A PSO-ANLBF based automated feature extraction method
Cataloged from PDF version of thesis report.
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BRAC Univeristy
2018
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| Acesso em linha: | http://hdl.handle.net/10361/9025 |
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10361-90252022-01-26T10:15:48Z A PSO-ANLBF based automated feature extraction method Shakhawat, Sabrina Amin, Mustahab Uddin, Dr. Jia Department of Computer Science and Engineering, BRAC University Extraction method Textured surfaces Cataloged from PDF version of thesis report. Includes bibliographical references (pages 27-30). This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017. Image 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. Sabrina Shakhawat Mustahab Amin B. Computer Science and Engineering 2018-01-11T06:49:39Z 2018-01-11T06:49:39Z 2017 8/21/2017 Thesis ID 13301143 ID 13101291 http://hdl.handle.net/10361/9025 en BRAC 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. 30 pages application/pdf BRAC Univeristy |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Extraction method Textured surfaces |
| spellingShingle |
Extraction method Textured surfaces Shakhawat, Sabrina Amin, Mustahab A PSO-ANLBF based automated feature extraction method |
| description |
Cataloged from PDF version of thesis report. |
| author2 |
Uddin, Dr. Jia |
| author_facet |
Uddin, Dr. Jia Shakhawat, Sabrina Amin, Mustahab |
| format |
Thesis |
| author |
Shakhawat, Sabrina Amin, Mustahab |
| author_sort |
Shakhawat, Sabrina |
| title |
A PSO-ANLBF based automated feature extraction method |
| title_short |
A PSO-ANLBF based automated feature extraction method |
| title_full |
A PSO-ANLBF based automated feature extraction method |
| title_fullStr |
A PSO-ANLBF based automated feature extraction method |
| title_full_unstemmed |
A PSO-ANLBF based automated feature extraction method |
| title_sort |
pso-anlbf based automated feature extraction method |
| publisher |
BRAC Univeristy |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/9025 |
| work_keys_str_mv |
AT shakhawatsabrina apsoanlbfbasedautomatedfeatureextractionmethod AT aminmustahab apsoanlbfbasedautomatedfeatureextractionmethod AT shakhawatsabrina psoanlbfbasedautomatedfeatureextractionmethod AT aminmustahab psoanlbfbasedautomatedfeatureextractionmethod |
| _version_ |
1814308361579003904 |