测量汽车鱼眼相机的自然场景SFR
Measuring Natural Scenes SFR of Automotive Fisheye Cameras
AI总结：
本文提出改进自然场景空间频率响应（NS-SFR）算法以适用于宽视场相机，用于测量汽车鱼眼相机数据集中未知的图像质量指标。
摘要：
调制传递函数（MTF）是汽车领域常用的重要图像质量指标。然而，尽管光学质量会影响车辆自动化中计算机视觉的性能，但在许多公开数据集中，该指标仍是未知的。此外，宽视场（FOV）相机日益普及，尤其用于低速车辆自动化应用。为研究数据集中的图像质量，本文提出对自然场景空间频率响应（NS-SFR）算法进行改进，以适用于宽视场相机。
英文摘要：
The Modulation Transfer Function (MTF) is an important image quality metric typically used in the automotive domain. However, despite the fact that optical quality has an impact on the performance of computer vision in vehicle automation, for many public datasets, this metric is unknown. Additionally, wide field-of-view (FOV) cameras have become increasingly popular, particularly for low-speed vehicle automation applications. To investigate image quality in datasets, this paper proposes an adaptation of the Natural Scenes Spatial Frequency Response (NS-SFR) algorithm to suit cameras with a wide field-of-view.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
