基于时间域读出的大动态范围CMOS图像传感器

设计了一款基于时间域读出的大动态范围CMOS图像传感器。传感器基于一种新型的结构,其可在时间域下探测高输入光强,在模拟域下探测低输入光强。该设计在传统电容反馈式跨阻放大器(CTIA)的基础上,新增了时间域测量电路,在不改变原有积分过程的同时可实现连续的大动态范围。基于0.35μm,5V-CMOS工艺进行了256×1线列CMOS图像传感器流片,光电二极管面积为22.5μm×22.5μm,并对器件的光电特性进行了后仿真验证。仿真测试结果表明,基于时间域读出的图像传感器可实现96dB的大动态范围,且时间域和模拟域的两路输出信号可同步输出,功耗为7.98mW。     

基于标准CMOS工艺的片上太阳敏感器研究

微纳卫星对于载荷的苛刻要求使得太阳敏感器的微型化研究具有重要意义。为了解决光学器件和处理电路的集成兼容问题,文章基于标准CMOS工艺提出一种新型片上太阳敏感器,以金属走线层构建微型墙结构,两侧均匀分布pn结构成光电传感器,通过检测两侧光电流比例解算出入射光角度。文章从工艺实现、模型建立、数值仿真和实验测试等方面验证了器件的合理性和可行性。最终,片上太阳敏感器阵列芯片质量为1.5g,尺寸为304.2mm³,检测精度为±1.6°,视场范围为80°,可满足微型化需求。     

Semantic Plane-Structure based motion detection with a nonstationary camera

This paper presents a novel method to accurately detect moving objects from a video sequence captured using a nonstationary camera. Although common methods provide effective motion detection for static backgrounds or through only planar-perspective transformation, many detection errors occur when the background contains complex dynamic interferences or the camera undergoes unknown motions. To solve this problem, this study proposed a motion detection method that incorporates temporal motion and spatial structure. In the proposed method, first, spatial semantic planes are segmented, and image registration based on stable background planes is applied to overcome the interferences of the foreground and dynamic background. Thus, the estimated dense temporal motion ensures that small moving objects are not missed. Second, motion pixels are mapped on semantic planes, and then, the spatial distribution constraints of motion pixels, regional shapes and plane semantics, which are integrated into a planar structure, are used to minimise false positives. Finally, based on the dense temporal motion and spatial structure, moving objects are accurately detected. The experimental results on CDnet dataset, Pbi dataset, Aeroscapes dataset, and other challenging self-captured videos under difficult conditions, such as fast camera movement, large zoom variation, video jitters, and dynamic background, revealed that the proposed method can remove background movements, dynamic interferences, and marginal noises and can effectively obtain complete moving objects.© 2017 ElsevierInc.Allrightsreserved.     

基于测量误差不确定性加权的快速相机位姿估计算法

在相机位姿估计任务中,参考点位置的测量容易受测量仪器或者所处环境影响,出现一定程度的测量误差,其中误差较大的点对最终求解精度的影响很大,被称为野值点。文章针对现有算法抗野值点能力较弱的问题,提出一种基于测量误差不确定性加权的快速相机位姿估计算法。该算法以经典的正交迭代算法为基础,引入M估计方法,对目标函数根据参考点的测量误差赋予相应大小的权值,最大程度地将野值点的影响排除,并引入Kronecker积对计算过程进行优化,减少了迭代过程中的冗余计算,提高运算速度。仿真实验结果表明,改进后的算法提升了原算法的抗野值点能力,且计算速度更快。     

Motion detection in moving camera videos using background modeling and FlowNet

Real-time moving object detection is challenging for moving cameras due to the moving background. Many studies use homography matrix to compensate for global motion by warping the background model to the current frame. Then, the pixel difference between the current frame and the background model is used for background subtraction. Moving pixels are extracted by applying adaptive threshold and some post-processing techniques. On the other hand, deep learning-based dense optical flow can be efficient enough to extract the moving pixels, but it increases computational cost. This study proposes a method to enhance a classical background modeling method with deep learning-based dense optical flow. The main contribution of this paper is to propose a fusing algorithm for dense optical flow and background modeling approach. The background modeling methods are error-prone, especially with continuous camera movement, while the optical flow method alone may not always be efficient. Our hybrid method fuses both techniques to improve the detection accuracy. We propose a software architecture to run background modeling and dense optical flow methods in parallel processes. The proposed implementation approach significantly increases the method’s working speed, while the proposed fusion and combining strategy improve detection results. The experimental results show that the proposed method can run at high speed and has satisfying performance against the methods in the literature.     

基于机器视觉的机器人辨识及分拣盒装香烟的系统

运用机器视觉技术和机器人技术设计并实现了一种识别、分拣盒装香烟的系统,以提高识别准确率和分拣效率。该系统通过USB摄像头采集烟盒图像信息,从烟盒的特征检测入手,利用Matlab工具对香烟条形码、烟盒颜色和字迹字符三个方面进行处理分析,识别出真伪香烟,并随即通过控制机器人手爪对识别出的香烟进行分拣。     

A fully differential transimpedance amplifier with integrated differential photodetector in standard CMOS process for optical communications and interconnects

A fully differential transimpedance amplifier (TIA) with integrated differential photodetector in standard CMOS technologies has been realized for optical receivers in optical communications and optical interconnects. And a novel, fully differential photodetector aiming to convert the incident light into a pair of fully differential photo-generated currents and ensure the differential symmetry on circuit configuration and model has also been proposed for the differential TIA to achieve the fully differentia...     

基于角度不变的线阵推扫式CCD相机几何畸变在轨检校方法

对航天遥感相机进行在轨检校,是提高卫星产品质量,扩大产品应用效果的重要技术内容.文中研究了一种基于角度不变的线阵推扫式CCD相机几何畸变在轨检校方法.该方法利用相机外方元素对于相机视向量夹角角度影响比较小的原理,根据地面控制点和一级产品图像,求解相机视向量夹角,从中提取相机光学部件的畸变模型参数,从而实现相机内外方元素解耦.相机畸变模型采用一维3阶多项式,对焦距和主点等引起的低阶误差能够很好地吸收.该方法用在HJ-1A/B卫星的宽幅盖CCD相机几何畸变校正上,检校误差残留在2—6个像元,和参考数据精度相似,表明取得了很好的校正效果.     

A fully integrated 1‐in. AMOLED display using current feedback based on a five‐mask LTPS CMOS process

Abstract— In the past, a five‐mask LTPS CMOS process requiring only one single ion‐doping step was used. Based on that process, all necessary components for the realization of a fully integrated AMOLED display using a 3T1C current‐feedback pixel circuit has recently been developed. The integrated data driver is based on a newly developed LTPS operational amplifier, which does not require any compensation for Vth or mobility variations. Only one operational amplifier per column is used to perform digital‐to‐analog conversion as well as current control. In order to achieve high‐precision analog behavior, the operational amplifier is embedded in a switched capacitor network. In addition to circuit verification by simulation and analytic analysis, a 1‐in. fully integrated AMOLED demonstrator was successfully built. To the best of the authors' knowledge, this is the first implementation of a fully integrated AMOLED display with current feedback.     

Analysis and evaluation of a general camera model

A versatile General Camera Model, GCM, has been developed, and is described in detail. The model is general in the sense that it can capture both fisheye and conventional as well as catadioptric cameras in a unified framework. The camera model includes efficient handling of non-central cameras as well as compensations for decentring distortion. A novel way of analysing radial distortion functions of camera models leads to a straightforward improvement of conventional models with respect to generality, accuracy and simplicity. Different camera models are experimentally compared for two cameras with conventional and fisheye lenses, and the results show that the overall performance is favourable for the GCM.