BackPropagation Neural Network for image Recognition by Ramesh

8/12/2019 BackPropagation Neural Network for image Recognition by Ramesh

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BackPropagation Neural Network for

image Recogn it ion


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Indira Gandhi Center For Atomic Research (IGCAR), is the

second largest establishment of the atomic energy next to Bhabha

Atomic Research Center, was setup at kalpakkam, 80 Kms south of

Chennai in 1971 with the main objective of conducting Board

based multidisciplinary programme of scientific research and

advanced engineering, directed towards the development of sodium

cooled Fast Breeder Reactor (FBR) technology, in India.

ORGANIZATION PROFILE


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ABSTRACT

Artificial Neural Networks are recent development tools that aremodeled from biological neural networks.

The powerful side of this new tool is its ability to solve problems thatare very hard to be solved by traditional computing methods (e.g. byalgorithms).

This work briefly explains Artificial Neural Networks and their

applications, describing how to implement a simple ANN for imagerecognition.

Back propagation, or propagation of error, is a common method of

teaching Artificial Neural Networks how to perform a given task.


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Neural Network: Neural network is a computer program that can

recognize the patterns in a given collection of data,and produce modelfor that data.

Conventional computers use algorithmicapproach, if the specific stepsthat the computer needs to follow are not known, the computer cannot

solve the problem.

That means, traditional computing methods can only solve theproblems that we have already understood and know how to solve.

However, ANNs are, in some way, much more powerful because theycan solve problems that we do not exactly know how to solve.


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That's why, of late, their usage is spreading over a wide range of

areaincluding,

robot control

pattern (image, fingerprint, noise...) recognition.

virus detection.

Back Propagation ANNs contain one or more layers each of which arelinked to the next layer.


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The Existing System

Consider an text processing task such as recognizing an everydayobject projected against a background of other objects. This is a taskthat even a small child's brain can solve in a few tenths of a second.

But building a conventional serial machine to perform as well isincredibly complex. However, that same child might NOT be capableof calculating 2+2=4, while the serial machine solves it in a few

nanoseconds. Ex: Hand-written characters also.


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Proposed System

The attraction of neural networks is that they are best suited to solvingthe problems that are the most difficult to solve by traditionalcomputational methods.

A large amount of input/outputdata is available, but you are not sure

how to relate it to the output.

The problem appears to have overwhelming complexity, but there isclearly a solution.

The solution to the problem may change over time, within the boundsof the given input and output parameters.

The output displayed as non-numeric.


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Software Specification

Operating System Server: Windows XP Tools: Microsoft Visual Studio .Net-2008

User Interface: Windows Application

Code Behind: C#.Net 3.5


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Hardware Specification

Processor: Intel Pentium

Ram: 512 MB

Hard Disk: PC with 40GB


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MODULES INVOLVED

Load Network

Train Network

Recognize Topology of a Multi-Layer Perception


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LOAD NETWORK In this module a user can upload any image using this system

user interface.

By using this load network we can load a network file Network

File (.net) extension.

Here we are using one sample.net for load a network


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TRAIN NETWORK

Apply input to the network.

Calculate the output.

Compare the resulting output with the desired output for the giveninput. This is called the error.

Modify the weights for all neurons using the error.

Repeat the process until the error reaches an acceptable value (e.g.error < 1%)


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RECOGNIZE

This section describes a simple demonstration of the neural networklibrary, using the character classifier which comes with the source

code.

The network contains35 input nodes (one for each pixel)

60 hidden nodes and

26 output nodes

The first layer is called the "input layer" which meets the initial input

(e.g. pixels from a letter)


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and so does the last one "output layer" which usually holds the input's

identifier (e.g. name of the input letter).

The layers between input and output layers are called "hiddenlayer(s)" which only propagate the previous layer's outputs to the nextlayer and [back] propagates the following layer's error to the previous

layer

When in use (i.e. after training), the network will be loaded with acorrupted image, with a pixel value going to each input node.

An alternative scheme would have 7 output nodes to give the binaryASCII character which best matches the given input.


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Topology of Multi-Layer Perception

The principal importance of a neural network is not only the way aneuron is implemented but also how their interconnections (morecommonly called topology) are made.

The topology of a human brain is too complicated to be used as amodel because a brain is made of hundreds of billions of connectionswhich can't be effectively described using such a low-level (andhighly simplified) model.

The topology we will study is therefore not the topology of a humanbrain but actually a simple topology designed for easyimplementation on a digital computer.


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One of the easiest forms of this topology at the moment is

made of three layers

input layer (the inputs of our network)

hidden layer output layer (the outputs of our network)

All neurons from one layer are connected to all neurons in the

next layer


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U Di


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Use-case Diagram

Input Text

Load Network

Train Network

Recognize The

image

Save Network &

Layers Settings

Admin User


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Data Flow Diagram

Dataflow is the movement of data in a system from a point of

origin to a specified destination indicated by line or arrow.

Dataflow diagram is the graphical representation of the data

movements, processes and files used in support of information

systems.


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Context Level (0thlevel DFD)

ADMIN

Administrator

Neural

Network

Image

Recognition

Data Input StageData

Storage

UI Screens

Data Out put Stage

Back PropagationUsing Neural Network


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Admin Functionalities1stLevel

Open Form

1.0.0

Draw theImage

1.0.1

Load NeuralNetwork

1.0.2

Train theNetwork

1.0.3

Recongnize

1.0.4

Local Disk

View Input

and Output

1.0.5Matched andUnmatched

Heights Check

1.0.6


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Admin Functionalities 2ndLevel

Open Form

1.0.0

Choose the

Existing

Image

1.0.1

Browse theImage

1.0.2

Load theNetwork

1.0.3

Train the

Network

1.0.4

Local Disk

Recongnize

1.0.5View Matched

and Unmatched

Heights Check

1.0.6


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Home Page


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Drawing Own Text for Recognize


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Load Network


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Train Network


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Recognize The Text


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Save Network


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Choose an Existing Text for

Recognize


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Providing Settings


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Save Settings


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It has been a great pleasure for me to work on this exciting andchallenging project. This project proved good for me as it

provided practical knowledge of not only programming in

ASP.NET and VB.NET web based application and no someextent Windows Application and SQL Server, but also about allhandling procedure related with Back propagation neuralnetwork for image recognition.

It also provides knowledge about the latest technology used in

developing web enabled application and client servertechnology that will be great demand in future. This will

provide better opportunities and guidance in future indeveloping projects independently.

Conclusion


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BackPropagation Neural Network for image Recognition by Ramesh

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