Feature extraction of non-uniform food products using RGB and RGB-D data combined with shape models
This research paper investigates a new 3D handling process in food industry: bin picking. Machines only function effectively when the input of product is physically well organised, well structured, and consistent. At many stages in a typical production line, foodstuffs are physically arranged as the...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
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IEEE Press
2011
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| Subjects: | |
| Online Access: | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6181526 http://hdl.handle.net/20.500.11937/12170 |
| _version_ | 1848748003579396096 |
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| author | Wurdermann, H. Aminzadeha, V. Cui, Lei Dai, J. |
| author2 | Zhidong Wang |
| author_facet | Zhidong Wang Wurdermann, H. Aminzadeha, V. Cui, Lei Dai, J. |
| author_sort | Wurdermann, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This research paper investigates a new 3D handling process in food industry: bin picking. Machines only function effectively when the input of product is physically well organised, well structured, and consistent. At many stages in a typical production line, foodstuffs are physically arranged as they move through a machine or equipment, however, this order is then lost again as products are ejected onto conveyors, bulked together into bins for transport, taken off-line for chilled storage. Bin picking is generally not solved for manufacturing parts. Unlike food ordering processes such as pick and place operations, vibratory feeders etc., this food handling operation has not been applied to food industry neither. A new approach is presented using the Microsoft KinectTM sensor and Active Shape Models. By combining the new device that obtains an RGB and RGB-D image and the flexible shape model, it is possible to identify non-uniform food products that have a high variation in shape. The methodology of this system is presented. The experiments show the achievability of this new system. |
| first_indexed | 2025-11-14T06:58:08Z |
| format | Conference Paper |
| id | curtin-20.500.11937-12170 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:58:08Z |
| publishDate | 2011 |
| publisher | IEEE Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-121702017-01-30T11:29:08Z Feature extraction of non-uniform food products using RGB and RGB-D data combined with shape models Wurdermann, H. Aminzadeha, V. Cui, Lei Dai, J. Zhidong Wang shape model food product RGB-D Kinect Feature extraction RGB This research paper investigates a new 3D handling process in food industry: bin picking. Machines only function effectively when the input of product is physically well organised, well structured, and consistent. At many stages in a typical production line, foodstuffs are physically arranged as they move through a machine or equipment, however, this order is then lost again as products are ejected onto conveyors, bulked together into bins for transport, taken off-line for chilled storage. Bin picking is generally not solved for manufacturing parts. Unlike food ordering processes such as pick and place operations, vibratory feeders etc., this food handling operation has not been applied to food industry neither. A new approach is presented using the Microsoft KinectTM sensor and Active Shape Models. By combining the new device that obtains an RGB and RGB-D image and the flexible shape model, it is possible to identify non-uniform food products that have a high variation in shape. The methodology of this system is presented. The experiments show the achievability of this new system. 2011 Conference Paper http://hdl.handle.net/20.500.11937/12170 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6181526 IEEE Press fulltext |
| spellingShingle | shape model food product RGB-D Kinect Feature extraction RGB Wurdermann, H. Aminzadeha, V. Cui, Lei Dai, J. Feature extraction of non-uniform food products using RGB and RGB-D data combined with shape models |
| title | Feature extraction of non-uniform food products using RGB and RGB-D data combined with shape models |
| title_full | Feature extraction of non-uniform food products using RGB and RGB-D data combined with shape models |
| title_fullStr | Feature extraction of non-uniform food products using RGB and RGB-D data combined with shape models |
| title_full_unstemmed | Feature extraction of non-uniform food products using RGB and RGB-D data combined with shape models |
| title_short | Feature extraction of non-uniform food products using RGB and RGB-D data combined with shape models |
| title_sort | feature extraction of non-uniform food products using rgb and rgb-d data combined with shape models |
| topic | shape model food product RGB-D Kinect Feature extraction RGB |
| url | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6181526 http://hdl.handle.net/20.500.11937/12170 |