Mobile visual product search and recommendation
Nowadays, computer vision is one of the popular developing fields which gives more effective data tracking and tracing services to the users. Hence, visual recognition based applications for the smartphone have drawn grown interest and demand by users lately. Although there are many studies of visua...
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sg-ntu-dr.10356-721162023-07-07T17:22:19Z Mobile visual product search and recommendation Aung, Thin Thin Yap Kim Hui School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Nowadays, computer vision is one of the popular developing fields which gives more effective data tracking and tracing services to the users. Hence, visual recognition based applications for the smartphone have drawn grown interest and demand by users lately. Although there are many studies of visual recognition based applications in various areas, the study of visual recognition based application for artifacts is still not widely covered and developed yet. Therefore, this project mainly discusses the insight of visual recognition techniques and conducts experiments to observe the accuracy rate of the technique on artifacts. During this project, a small database with minimum criteria set is created and used for experiments. Moreover, Geometric Verification (GV), an alternative method which can reduce external noises such as illuminations changes, orientation changes and occlusions is introduced. After several experiments, results showed an increase of matching accuracy between 5 to 10% by this method. However, one of the disadvantages of this method is it required longer processing time for feature matching step. Lastly, various sets of experiments with different conditions are conducted with and without GV. Then, result analysis of the experiments are made and compared in term of processing time and accuracy rate. In conclusion, the possible ways to enhance the matching efficiency as well as to further test algorithm robustness for the future studies are discussed. Bachelor of Engineering 2017-05-26T02:47:36Z 2017-05-26T02:47:36Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/72116 en Nanyang Technological University 51 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Aung, Thin Thin Mobile visual product search and recommendation |
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Nowadays, computer vision is one of the popular developing fields which gives more effective data tracking and tracing services to the users. Hence, visual recognition based applications for the smartphone have drawn grown interest and demand by users lately. Although there are many studies of visual recognition based applications in various areas, the study of visual recognition based application for artifacts is still not widely covered and developed yet. Therefore, this project mainly discusses the insight of visual recognition techniques and conducts experiments to observe the accuracy rate of the technique on artifacts. During this project, a small database with minimum criteria set is created and used for experiments. Moreover, Geometric Verification (GV), an alternative method which can reduce external noises such as illuminations changes, orientation changes and occlusions is introduced. After several experiments, results showed an increase of matching accuracy between 5 to 10% by this method. However, one of the disadvantages of this method is it required longer processing time for feature matching step. Lastly, various sets of experiments with different conditions are conducted with and without GV. Then, result analysis of the experiments are made and compared in term of processing time and accuracy rate. In conclusion, the possible ways to enhance the matching efficiency as well as to further test algorithm robustness for the future studies are discussed. |
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Yap Kim Hui |
author_facet |
Yap Kim Hui Aung, Thin Thin |
format |
Final Year Project |
author |
Aung, Thin Thin |
author_sort |
Aung, Thin Thin |
title |
Mobile visual product search and recommendation |
title_short |
Mobile visual product search and recommendation |
title_full |
Mobile visual product search and recommendation |
title_fullStr |
Mobile visual product search and recommendation |
title_full_unstemmed |
Mobile visual product search and recommendation |
title_sort |
mobile visual product search and recommendation |
publishDate |
2017 |
url |
http://hdl.handle.net/10356/72116 |
_version_ |
1772825798528466944 |