backpropagation neural network for image recognition by ramesh
TRANSCRIPT
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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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