标题: 嵌入式影像处理系统及 SURF 特征撷取
Embedded Image Processing System and SURF Feature Extraction
作者: 徐尉嘉
Hsu, Wei-Jia
宋开泰
Song, Kai-Tai
电机学院电机与控制学程
关键字: 数位讯号处理器;可程式逻辑闸阵列;加速强健特征;嵌入式;DSP;FPGA;SURF;Embedded
公开日期: 2011
摘要:   本论文主要目的在研制一个嵌入式影像处理卡,未来能将其应用在机器人上进行特征撷取及各种的影像处理演算法。本影像处理卡之电路设计可以分成数位讯号处理器(DSP)及可程式逻辑闸阵列(FPGA)两个模组。影像撷取采用低功耗及小尺寸的CMOS影像感测器,其输出格式设定为YUV4:2:2,撷取的影像尺寸则是512x480像素。DSP采用德州仪器公司的TMS320C6414TGLZ7,主要用于实现影像处理演算法;FPGA采用 Altera公司的EP2C35F672C6 ,用于比较低阶的影像处理及CMOS影像感测器控制。本论文成功地于所研制的影像处理卡中实现加速强健特征(SURF)演算法,透过USB介面将所撷取的影像画面及SURF描述子传送到个人电脑显示及进行特征匹配。实验结果显示,即时影像撷取周期大约33.5ms;当SURF描述子数量等于64个,计算时间大约417.5ms;当SURF描述子数量为199个,计算时间大约是985ms;依样本模型在特征撷取时设定的门槛值不同,特征匹配率则由70.2%变化到20%。
This thesis aims to develop an embedded image processing board, which can be used to realize various image feature extraction and processing algorithms for robotic applications. The circuitry of this image board can be divided into two parts, one is for a digital signal processor (DSP) and the other for a field programmable gate array (FPGA). A low power, small size CMOS image sensor with YUV4:2:2 image format and 512x480 pixel size is adopted for image acquisition. The DSP module is mainly used for implementing image processing algorithms, while the FPGA module for lower level image processing and sensor control functions. In this work, the TMS320C6414TGLZ7 DSP from Texas Instruments Corporation and the EP2C35F672C6 FPGA from Altera Corporation were selected for the image board. In this work, speeded-up robust features (SURF) algorithm has been successfully realized on the developed image board. An USB interface with average throughput of 22MB/sec works to transfer acquired image frames and extracted SURF descriptors to a personal computer, where feature matching is executed. Experimental results show that the refresh period of frame grabbing is about 33.5ms. When the number of SURF descriptor is equal to 64, the computing time is about 417.5ms; when the number of SURF descriptor is equal to 199, the computing time is about 985ms. The typical matching rate varies from 70.2% to 20%, depending on the threshold of the patterns used in feature extraction.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079367544
http://hdl.handle.net/11536/40667
显示于类别:Thesis


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