Developing Hopfield Neural Network For Color Image Recognition

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dc.contributor.authorMutter, Kussay Nugamesh
dc.date.accessioned2018-06-26T02:12:18Z
dc.date.available2018-06-26T02:12:18Z
dc.date.issued2010
dc.description.abstractHopfield Neural Network (HNN) is an iterative auto-associative network which consists of a single layer of fully connected processing elements and converges to the nearest match vector. This network alters the input patterns through successive iterations until a learned vector evolves at the output. Then the output will no longer change with successive iterations. HNN faces real problems when it deals with images of more than two colors, noisy convergence, limited capacity, and slow learning and converging according to the number of vectors and their sizes. These problems were studied and tested the proposed solutions to obtain the optimum performance of HNN and set a starting for future research. Smaller size of vectors of three pixels and dismantling the presented digital image into its essential bitplanes as independent sub-images is used for HNN’s processes. In addition, the stability value of HNN’s weight matrices can be re-localized from zero to non-zero. This will correct the errors which may appear in the final vector. However, the previous modifications still require processing of large data which are produced from separating high level color images into bitplanes. Using HNN as a compressor algorithm and Run-Length-Encoding will help to reduce the amount of the saved data. The final new Modified HNN (MHNN) has a complex and a slow processing; therefore, the Optical Logic Gates promotes a solid base to speed up MHNN processes. For testing the reliability of the proposed MHNN, three new sequenced implementations are suggested which are binary, gray, and RGB images. The experimental findings show that the new proposed MHNN can successfully work with color images with low noises and clear converging in comparison with the traditional HNN. Finally, the proposed technique of the MHNN can be generalized to be applied for any color images with optimum converging.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/5781
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectDeveloping hopfield neural networken_US
dc.subjectfor color image recognitionen_US
dc.titleDeveloping Hopfield Neural Network For Color Image Recognitionen_US
dc.typeThesisen_US
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