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dc.contributor.advisorUddin, Dr. Jia
dc.contributor.authorShakhawat, Sabrina
dc.contributor.authorAmin, Mustahab
dc.date.accessioned2018-01-11T06:49:39Z
dc.date.available2018-01-11T06:49:39Z
dc.date.copyright2017
dc.date.issued2017-08-21
dc.identifier.otherID 13301143
dc.identifier.otherID 13101291
dc.identifier.urihttp://hdl.handle.net/10361/9025
dc.descriptionThis thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (pages 27-30).
dc.description.abstractImage is a much better way of explanation than documentation. The summary of any problem area can be easily described with images. On the other hand, documentation takes much time to make people understood the real problem. With the developing technology, images have been so important in many sectors of the modern world. The medical reports, security verification, boundary measurement- these types of services need very regulable images. Thus image processing is drawing an important role in modern technology. Filtering and feature extraction are the major steps of image processing. There are various types of methods for feature extraction and filtering. Non-local filtering and bilateral filtering are two efficient methods of filtering. We come up with an idea of a hybrid approach of filtering using both non-local and bilateral filtering, called ANLBF (adaptive nonlocal bilateral filtering). The filtering system will efficiently contribute in noise reduction and color enhancement and the output image will be converted into binary image and later will be classified utilizing threshold segmentation. The feature extraction is based on particle swarm optimization technique that helps to reduce the feature vectors. The performance of the proposed approach is acquired using the classification accuracy rate that shows that the approach is effective with minimum 82% accuracy and maximum 91% accuracy rate.en_US
dc.description.statementofresponsibilitySabrina Shakhawat
dc.description.statementofresponsibilityMustahab Amin
dc.format.extent30 pages
dc.language.isoenen_US
dc.publisherBRAC Univeristyen_US
dc.rightsBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectExtraction methoden_US
dc.subjectTextured surfacesen_US
dc.titleA PSO-ANLBF based automated feature extraction methoden_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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