Bird nest shape quality assessment using machine vision system
International Conference on Man Machine Systems (ICoMMS 2012) organized by School of Mechatronic Engineering, co-organized by The Institute of Engineer, Malaysia (IEM) and Society of Engineering Education Malaysia, 27th - 28th February 2012 at Bayview Beach Resort, Penang, Malaysia.
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Universiti Malaysia Perlis (UniMAP)
2012
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my.unimap-207072012-08-15T01:33:26Z Bird nest shape quality assessment using machine vision system Fathinul Syahir, Ahmad Sa'ad Ali Yeon, Md Shakaff, Prof. Dr. Mohd Zulkifly, Abdullah, Dr. Ammar, Zakaria fathinul@unimap.edu.my Shape analysis Vision system Fourier descriptor International Conference on Man Machine Systems (ICoMMS 2012) organized by School of Mechatronic Engineering, co-organized by The Institute of Engineer, Malaysia (IEM) and Society of Engineering Education Malaysia, 27th - 28th February 2012 at Bayview Beach Resort, Penang, Malaysia. Swiftlets are birds contained within the four genera Aerodramus, Hydrochous, Schoutedenapus and Collocalia. They form the Collocaliini tribe within the swift family Apodidae. Swiftlet nest economy is currently envisaged to contribute significantly to foreign earnings of Malaysia. Many establishments are currently engaged in bird nest farming and trying to improve the quality and quantity of nest production. The raw bird’s nest (unprocessed) can achieve up to RM 4,000 per kilos. Processed and cleaned bird’s nest can reach up to RM 9,000 or more per kilo. To date, the bird nest grading is based on weight and shape. The inspection and grading for raw edible bird nest were performed visually by expert panels. This conventional method is relying more on human judgments. Unfortunately, it is a tedious process and often inconsistence from one person to another. Bird nest has an approximately two-dimensional nature, and, therefore they are most suitable for real-time machine processing. This experiment was performed on various camera angel and bird nest position. More than hundreds birds nest was used in this experiment obtained throughout west peninsular Malaysia. A Fourier-based shape separation (FD) method was developed from CCD image data to grade bird nest by its shape and size. FD was able to differentiate different shape such as round (oval) and 'v' shaped depending on the swiftlet species and geographical origin. Shape analysis was established using multivariate discriminant analysis. The Wilks' lambda analysis was invoked to transform and compress the data set comprising of large number of interconnected variables to a reduced set of variates. It can be further used to differentiate bird nest from different geographical origin. Overall, the vision system was able to correctly classify 100% of the V and Oval shaped and 81.3% for each grade in oval shape of the bird nest. The performances were compared with the expert panels and the results show that this technique achieved similar accuracy. 2012-08-15T01:33:26Z 2012-08-15T01:33:26Z 2012-02-27 Working Paper http://hdl.handle.net/123456789/20707 en Proceedings of the International Conference on Man-Machine Systems (ICoMMS 2012) Universiti Malaysia Perlis (UniMAP) School of Mechatronic Engineering |
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Malaysia |
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Universiti Malaysia Perlis |
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http://dspace.unimap.edu.my/ |
| language |
English |
| topic |
Shape analysis Vision system Fourier descriptor |
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Shape analysis Vision system Fourier descriptor Fathinul Syahir, Ahmad Sa'ad Ali Yeon, Md Shakaff, Prof. Dr. Mohd Zulkifly, Abdullah, Dr. Ammar, Zakaria Bird nest shape quality assessment using machine vision system |
| description |
International Conference on Man Machine Systems (ICoMMS 2012) organized by School of Mechatronic Engineering, co-organized by The Institute of Engineer, Malaysia (IEM) and Society of Engineering Education Malaysia, 27th - 28th February 2012 at Bayview Beach Resort, Penang, Malaysia. |
| author2 |
fathinul@unimap.edu.my |
| author_facet |
fathinul@unimap.edu.my Fathinul Syahir, Ahmad Sa'ad Ali Yeon, Md Shakaff, Prof. Dr. Mohd Zulkifly, Abdullah, Dr. Ammar, Zakaria |
| format |
Working Paper |
| author |
Fathinul Syahir, Ahmad Sa'ad Ali Yeon, Md Shakaff, Prof. Dr. Mohd Zulkifly, Abdullah, Dr. Ammar, Zakaria |
| author_sort |
Fathinul Syahir, Ahmad Sa'ad |
| title |
Bird nest shape quality assessment using machine vision system |
| title_short |
Bird nest shape quality assessment using machine vision system |
| title_full |
Bird nest shape quality assessment using machine vision system |
| title_fullStr |
Bird nest shape quality assessment using machine vision system |
| title_full_unstemmed |
Bird nest shape quality assessment using machine vision system |
| title_sort |
bird nest shape quality assessment using machine vision system |
| publisher |
Universiti Malaysia Perlis (UniMAP) |
| publishDate |
2012 |
| url |
http://dspace.unimap.edu.my/xmlui/handle/123456789/20707 |
| _version_ |
1643793157448531968 |
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13.18916 |