Mobile product recognition service
With the advent of digitalisation and Artificial Intelligence (AI), automatic recognition has become an increasingly important domain. This research project deals with the recognition of various brands of potato chips under varying environmental conditions of illumination, size resolution, angles an...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/77398 |
| _version_ | 1811684564597211136 |
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| author | Yuen, Pui Leng |
| author2 | Yap Kim Hui |
| author_facet | Yap Kim Hui Yuen, Pui Leng |
| author_sort | Yuen, Pui Leng |
| collection | NTU |
| description | With the advent of digitalisation and Artificial Intelligence (AI), automatic recognition has become an increasingly important domain. This research project deals with the recognition of various brands of potato chips under varying environmental conditions of illumination, size resolution, angles and occlusion. In this report, we employed a simple yet robust preprocessing technique that first detects the packet of chips from an image and then corrects the illumination as needed. Therefore, this report focuses on studying the various algorithms. The processes and outcome of different feature extraction techniques were carefully studied: (1) SIFT feature and (2) Histogram of Gradient feature, both tested with K-Nearest Neighbour and Support Vector Machines as classifier respectively. An average hit rate of 98% was found for the best combination – namely the SIFT (in the case of all conditions namely; Angles, Sizes, Occlusion, Illumination and Distractors). |
| first_indexed | 2024-10-01T04:30:38Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/77398 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:30:38Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/773982023-07-07T15:57:07Z Mobile product recognition service Yuen, Pui Leng Yap Kim Hui School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering With the advent of digitalisation and Artificial Intelligence (AI), automatic recognition has become an increasingly important domain. This research project deals with the recognition of various brands of potato chips under varying environmental conditions of illumination, size resolution, angles and occlusion. In this report, we employed a simple yet robust preprocessing technique that first detects the packet of chips from an image and then corrects the illumination as needed. Therefore, this report focuses on studying the various algorithms. The processes and outcome of different feature extraction techniques were carefully studied: (1) SIFT feature and (2) Histogram of Gradient feature, both tested with K-Nearest Neighbour and Support Vector Machines as classifier respectively. An average hit rate of 98% was found for the best combination – namely the SIFT (in the case of all conditions namely; Angles, Sizes, Occlusion, Illumination and Distractors). Bachelor of Engineering (Electrical and Electronic Engineering) 2019-05-28T05:06:35Z 2019-05-28T05:06:35Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77398 en Nanyang Technological University 43 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Yuen, Pui Leng Mobile product recognition service |
| title | Mobile product recognition service |
| title_full | Mobile product recognition service |
| title_fullStr | Mobile product recognition service |
| title_full_unstemmed | Mobile product recognition service |
| title_short | Mobile product recognition service |
| title_sort | mobile product recognition service |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/77398 |
| work_keys_str_mv | AT yuenpuileng mobileproductrecognitionservice |