition Preprocessing Segmentation Representation and description Recognition and interpretation


General proceduresGeneral procedures

Goal: to obtain similar effect provided by biological systems

Two-level approaches Low level image processing. Very little

knowledge about the content or semantics of images

High level image understanding. Imitating human cognition and ability to infer information contained in the image.

Goal: to obtain similar effect provided by biological systems

Two-level approaches Low level image processing. Very little

knowledge about the content or semantics of images

High level image understanding. Imitating human cognition and ability to infer information contained in the image.


Low level image processingLow level image processing

Very little knowledge about the content of the images.

Data are the original images, represented as matrices of intensity values, i.e. sampling of a continuous field using a discrete grid.

Focus of this course.

Very little knowledge about the content of the images.

Data are the original images, represented as matrices of intensity values, i.e. sampling of a continuous field using a discrete grid.

Focus of this course.



Origin (Ox,Oy)

Spacing (Sy)

Spacing (Sx)

Pixel Value

Pixel Region

3x3 neighborhood



Image compression Noise reduction Edge extraction Contrast enhancement Segmentation Thresholding Morphology Image restoration


























ErosionDilation






High level image understandingHigh level image understanding

To imitate human cognition according to the information contained in the image.

Data represent knowledge about the image content, and are often in symbolic form.

Data representation is specific to the high-level goal.

To imitate human cognition according to the information contained in the image.

Data represent knowledge about the image content, and are often in symbolic form.

Data representation is specific to the high-level goal.


High level image understandingHigh level image understanding

Landmarks(bifurcation/crossover)Traces

(vessel centerlines)

What are the high-level components? What tasks can be achieved?

What are the high-level components? What tasks can be achieved?


ApplicationsApplications

Medicine Defense Meteorology Environmental science Manufacture Surveillance Crime investigation

Medicine Defense Meteorology Environmental science Manufacture Surveillance Crime investigation


Applications: MedicineApplications: Medicine

CT

(computed Tomography)

PET

(Positron Emission Tomography

PET/CT


Applications: SurveillanceApplications: Surveillance

Positioning pixel target in Aerial images

Photograph A Photograph B


Applications: MeteorologyApplications: Meteorology


Applications: Environmental ScienceApplications: Environmental Science


Applications: ManufactureApplications: Manufacture


Application: SurveillanceApplication: Surveillance


Applications: Crime InvestigationApplications: Crime Investigation

Fingerprint enhancement


What are the difficulties?What are the difficulties?

Poor understanding of the human vision system Poor understanding of the human vision system

Do you see a young or an old lady?



Human vision system tends to group related regions together, not odd mixture of the two alternatives.

Attending to different regions or contours initiate a change of perception

This illustrates once more that vision is an active process that attempts to make sense of incoming information.

Human vision system tends to group related regions together, not odd mixture of the two alternatives.

Attending to different regions or contours initiate a change of perception

This illustrates once more that vision is an active process that attempts to make sense of incoming information.



The interpretation is based heavily on prior knowledge.

The interpretation is based heavily on prior knowledge.


Class Format – Efficiency of LearningClass Format – Efficiency of Learning

What we read 10% What we hear 20% What we see 30% What we hear + see 50% What we say ourselves 70% What we do ourselves 90%

What we read 10% What we hear 20% What we see 30% What we hear + see 50% What we say ourselves 70% What we do ourselves 90%


Class Format – Efficiency of LearningClass Format – Efficiency of Learning

This leads to in-class discussion and quizzes.

50-minute lecture Remaining for group discussion & in-class

quiz

This leads to in-class discussion and quizzes.

50-minute lecture Remaining for group discussion & in-class

quiz


Course requirementsCourse requirements

In-class quizzes 10% 6 Homework assignments 30% Final project 20% Midterm exam 20% Final exam 20%

Peer learning is encouraged BUT, NO PLAGIARISM!!!

(20% deduction if caught)

In-class quizzes 10% 6 Homework assignments 30% Final project 20% Midterm exam 20% Final exam 20%

Peer learning is encouraged BUT, NO PLAGIARISM!!!

(20% deduction if caught)


Textbooks and Programming ToolTextbooks and Programming Tool

Prescribed: Alasdair McAndrew: Introduction to Digital

Image Processing with Matlab, 2004.

(We should cover major sections of the book) Other references:

Rafael C. Gonzalez, Richard E. Woods: Digital Image Processing. Prentice Hall; 2nd edition, 2002

Programming: Matlab with Image Processing Toolbox

Prescribed: Alasdair McAndrew: Introduction to Digital

Image Processing with Matlab, 2004.

(We should cover major sections of the book) Other references:

Rafael C. Gonzalez, Richard E. Woods: Digital Image Processing. Prentice Hall; 2nd edition, 2002

Programming: Matlab with Image Processing Toolbox


Example: Detection of ozone layer hole Example: Detection of ozone layer hole

Over the Antarctic, normal value around 300 DU


Looking ahead: lecture2Looking ahead: lecture2

Image types File format Matlab programming.

Image types File format Matlab programming.

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Digital Image Processing Lecture 1: Introduction February 21, 2005 Prof. Charlene Tsai...

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