Teacher: Daw Lai Ye Myint

Teacher: Dr. May Cho Aye

Chapter 1 was rewritten completely.The main focus of the current treatment

is on examples of areas that use digital image processing.While far from exhaustive,

the examples shown will leave little doubt in the reader’s mind regarding

the breadth of application of digital image processing methodologies.

Chapter 2 is totally new also.The focus of the presentation in this chapter is on

how digital images are generated, and on the closely related concepts of


sampling, aliasing, Moiré patterns, and image zooming and shrinking.The new

material and the manner in which these two chapters were reorganized address

directly the first two findings in the market survey mentioned above.

Chapters 3 though 6 in the current edition cover the same concepts as Chapters

3 through 5 in the previous edition, but the scope is expanded and the presentation

is totally different. In the previous edition, Chapter 3 was devoted

exclusively to image transforms. One of the major changes in the book is that

image transforms are now introduced when they are needed.This allowed us to

begin discussion of image processing techniques much earlier than before, further

addressing the second finding of the market survey. Chapters 3 and 4 in the

current edition deal with image enhancement, as opposed to a single chapter

(Chapter 4) in the previous edition.The new organization of this material does

not imply that image enhancement is more important than other areas. Rather,

we used it as an avenue to introduce spatial methods for image processing

(Chapter 3), as well as the Fourier transform, the frequency domain, and image

filtering (Chapter 4). Our purpose for introducing these concepts in the context

of image enhancement (a subject particularly appealing to beginners) was to increase

the level of intuitiveness in the presentation, thus addressing partially

the third major finding in the marketing survey.This organization also gives instructors

flexibility in the amount of frequency-domain material they wish to


Chapter 5 also was rewritten completely in a more intuitive manner. The

coverage of this topic in earlier editions of the book was based on matrix theory.

Although unified and elegant, this type of presentation is difficult to follow,

particularly by undergraduates. The new presentation covers essentially the

same ground, but the discussion does not rely on matrix theory and is much

easier to understand, due in part to numerous new examples.The price paid for

this newly gained simplicity is the loss of a unified approach, in the sense that

in the earlier treatment a number of restoration results could be derived from

one basic formulation. On balance, however, we believe that readers (especially

beginners) will find the new treatment much more appealing and easier to follow.

Also, as indicated below, the old material is stored in the book Web site for

easy access by individuals preferring to follow a matrix-theory formulation.

Chapter 6 dealing with color image processing is new. Interest in this area has

increased significantly in the past few years as a result of growth in the use of

digital images for Internet applications. Our treatment of this topic represents

a significant expansion of the material from previous editions. Similarly Chapter

7, dealing with wavelets, is new. In addition to a number of signal processing

applications, interest in this area is motivated by the need for more

sophisticated methods for image compression, a topic that in turn is motivated

by a increase in the number of images transmitted over the Internet or stored

in Web servers. Chapter 8 dealing with image compression was updated to include

new compression methods and standards, but its fundamental structure

remains the same as in the previous edition. Several image transforms, previously

covered in Chapter 3 and whose principal use is compression, were moved to

this chapter.

xvi  Preface

Chapter 9, dealing with image morphology, is new. It is based on a significant

expansion of the material previously included as a section in the chapter

on image representation and description. Chapter 10, dealing with image segmentation,

has the same basic structure as before, but numerous new examples

were included and a new section on segmentation by morphological watersheds

was added. Chapter 11, dealing with image representation and description, was

shortened slightly by the removal of the material now included in Chapter 9.

New examples were added and the Hotelling transform (description by principal

components), previously included in Chapter 3, was moved to this chapter.

Chapter 12 dealing with object recognition was shortened by the removal of

topics dealing with knowledge-based image analysis, a topic now covered in

considerable detail in a number of books which we reference in Chapters 1 and

12. Experience since the last edition of Digital Image Processing indicates that

the new, shortened coverage of object recognition is a logical place at which to

conclude the book.

Artificial Intelligence I (2-1-1)

                             Text Book: Artificial Intelligence A Systems Approach

                             Author: M. TIM JONES

What is Intelligence?, The Search for Mechanical Intelligence, Artificial Intelligence  Emerges as a Field, AI’s Winter, AI Remerges, AI Inter-disciplinary R&D, Search and AI             ,Classes of Search ,General State Space Search, Trees, Graphs and Representation, Uninformed Search, Improvements, Algorithm Advantages, Two Player Games, The Minimax  Algorithm, Classical Game AI, Checkers, Chess, Othello, Go, Backgammon,   Poker, Machine Learning Algorithms, Short History of Neural Networks, Biological              Motiviation, Fundamentals of Neural Networks, The Perceptron, Least-Mean-Square (LMS) Learning, Learning with Backpropagation, Probabilistic Neural Networks, Other         Neural Network Architectures, Unsupervised Learning, Hebbian Learning, Simple Competitive Learning, k-Means Clustering, Adaptive Resonance Theory,        Hopfield Auto-Associative Model