CS-485: Capstone in Computer Science

Artificial Neural Networks and their application in Intelligent Image

ProcessingSpring 2010

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Organizational Details• Class Meeting:

12:25-3:45pm Tuesday, SCIT213

• Class webpage http://www.eagle.tamut.edu/faculty/igor/CS-485.htm

• Instructor: Dr. Igor Aizenberg

• Office: Science and Technology Building, 104C• Phone (903 334 6654) • e-mail: igor.aizenberg@tamut.edu

• Office hours:• Monday, Thursday 10-30 – 6-30 • Tuesday, Wednesday 4-30 – 6-30

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Text Book

1) I. Aizenberg, “Advances in Neural Networks”, University of Dortmund, 2005,Class notes (available from the class webpage)

2) Additional materials will also be available from the class webpage

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Applied Problems:

•Image, Sound, and Pattern recognition•Decision makingKnowledge discovery Context-Dependent Analysis…

Artificial Intellect:Who is stronger and why?

NEUROINFORMATICS

- modern theory about principles and new mathematical models of information processing, which based on the biological prototypes and mechanisms of human brain activities

Introduction to Neural Networks

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Natural language understanding (Translation of the texts)

Recognition of Images

Decision Making

Knowledge Discovery

Learning and Adaptation

Team behavior

Fuzzy Logic

Reasoning and Prediction

Cognitive analysis

Applied Problems

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Renaissance of connectionism from the papers by Hopfield, and popularizing the back-propagation algorithm for multiplayer feed-forward networks

McCulloch and Pitts introduced the fundamental ideas of analyzing neural activity via thresholds and weighted sums

Notion of Wiener about key role of connectionism and feedback loops as a model for learning in neural networks

Hebb hypothesis that human and animal long-term memory is mediated by permanent alterations in the synapses.

Minsky’s builts the first actual neural network learning system

Frank Rosenblatt invented the modern “perceptron” style of NN, composed of trainable threshold units Ashby puts the idea that intelligence

could be created by the use of “homeostatic” devices which learn through a kind of exhaustive search

1982

1969

1949

1948

1943

End of Perceptron era:Work “Perceptron” by Minsky and Papert

1957

1952

1951

The History of Neuroscience

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NN as an model of brain-like Computer

An artificial neural network (ANN) is a massively parallel distributed processor that has a natural propensity for storing experimental knowledge and making it available for use. It means that: Knowledge is acquired by the network through a learning (training) process; The strength of the interconnections between neurons is implemented by means of the synaptic weights used to store the knowledge.

The learning process is a procedure of the adapting the weights with a learning algorithm in order to capture the knowledge. On more mathematically, the aim of the learning process is to map a given relation between inputs and output (outputs) of the network.

Brain

The human brain is still not well understood and indeed its behavior is very complex!There are about 10 billion neurons in the human cortex and 60 trillion synapses of connectionsThe brain is a highly complex, nonlinear and parallel computer (information-processing system)

ANN as a Brain-Like Computer

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DataAcquisition

DataAnalysis

Interpretation and

Decision Making

Signals&

parameters

Characteristics&

Estimations

Rules&

KnowledgeProductions

DataAcquisition

DataAnalysis

Decision Making

KnowledgeBase

Adaptive Machine Learningvia Neural Network

Intelligent Data Analysis in Engineering Experiment

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mp

m1

m2

m3

xi

yi

n

tff pn

F

:

p

1. Quantization of pattern space into p decision classes

Input Patterns Response:

1

1

2

1

1

nx

x

x

ix

1

12

11

ny

y

y

iy

2. Mathematical model of quantization:

“Learning by Examples”

Mathematical Interpretation of Classification in Decision Making

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Self-organization – basic principle of learning:Structure reconstruction

Input Images

Teacher

NeuroprocessorResponce

The learning involves

change of structure

Learning Rule

Learning via Self-Organization Principle

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Artificial Intellect with

Neural Networks

Intelligent Control

Intelligent Control

Technical DiagnisticsTechnical

Diagnistics

Intelligent Data Analysis

and Signal Processing

Intelligent Data Analysis

and Signal Processing

Advance Robotics

Advance Robotics

Machine Vision

Machine Vision

Image & Pattern

Recognition

Image & Pattern

Recognition

Intelligent Security Systems

Intelligent Security Systems

Intelligentl Medicine

Devices

Intelligentl Medicine

Devices

Intelligent Expert

Systems

Intelligent Expert

Systems

Applications of Artificial Neural Networks

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Theory Practice Self-Paced

Work

Artificial Neural NetworksAnd Its Applications

You will learn:Contemporary theoretical principles and paradigms of Neuroinformatics,Mathematical models and algorithms of neural network techniques for experimentation,Applications of Neuroinformatics to engineering and sciences problems,Computer-Aided Technology for Instrumentation

What we will learn and do?

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What we will learn and do?

• General principles of artificial neural networks• General principles of learning algorithms• Feedforward neural network and

backpropagation learning• Multi-valued neurons and a feedforward neural

network based on multi-valued neurons• Basic ideas of image processing• Edge detection on noisy images using a neural

network

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Symbol manipulation Pattern recognition

Which way of imagination is best for you ?

Dove fliesLion goesTortoise scrawlsDonkey sitsShark swims

Ill-Formalizable Tasks:•Sound and Pattern recognition•Decision making•Knowledge discovery•Context-Dependent Analysis

What is difference between human brain and traditional computer via specific approaches to solution of ill-formalizing tasks (those tasks that can not be formalized directly)?

Symbol Manipulation or Pattern Recognition ?

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Massive parallelism Brain computer as an information or signal processing system, is composed of a large number of a simple processing elements, called neurons. These neurons are interconnected by numerous direct links, which are called connection, and cooperate which other to perform a parallel distributed processing (PDP) in order to soft a desired computation tasks.

Connectionism

Brain computer is a highly interconnected neurons system in such a way that the state of one neuron affects the potential of the large number of other neurons which are connected according to weights or strength. The key idea of such principle is the functional capacity of biological neural nets determs mostly not so of a single neuron but of its connections

Associative distributed memoryStorage of information in a brain is supposed to be concentrated in synaptic connections of brain neural network, or more precisely, in the pattern of these connections and strengths (weights) of the synaptic connections.

A process of pattern recognition and pattern manipulation is based on:

How our brain manipulates with patterns ?

Principles of Brain Processing

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Brain-Like Computer

Brain-like computer –

is a mathematical model of humane-brain principles of computations. This computer consists of those elements which can be called the biological neuron prototypes, which are interconnected by direct links called connections and which cooperate to perform parallel distributed processing (PDP) in order to solve a desired computational task.

Neurons and Neural Net

The new paradigm of computing mathematics consists of the combination of such artificial neurons into some artificial neuron net.

Artificial Neural Network – Mathematical Paradigms of Brain-Like Computer

Brain-like Computer

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Connectionizm

NN is a highly interconnected structure in such a way that the state of one neuron affects the potential of the large number of another neurons to which it is connected accordiny to weights of connections

Not Programming but TrainingNN is trained rather than programmed to perform the given task since it is difficult to separate the hardware and software in the structure. We program not solution of tasks but ability of learning to solve the tasks

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wwwwDistributed MemoryNN presents an distributed memory so that changing-adaptation of synapse can take place everywhere in the structure of the network.

Principles of Neurocomputing

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2xy 2xy

Learning and AdaptationNN are capable to adapt themselves (the synapses connections between units) to special environmental conditions by changing their structure or strengths connections.

Non-Linear FunctionalityEvery new states of a neuron is a nonlinear function of the input pattern created by the firing nonlinear activity of the other neurons.

Robustness of AssosiativityNN states are characterized by high robustness or insensitivity to noisy and fuzzy of input data owing to use of a highly redundance distributed structure

Principles of Neurocomputing

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