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OBJECT RECOGNITION BY USING ARTIFICIAL NEURAL NETWORK: Turning point based shape recognition using NN

ABDULRHMAN IDRIS, MALIK ABDALLHA OSMAN (2008) OBJECT RECOGNITION BY USING ARTIFICIAL NEURAL NETWORK: Turning point based shape recognition using NN. Universiti Teknologi PETRONAS. (Unpublished)

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Abstract

An artificial neural network (ANN) classifier for recognizing an object based on their shapes is presented, regardless their position, orientation or size. To extract features of an object, the significant point on the object known as comer or break point is extracted and the object shape is approximated by connecting this extracted break point with straight line. Shape features are associated with each segment consisting of three successive break points, or any two lines in the approximated shape. These features are the ratio between any two adjacent lines and the angle between them. The extracted features are used as input the ANN. The neural network configuration used in this project is multi-layer perceptron using back-propagation learning algorithm. In this project two type of shape have been recognized by a MLP. The network performance is evaluated by presenting several examples to the network and determines the difference between the tested image and the original shape used in the training, until the differences are minimized.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Microelectronics - Sensor Development
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 2053 not found.
Date Deposited: 28 Oct 2013 11:08
Last Modified: 25 Jan 2017 09:45
URI: http://utpedia.utp.edu.my/id/eprint/9995

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