Mango variety classifier system using neural network / Richelle I. Lopez.
By: Lopez, Richelle I [author]
Description: viii, 114 leaves ; 28cmContent type: text Media type: unmediated Carrier type: volumeSubject(s): Artificial intelligence | Computational intelligence | Pattern recognitionDDC classification: 006.3 Dissertation note: Thesis (Master in Computer Science) -- Cebu Institute of Technology- University, March 2011. Summary: This study presents a mango variety classifier system using neural network. This system has the ability to read real-time image from a camera. The image undergoes image processing techniques in order to remove noise and also enhance the image. The resulting image is used for the feature extraction like area, perimeter and diameter. The results are then interpreted and are fed to the network for training and classification. The proponent employs Kohonen SOM neural network with image and data processing techniques to implement and automatic mango variety classification system. The system accepts an image data and classifies the image according to the variety of mango which it belongs. It also gives information on the nutritional content of the mango based on its estimated size as what it appears in the image.| Item type | Current location | Home library | Call number | Copy number | Status | Date due | Barcode | Item holds |
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THESIS / DISSERTATION
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GRADUATE LIBRARY | GRADUATE LIBRARY | 006.3 T L8818 2011 (Browse shelf) | c.1 | Not for loan | CL-T1753 | ||
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THESIS / DISSERTATION
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GRADUATE LIBRARY | GRADUATE LIBRARY | 006.3 T L8818 2011 (Browse shelf) | c.2 | Not for loan | CL-T1754 |
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Thesis (Master in Computer Science) -- Cebu Institute of Technology- University, March 2011.
This study presents a mango variety classifier system using neural network. This system has the ability to read real-time image from a camera. The image undergoes image processing techniques in order to remove noise and also enhance the image. The resulting image is used for the feature extraction like area, perimeter and diameter. The results are then interpreted and are fed to the network for training and classification. The proponent employs Kohonen SOM neural network with image and data processing techniques to implement and automatic mango variety classification system. The system accepts an image data and classifies the image according to the variety of mango which it belongs. It also gives information on the nutritional content of the mango based on its estimated size as what it appears in the image.
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