Huang Jingjin, Zhou Guoqing, Zhou Xiang, Zhang Rongting
School of Precision Instrument & Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China.
Guangxi Key Laboratory for Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China.
Sensors (Basel). 2018 Mar 28;18(4):1014. doi: 10.3390/s18041014.
Although some researchers have proposed the Field Programmable Gate Array (FPGA) architectures of Feature From Accelerated Segment Test (FAST) and Binary Robust Independent Elementary Features (BRIEF) algorithm, there is no consideration of image data storage in these traditional architectures that will result in no image data that can be reused by the follow-up algorithms. This paper proposes a new FPGA architecture that considers the reuse of sub-image data. In the proposed architecture, a remainder-based method is firstly designed for reading the sub-image, a FAST detector and a BRIEF descriptor are combined for corner detection and matching. Six pairs of satellite images with different textures, which are located in the Mentougou district, Beijing, China, are used to evaluate the performance of the proposed architecture. The Modelsim simulation results found that: (i) the proposed architecture is effective for sub-image reading from DDR3 at a minimum cost; (ii) the FPGA implementation is corrected and efficient for corner detection and matching, such as the average value of matching rate of natural areas and artificial areas are approximately 67% and 83%, respectively, which are close to PC's and the processing speed by FPGA is approximately 31 and 2.5 times faster than those by PC processing and by GPU processing, respectively.
尽管一些研究人员已经提出了基于现场可编程门阵列（FPGA）的加速段测试特征（FAST）和二进制鲁棒独立基元特征（BRIEF）算法的架构，但这些传统架构没有考虑图像数据存储，这将导致后续算法无法重用图像数据。本文提出了一种考虑子图像数据重用的新型FPGA架构。在所提出的架构中，首先设计了一种基于余数的方法来读取子图像，将FAST检测器和BRIEF描述符结合用于角点检测和匹配。使用位于中国北京门头沟区的六对具有不同纹理的卫星图像来评估所提出架构的性能。Modelsim仿真结果表明：（i）所提出的架构以最低成本有效地从DDR3读取子图像；（ii）FPGA实现对于角点检测和匹配是正确且高效的，例如自然区域和人工区域的匹配率平均值分别约为67%和83%，接近PC的匹配率，并且FPGA的处理速度分别比PC处理和GPU处理快约31倍和2.5倍。
