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视觉传感成像技术与数据处理进展
Review on imaging and data processing of visual sensing

查看参考文献162篇

王程 1   陈峰 2   汶德胜 3   雷浩 3   宋宗玺 3   赵航芳 4  
文摘 本文以视觉传感的新视觉传感硬件、处理技术和应用场景为主线,通过综合国内外文献和相关报道来梳理该领域在成像技术和数据处理方面的主要进展。从激光扫描成像、大动态范围光学成像技术、偏振成像与传感技术和海洋声学层析成像等研究方向,重点论述视觉传感领域的发展现状、前沿动态、热点问题和趋势。基于激光扫描的3维建模技术虽然取得了一些进展,但仍面临居多挑战。随着硬件设备和数据处理技术的发展,未来激光扫描系统将在众多民用领域得到广泛应用,满足不同的探测和建模任务;大动态范围光学成像相关技术已逐步应用于红外成像、光谱成像、偏振成像、超声成像和单光子成像等领域,将为多维信息获取、智能处理以及数据挖掘等提供有力支撑;充分挖掘偏振成像的应用潜能,与其他先进成像传感技术相结合,实现更优性能,对各个尺度下的成像场景都具有重要的应用价值;海洋声学层析成像需要与其他方法相结合,发展基于分布式水下传感网络、卫星观测、海底电缆、人工与自然噪声机会声源等联合观测的低成本、长期观测网络。对国内外视觉传感领域进展情况进行梳理、总结,有助于发现该领域的发展趋势以及明确下一步的研究方向。
其他语种文摘 Recently,significant developments of visual sensing have been observed in imaging technology and data processing, thereby providing great opportunities to enhance our ability to perceive and recognize our real world. Therefore,investigations on visual sensing possess important theoretical value and are required for application needs. Surveying the progress to understand the trend in the field of visual sensing and to clarify the future research direction is beneficial. The reviews are generated mainly based on analyzing peer-reviewed academic publications and related reports. A general description on the states of the art and trends about the visual sensing is provided,mainly including laser scanning,high dynamic range (HDR) imaging,polarization imaging,and ocean acoustic tomography. Specifically,for each of these imaging fields,parts discussed include new hardware,processing technology,and application scenarios. Processing of 3D point cloud data has become more effective along with the great progresses in deep learning and the advancement of hardware devices. Mean-while,applications of 3D point cloud data are increasingly popular for diverse purposes. Over past several years,many domestic institutions and teams focused on developing algorithms for 3D point cloud data processing,such as in feature extraction,semantic labeling and segmentation,and object detection. In particular,several teams have conducted a number of substantive work in the production and sharing of standard data sets,which promote and improve the processing ability and application level of point cloud data. However,at present,the commercial hardware still has some deficiencies. Combining 3D point cloud data with observation from other sensors is a valuable but challenging task. Nevertheless,the laser scanning system is expected to be widely used in transportation,civil engineering,forestry,agriculture,and other civil fields in the future to satisfy different detection and modeling tasks. At the same time,with ongoing advancements in laser scanning equipment,it also plays an important role in understanding natural sciences,such as archaeology and geoscience. High dynamic range imaging is a hot research field in digital image acquisition,processing,display,and applications. Currently, researchers mostly focus on multiple exposure,different modulation methods,and multi detector methods in the HDR imaging. For example,through nonlinear response and multiple exposure imaging,the dynamic range can reach approximately 140 dB,and it can reach approximately 160 dB by using multi detector imaging. Using deep learning directly in HDR image mapping,instead of using traditional methodology,such as optical flow method and the combination of optical flow and neural network,has become a distinguished characteristic. Deep learning neural network has also been gradually applied to single exposure HDR reconstruction and tone mapping. Many domestic research teams have investigated the issues for the combination of deep learning neural network and HDR imaging.
来源 中国图象图形学报 ,2021,26(6):1450-1469 【核心库】
DOI 10.11834/jig.200852
关键词 视觉传感 ; 激光扫描 ; 大动态范围成像 ; 偏振成像 ; 海洋声学层析成像
地址

1. 厦门大学, 福建省智慧城市感知与计算重点实验室, 厦门, 361005  

2. 厦门理工学院计算机与信息工程学院, 厦门, 361024  

3. 中国科学院西安光学精密机械研究所, 西安, 710119  

4. 浙江大学信息与电子工程学院, 杭州, 310058

语种 中文
文献类型 研究性论文
ISSN 1006-8961
学科 自动化技术、计算机技术
文献收藏号 CSCD:6993703

参考文献 共 162 共9页

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引证文献 3 篇

1 黄铁军 脉冲视觉研究进展 中国图象图形学报,2022,27(6):1823-1839
CSCD被引 5 次

2 郭露露 基于局域加权叠加的高动态范围图像融合算法 光子学报,2022,51(11):1110001
CSCD被引 0 次

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