| Summary: | 碩士 === 義守大學 === 電子工程學系 === 102 === This thesis discusses how to use the cloud-computing to accomplish a butterfly recognition system via the network connection and camera of Android phone.
First, the system is used to execute the client program on Android phone to take a butterfly image, then is chosen whether to upload to cloud server or not. After the image is uploaded to server with network connection, the server will execute the recognition processing using the theory of human visualization to achieve recognition processing. At pre-processing step, the image is normalized to specific image size and transformed from the RGB color space to LMS color space, and normalize the image luminance to reduce the influence of the light brightness. After that, we used Independent Components Analysis (ICA) filters to find the ICA feature and combine the visual attention model, to compute the saliency maps. In addition, the approaches of Naïve Bayesian classifier and K-Nearest Neighbor algorithm are used to accomplish the training and classification processing. After finding the top five butterfly’s candidates, the recognition system will return butterfly’s data from MySQL database to Android phone, such as butterfly’s name, range, distribution, description, etc.
The experimental results show that the recognition system uses only one feature, up to 83% recognition rate on Android phone testing. Furthermore, it can reduce the phone requirement and promote phone''s endurance by using the cloud server to achieve the complex calculation. Finally, it will be helpful for more people to use this system to real-time inquiry the butterfly data in real world.
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