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Texture recognition by using artificial neural network / Lee Sai Foong

By:

Lee, Sai Foong

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2013Description: xi, 38 p. : ill. ; 30 cm. + 1 CD-ROMISBN:

THE0001770(Local)

Subject(s):

Dissertation note: Project paper (Bachelor of Computer Science (Computer Graphic and Multimedia)) -- Universiti Malaysia Pahang - 2013 Abstract: This thesis describes the texture recognition by using the Artificial Neural Network (ANN). There are hard to understand on how to perform the texture recognition on any new set of image data. Therefore, to ease up the process on texture recognition, ANN has been chosen as the classifier to enhance the process of the texture recognition. There are thirteen types of Brodatz textures are considered as the dataset for this research and five sets for each type texture with different level of histogram equalized, noise for the training dataset. Backpropagation algorithm is one of the methods for the ANN. After the feature is obtained from the dataset, the feature will be trained and classifier by using theBack-propagation algorithm. All in all, this project will tell us how the Back-propagation classifier help in texture recognition and how to increases the success rate in texture recognition.

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Holdings ( 2 )
Title notes ( 3 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB GAMBANG	CD 7663 \| QA76.76.A65 L44 2013 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000078970
Final Year Report	UMPLIB PEKAN	QA76.76.A65 L44 2013 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000078969

Project paper (Bachelor of Computer Science (Computer Graphic and Multimedia)) -- Universiti Malaysia Pahang - 2013

Bibliography : p. 33-34

This thesis describes the texture recognition by using the Artificial Neural Network (ANN). There are hard to understand on how to perform the texture recognition on any new set of image data. Therefore, to ease up the process on texture recognition, ANN has been chosen as the classifier to enhance the process of the texture recognition. There are thirteen types of Brodatz textures are considered as the dataset for this research and five sets for each type texture with different level of histogram equalized, noise for the training dataset. Backpropagation algorithm is one of the methods for the ANN. After the feature is obtained from the dataset, the feature will be trained and classifier by using theBack-propagation algorithm. All in all, this project will tell us how the Back-propagation classifier help in texture recognition and how to increases the success rate in texture recognition.