Fundamental Steps of Digital Image Processing

Fundamental Steps of Digital Image Processing : There are some fundamental steps but as they are fundamental, all these steps may have sub-steps. The fundamental steps are described below with a neat diagram.

(i) Image Acquisition : This is the first step or process of the fundamental steps of digital image processing. Image acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling etc.

(ii) Image Enhancement : Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image. Such as, changing brightness & contrast etc.

(iii) Image Restoration : Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation.

(iv) Color Image Processing : Color image processing is an area that has been gaining its importance because of the significant increase in the use of digital images over the Internet. This may include color modeling and processing in a digital domain etc.

(v) Wavelets and Multi resolution Processing : Wavelets are the foundation for representing images in various degrees of resolution. Images subdivision successively into smaller regions for data compression and for pyramidal representation.

(vi) Compression : Compression deals with techniques for reducing the storage required to save an image or the bandwidth to transmit it. Particularly in the uses of internet it is very much necessary to compress data.

(vii) Morphological Processing : Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape.

(viii) Segmentation : Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a long way toward successful solution of imaging problems that require objects to be identified individually.

(ix) Representation and Description : Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region or all the points in the region itself. Choosing a representation is only part of the solution for transforming raw data into a form suitable for subsequent computer processing. Description deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.

(x) Object recognition : Recognition is the process that assigns a label, such as,  “vehicle” to an object based on its descriptors.

(xi) Knowledge Base : Knowledge may be as simple as detailing regions of an image where the information of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite complex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing high-resolution satellite images of a region in connection with change-detection applications.

Rafael C Gonzalez and Richard E .Woods Digital Image Processing

Digital Image Processing by Gonzalez Second Edition

HANDBOOK OF IMAGE VIDEO PROCESSING

IMAGE VIDEO PROCESSING BY AL BOVIK

PATTERN RECOGNITION AND IMAGE PREPROCESSING

PATTERN RECOGNITION AND IMAGE PREPROCESSING

DATA COMPRESSION BY MARK NELSON

DATA COMPRESSION

IMAGE PROCESSING AL BOVIK

THE ESSENTIAL GUIDE TO IMAGE PROCESSING

DATA COMPRESSION

DATA COMPRESSION BY DAVID SALOMON

Remote Sensing Digital Image Analysis

Remote Sensing Image Analysis

DIP BY BERND JAHNE

DIP

FILLER STRUCTURES

STRONG -WEAK SLOT FILLER STRUCTURES

DEMPSTER-SHAFER THEORY

DEMPSTER-SHAFER THEORY

GAME PLAYING

MINIMAX & ALPHA-BETA PRUNING

INTRODUCTION TO ROBOTICS

ROBOTICS-1

ROBOTICS-2

SWARM INTELLIGENCE

SWARM INTELLIGENCE

PARTICLE SWARM INTELLIGENCE

EXPERT SYSTEMS

EXPERT SYSTEMS-1

EXPERT SYSTEMS-2

IMAGE PROCESSING FUNDAMENTALS

IMAGE PROCESSING

Principles of Image Processing :Core Algorithms

Image Processing

FUZZY LOGIC BY H.J ZIMMERMANN

FUZZY LOGIC

FUZZY LOGIC BY TIMOTHY J.ROSS

FUZZY LOGIC

Artificial Intelligence and Robotics

Robotics

FUNDAMENTALS OF DIP

DIGITAL IMAGE PROCESSING

IMAGE PROCESSING FUNDAMENTALS

FUNDAMENTALS

IMAGE PROCESSING PRINCIPLES AND APPLICATIONS

IMAGE PROCESSING PRINCIPLES AND APPLICATIONS

IMAGE PROCESSING UNITS 1 & 2

UNITS 1 & 2

FUZZY LOGIC

INTRODUCTION

FUNDAMENTALS OF FUZZY LOGIC

 FUZZY LOGIC

DIP using MATLAB CHAPTER 2

DIP using MATLAB

NLP & PLANNING

NLP & PLANNING

EXPERT SYSTEMS & AI LANGUAGES

EXPERT SYSTEMS & AI LANGUAGES

Benefits and Limitations of Expert Systems

Benefits and Limitations of Expert Systems:

Some problems like scheduling manufacturing can not be adequately dealt with using mathematical algorithms. But such problems can be dealt with an intelligent way using E.S. E.S. allow experts to be expert. As e.s. are built by DEs, experts will try to focus on the more difficult problems of their speciality.

This, in turn, will result in solutions to new problems and the range of problems which they can solve will widen. Although expert systems lack the robust and general intelligence of human beings they can provide benefits to organisations if their limitations are well understood; only certain classes of problems can be solved using expert system.

Many expert systems require large lengthy and expensive development efforts. Moreover, expert systems lack the breadth of knowledge and the understanding of fundamental principles of a human expert. Their knowledge bases are quite narrow, shallow and brittle.

In fast moving fields such as medicine or computer science, keeping the knowledge base upto date is a critical problem. Expert systems can only represent limited forms of IF-THEN type of knowledge. Such knowledge exists primarily in textbooks. There are no adequate representations for deep causal models or temporal trends. Expert systems cannot yet replicate knowledge which is intuitive, based on analogy or on common sense.

Contrary to early promises, expert system do best in automating lower-end clerical functions. They can provide electronic check lists for lower-level employees in service bureaucracies such as banking, insurance, sales and welfare agencies.

The applicability of expert system to managerial problems generally involve drawing facts and interpretations from divergent sources, evaluating facts and comparing one interpretation of the facts with another and do not involve analysis or simple classification. They typically perform very limited tasks which can be performed by professional, in a few minutes and hours. Hiring or training more experts may be less expensive than building an expert system.

REASONING UNDER UNCERTAINTY

REASONING UNDER UNCERTAINTY

Introduction to Reasoning with Uncertain Knowledge:

Introduction to Reasoning with Uncertain Knowledge:

Reasoning probably brings to mind logic puzzles, but it is something which we do every day of our lives. Reasoning in AI is the process by which we use the knowledge we have to draw conclusions or infer something new about a domain of interest. It is necessary part of what we call “intelligence”. Without the ability to reason we are doing little more than a lookup when we use information.

In fact, this is the difference between a standard data base system and a knowledge base. Both have information which can be accessed in various ways but the data base, unlike the knowledge base in the expert system, has no reasoning facilities and can therefore answer only limited specific questions.

What are the types of reasoning we come across? How do we know what to expect when we go on a train journey? What do we think when our friend is annoyed with us? How do we know what will happen if our car has a flat battery? Whether we are aware of it or not, we will use a number of different methods of reasoning, depending on the problem we are considering and the information that we have before us.

The three everyday situations mentioned above illustrate three key types of reasoning which we use. In the first case we know what to expect on a train journey because of our experience of numerous other train journeys. We infer that the new journey will share common features with the examples.

We are aware of the first example called induction, which can be summarised as generalisation from cases seen to infer information about cases unseen. We use it frequently in learning about the world around us. For example, every crow we see is black; therefore we infer that all crows are black. If we think about it, such reasoning is unreliable, we can never prove our inferences to be true, we can only prove them to be false. Take the crow again.

To prove that all crows are black we would have to confirm that all crows which exist, have existed or will exist are black. This is obviously not possible. However, to disprove the statement, all we need is to produce a single crow which is white or pink.

So at best we can amass evidence to support our belief that all crows are black. In spite of its unreliability inductive reasoning is very useful and is the basis of much of our learning. It is used particularly in machine learning.

The second example we considered was working out why a friend is annoyed with us, in other words trying to find an explanation for our friend’s behaviour. It may be that this particular friend is a stickler for punctuality and we are a few minutes late to our rendezvous. We may therefore infer that our friend’s anger is caused by we being late.

This is abduction, the process of reasoning back from something to the state or event which caused it. Of course this too is unreliable; it may be possible that our friend is angry for some other reason (perhaps we had promised to telephone him before coming to him but had avoided). Abduction can be used in cases where the knowledge is incomplete, Abduction can provide a “best guess” given, the available evidence.

The third problem is usually solved by deduction: we have knowledge about cars such as “if the battery is flat the headlights won’t work”; we know the battery is flat so we can infer that the lights won’t work. This is the reasoning of standard logic.

Indeed, we would express our car problem in terms of logic given that:

a = the battery is flat and b = the lights won’t work and the axioms

ᗄx: a(x) → b(x)

a (my car).

We can deduce b the light of my car won’t work (my car).

However, we cannot deduce the inverse: that is if we know b, we cannot deduce a; the battery of my car is flat if the lights of my car won’t work. This is not permitted in standard logic. If lights don’t work we may use abduction to derive this explanation. However, it could be wrong; there may be another explanation for the light failure (for example, a bulb may have blown or the battery connections may be loose).

Deduction is probably the most familiar form of explicit reasoning. It can be defined as the process of driving the logically necessary conclusion from the initial premises.

For example:

Elephants are bigger than dogs

Dogs are bigger than mice

Therefore

Elephants are bigger than mice

However, it should be noted that deduction is concerned with logical validity, not actual truth.

Consider the following example; given the facts, can we reach the conclusion by deduction?

Some dogs are greyhounds

The greyhounds run fast

Therefore

Some dogs run fast.

The answer is no. We cannot make this deduction because we do not know that all greyhounds are dogs. The fast dogs may therefore be the greyhounds which are not dogs. This of course is non-sensual in terms of what we know (or more accurately have induced) about the real world, but it is perfectly valid based on the premises given. We should therefore be cautious: deduction (also called analogical inference) does not always correspond to natural human (common sense) reasoning.

Fuzzy Reasoning

Probabilistic reasoning and reasoning with certainty factors deal with uncertainty using principles from probability to extend the scope of standard logics. An alternative approach is to change the properties of logic itself. Fuzzy sets and Fuzzy logic do just.

In classical set theory an item, say a is either member of set A or it is not. So a meal at a restaurant is either expensive or not expensive and a value must be provided to delimit set membership. Clearly, however, this is not the way we think in real life. While some sets are clearly defined (piece of fruit is either an orange or not an orange), others are not (qualities such as size, speed and price are relative).

Fuzzy set theory extends a classical set theory to accommodate the notion of degree of set membership. Each item is associated with a value between 0 and 1, where 0 indicates that it is not a member of the set and 1 that if in definitely a member. Values in between indicate a certain degree of membership.

For example, although we may agree with the inclusion of cars, Honda, and Maruti in the fast (car) {using logic}, we may wish to indicate that one is faster than the other. This can be possible in Fuzzy set theory.

Here the value in the bracket is a degree of set membership. Fuzzy logic is similar in that it attaches a measure of truth to facts. A predicate P is given. Value between 0 and 1 (as in fuzzy sets).

So the predicate fast (car) can be represented as:

Fast car (Honda 1.5 = 0.9)

Standard logic operators such as AND, OR, NOT are applicable and interpreted as:

Demster Shaffer Theory

 Demster Shaffer Theory :

Deals with the distinction between uncertainty and ignorance. Rather than computing the probability of a proposition it computes the probability that the evidence supports the proposition. This measure of belief is called a belief function, Bel (x).

In Bayesian network technique, degree of belief assigned to a proposition with given evidence, is a point, whereas in D.S. theory we consider sets of propositions and to each set we assign an interval [Belief, Plausibility]. 

in which the degree of belief must lie. We use probability density function m and for all subsets of ɸ for an exhaustive universe of mutually exclusive hypotheses Θ (called frame of discernment) and for all subsets of ɸ. Belief (represented as Bel) measures the strength of evidence in a set of propositions. This lies in the range 0-1 when no evidence means belief 0 and certainty: means belief is 1.

Plausibility (PI) is given by

PI (S) = 1- Bel (∼ S)

where S is set of proposition

In particular if we have certain evidence in favour of ∼ S then Bel (∼ S) will be 1.

and PI (S) is zero

and Bel (S) is also zero.

The belief-plausibility interval defined above measures not only our level of belief in some set of proposition but also the amount of information we have. This can be illustrated with the help of an example.

Suppose a person with doubtful character (shady character) approaches you to bet for Rs.500, telling that on the next flip on his coin heads would appear. You think for a moment that coin may not be fair. You have no evidence on the fairness or unfairness of the coin. Then as per the D-S theory, with no evidence whatsoever (on coin being fair or unfair)

Bel (Heads) = 0

Bel (−Heads) = 0

That is D−S theory has no intuitive faculty.

Now suppose you consult an expert of coins and he assets that with 90% certainty the coin is fair so you become 90% sure that P (Heads) = 0.5.

Now D-S theory gives Bel (Heads) = 0.9 x 0.5 = 0.45

and similarly bel (∼ Heads) = 0.45

There is still 10% point gap which is not accounted for by the evidence. Actually dempster rule shows how to combine evidence to give new value for belief and shafer’s work extends this into a complete computational model.

Since D-S theory deals not with point probability but with probability interval, the width of the interval might be an aid in deciding when do we need to acquire evidence.

In the present example before acquiring the expert’s testimony probability interval for Heads is [0, 1] and this gets reduced to [0.45, 0.55] after the expert’s testimony is received for the coin (information about fairness of coin).

However, there are no clear guidelines for how to do this and there is no clear meaning for what the width of an interval means. For example, knowing whether the coin is fair would have a significant impact on the belief that it will come up heads and detecting an asymmetric weight would have an impact on the belief that the coin is fair.

Consider another example:

Diagnosis problem, as a case of exhaustive universe of mutually exclusive hypotheses. This can be called the frame of discernment, written as Q.

This may be consisting of the set (Alg, Flu, Coe, Pne) following symptoms, where:

Alg – Allergy

Flu – Flue

Coe – Cold

Pne – Pneumonia 

Probability density function m is defined not for elements of Q but for all subsets of it is for ɸ and all subsets a m (p) is the amount of belief in the subset (p) of there is no prior evidence in is 1.0.

But when it becomes known through an evidence (at level of 0.6) that the correct diagnosis is in the set {Flu, Col, Pne}

then m gets updated as

{Flu, Col, Pne} (0.6)

{Θ} (0.4)

that is belief is assigned to set of symptoms {Flue, Col, Pne} the remainder of belief still continues to be in the layer set Θ.

Thus, in order to be able to use m and belief and plausibility in D-S. Theory we define functions which enable us to combine m’s from multiple sources of evidence.

Our goal is to attach some measure of belief, m, to the various subsets Z or Q. m is sometimes called the probability density function for a subset of Q. Realistically not all evidence is directly supportive of individual elements of Q. In fact, evidence most often supports different subsets Z of Q.

In addition, since the elements of Q are assumed mutually exclusive evidence in favour of some may have an effect on our belief in others. In purely Bayesian section, we address both of these situations by listing all the combinations of conditional probabilities. In the D-S theory, we handle these interactions by directly manipulating the sets of hypotheses.

There are 2n subsets of Q. We must assign m so that the sum of all the m values assigned to the subsets of Q is 1. Although dealing with 2n values may appear intractable, it usually turns out that many of the subsets will never need to be considered because they may have no significance in the problem domain; so their associated value m may tend to be zero to 3 above.

D-S theory is an example of an algebra supporting the use of subjective probabilities in reasoning, as compared with the objective probabilities of Bayes. In subjective probability theory, we build a reasoning algebra, often by relaxing some of the constraints of Bayes. It is sometimes felt that subjective probabilities better reflect human expert reasoning.

So we conclude uncertain reasoning by saying that D-S allows us to combine:

(i) Multiple sources of evidence for a simple hypothesis.

(ii) Multiple sources of evidence for different hypothesis.

Ways of Dealing Reasoning with Uncertain Knowledge

Ways of Dealing Reasoning with Uncertain Knowledge:

We looked at knowledge and considered how different knowledge representation schemes allow us to reason. Recall, for example that standard or classical logics allow us to infer new information from the facts and rules which we have. Such reasoning is useful in that it allows us to store and utilise information efficiently (we do not have to store everything).

However, such reasoning assumes that the knowledge available is complete (or can be inferred) and correct and that it is consistent. Knowledge added to such systems never makes previous knowledge invalid. Each new piece of information simply adds to the knowledge.

This is monotonic reasoning. Monotonic reasoning can be useful in complex knowledge bases since it is not necessary to check consistency (as required in expert systems) when adding knowledge or to store information relating to the truth of knowledge. It therefore saves time and storage.

However, if knowledge is incomplete or changing an alternative reasoning system is required. There are a number of ways of dealing with uncertainty.

We shall consider some of them, briefly:

1. Dempster shaffer theory

2. Fuzzy reasoning.

MYCIN EXPERT SYSTEM

MYCIN:

Historically, the MYCIN system played a major role in stimulating research interest in rule-based expert systems.

MYCIN is an expert system for diagnosing and recommending treatment of bacterial infections of the blood (such as meningitis and bacteremia). It was developed at Stanford University in California, USA, in the 1970s, and has become a template for many similar rule based systems. It is intended to support clinicians in the early diagnosis and treatment of meningitis, which can be fatal if not treated in time.

However, the laboratory tests for these conditions take several days to be completed, so doctors (and therefore MYCIN) have to make decisions with incomplete information associated with medical knowledge. MYCIN incorporated a calculus of uncertainty called certainty factors which seemed (at that time) to fit well with how doctors assessed the impact of evidence on the diagnosis.

This system was able to perform as well as some experts and considerably better than junior doctors. A consultation with MYCIN begins with requests for routine information such as age, medical history and so on, programming to more specific questions as required.

MYCIN’S expertise knowledge lies in the domain of bacterial infections. MYCIN’S pool of knowledge consists of approximately 500 antecedent-consequent rules, implemented in LISP which give MYCIN the ability to recognise about 100 causes of bacterial infections. Physicians usually begin antibiotic treatment for patients who have bacterial infections without knowing exactly which organism is the culprit.

There is no time to wait for definitive laboratory culture evidence, which accumulates too slowly. For the desperately sick, therapy must begin at once, not 2 days after.

This requirement leaves two choices:

i. The physician can either prescribe a broad-spectrum drug which covers all possibilities, or

ii. He can prescribe a better, disease specific drug.

MYCIN helps the physician to prescribe disease-specific drugs. MYCIN informs itself about particular cases by requesting information from the physician about a patient’s symptoms, general condition, history, and laboratory-test results. At each point, the question MYCIN asks is determined by MYCIN’S current hypothesis and the answer to all previous questions. Thus, the questions start as though taken from a checklist, but the questions then vary as evidence builds.

Here is an excerpt from a sample session:

1. What is the patient’s name?

Arun Sehgal

2. Male or Female?

Male

3. Age?

55

4. Have you obtained positive cultures indicating general type?

Yes.

5. What type of infection is it?

Primary bacteria.

6. When did symptoms first appear?

May 5, 2006

7. Let’s call the most recent positive culture C1. From what site was C1 taken?

From the blood.

8. When?

May 9, 2006

9. Let’s call the first significant organism from this culture U1. Do you know the identity of U1?

No.

10. Is U1 a rod or a coccus or something else?

Rod.

11. What in the gram stain of U1?

Gram-negative.

12. Have there been positive cultures from sites which are not associated with the infections about which you are seeking advice?

No.

13. Did U1 grow aerobically?

Yes.

When MYCIN is satisfied that it has a reasonably good grasp of the situation, MYCIN announces its diagnosis:

My recommendation will be based on the opinion that the identity of U1 may be:

1. Pseudomonas-aeruginosa

2. Klebsiella-pneumonise.

3. E.coli

4. Bacteroides-fragilis.

5. Enterobacter.

6. Proteus-nonmirabilis.

After asking a few more questions about the patient, MYCIN announces its recommendation:

1. On a scale of 0 to 4, where the higher number indicates greater severity, how would you rate the patient’s degree of sickness?

2. Does the patient have a clinically significant allergic reaction to any antimicrobial agent?

3. How much does the patient weigh?

4. My first recommendation is as follows: to cover for items 1, 2, 3, 5 and 6, give gentamycin using a dose of 119 mg (1.7 mg/kg) q8h IV [or IM] for 10 days. Modify dose in renal failure. Also, to cover for item 4, give clindamycin using a dose of 595 mg (8.5 mg/kg) q6th IV [or IM] for 14 days. If diarrhea or other gastrointestinal symptoms develop, check for pseudomembranous colitis.

It has the following organisational features:

i. Knowledge Representation:

In the form of production rules implemented in LISP. K.B. is of the form – If condition, and …. and condition hold then draw conclusion, and …. and conclusion encoded in the data structure of LISP programming.

ii. Reasoning:

Backward chaining, and forward reasoning uses certainty factors to reason with uncertain information. MYCIN uses backward chaining to discover what organisms where present. Then it uses forward chaining to reason from the organism to a treatment regime.

iii. Heuristics:

When the general category of infection has been established, MYCIN examines each candidate diagnosis in a depth-first manner. Heuristics are used to limit the search, including checking all premises of a possible rule to see if anyone of these is known to be false.

iv. Dialogue/Explanation:

The dialogue is computer controlled, with MYCIN driving the consultation through asking questions. Explanations are generated by tracing back through the rules which have been fired. Both “how? ” and “why? ” explanation are supported.