Downsampling of Graph Signals and Object Detection Application Using Fast Region-based Convolutional Networks

• Main Authors: Pei-Hsuan Hung, 洪培軒
• Other Authors: Soo-Chang Pei
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/54802948944695108964

碩士 === 國立臺灣大學 === 電信工程學研究所 === 104 === This thesis consists of two sections. In the first section, we study the downsampling methods for graph signals. Graph Signal Processing is an emerging field of signal processing for us to analysis irregular structure signals and becomes more and more significant in these days. The operations on these datasets as graph signals have been subjects to many recent studies, especially for basic signal operations such as shifting, modulating, and down-sampling. However, the sizes of the graphs in the applications can be very large and lead a lot of computational and technical challenges for the purpose of storage or analysis. To compress these datasets on graphs more effectively, we propose a pre-filtering classifier can selectively downsample signals and also consider the distribution of the signals on graphs. As compared to the other methods, such as color-based methods and topology-based methods, our proposed method can achieve better performance in terms of higher SNR. Moreover, our method can be processed efficiently and efficacy in terms of shorter computing-time and fewer vertices in use during compression. The second section of this thesis talks about how to use Fast Regions with Convolutional Neural Network (Fast R-CNN) to develop some object detection applications from the building of the environment including the setup of GPU and the platform of parallel computing to the process of training and testing in fast R-CNN algorithm. By using region-based convolutional neural networks, the correctness of object detection has a large progress in recent years, and fast R-CNN algorithm helps us to achieve near real-time rates when using very deep networks. To realize this efficient and powerful method more, some applications based on it are also proposed. Further, a machine learning technique is also applied to graph signal processing.