Camera Self-Calibration in Computer Vision with Precise Estimation of Initial Parameters

A key problem that plagues camera self-calibration, namely that the classical self-calibration algorithms are very sensitive to the initial values of the camera intrinsic parameters, is analyzed and a practical solution is provided. The effect of the camera intrinsic parameters, mainly the principal point and the skew factor is first discussed. Then a practical method via a controlled motion of the camera is introduced so as to obtain an accurate estimation of these parameters. Feasibility of this approach is illustrated by carrying out comprehensive experiments using synthetic data as well as real image sequences. Unreasonable initial values can often make self-calibration impossible, yet a precise initialization guarantees a better and successful reconstruction. Trying to obtain a more reasonable initialization is worthwhile the effort in camera self-calibration.

A Self-calibration Bundle Adjustment Algorithm Based on Block Matrix Cholesky Decomposition Technology

In this study,the problem of bundle adjustment was revisited,and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment.The innovation points are reflected in the following aspects:①The proposed algorithm is not dependent on the Schur complement,and the calculation process is simple and clear;②The complexities of time and space tend to O(n)in the context of world point number is far greater than that of images and cameras,so the calculation magnitude and memory consumption can be reduced significantly;③The proposed algorithm can carry out self-calibration bundle adjustment in single-camera,multi-camera,and variable-camera modes;④Some measures are employed to improve the optimization effects.Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness,and it has a strong adaptability as well,because the optimized results are accurate and robust even if the initial values have large deviations from the truth.This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry,computer vision and robotics.

Effective Self-calibration for Camera Parameters and Hand-eye Geometry Based on Two Feature Points Motions

A novel and effective self-calibration approach for robot vision is presented, which can effectively estimate both the camera intrinsic parameters and the hand-eye transformation at the same time. The proposed calibration procedure is based on two arbitrary feature points of the environment, and three pure translational motions and two rotational motions of robot endeffector are needed. New linear solution equations are deduced, and the calibration parameters are finally solved accurately and effectively. The proposed algorithm has been verified by simulated data with different noise and disturbance. Because of the need of fewer feature points and robot motions, the proposed method greatly improves the efficiency and practicality of the calibration procedure. © 2017 Chinese Association of Automation.

Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

The widespread availability of digital multimedia data has led to a new challenge in digital forensics.Traditional source camera identification algorithms usually rely on various traces in the capturing process.However,these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms.Convolutional Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices.However,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall differences.In this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these issues.The proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature representation.This representation is then fed into a subsequent camera fingerprint classification network.Building upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone network.Furthermore,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

Ultrafast pulse-dilation framing camera and its application for time-resolved X-ray diagnostic

An ultrafast framing camera with a pulse-dilation device,a microchannel plate(MCP)imager,and an electronic imaging system were reported.The camera achieved a temporal resolution of 10 ps by using a pulse-dilation device and gated MCP imager,and a spatial resolution of 100μm by using an electronic imaging system comprising combined magnetic lenses.The spatial resolution characteristics of the camera were studied both theoretically and experimentally.The results showed that the camera with combined magnetic lenses reduced the field curvature and acquired a larger working area.A working area with a diameter of 53 mm was created by applying four magnetic lenses to the camera.Furthermore,the camera was used to detect the X-rays produced by the laser-targeting device.The diagnostic results indicated that the width of the X-ray pulse was approximately 18 ps.

Monocular Distance Estimated Based on PTZ Camera

This paper introduces an intelligent computational approach for extracting salient objects fromimages and estimatingtheir distance information with PTZ (Pan-Tilt-Zoom) cameras. PTZ cameras have found wide applications innumerous public places, serving various purposes such as public securitymanagement, natural disastermonitoring,and crisis alarms, particularly with the rapid development of Artificial Intelligence and global infrastructuralprojects. In this paper, we combine Gauss optical principles with the PTZ camera’s capabilities of horizontal andpitch rotation, as well as optical zoom, to estimate the distance of the object.We present a novel monocular objectdistance estimation model based on the Focal Length-Target Pixel Size (FLTPS) relationship, achieving an accuracyrate of over 95% for objects within a 5 km range. The salient object extraction is achieved through a simplifiedconvolution kernel and the utilization of the object’s RGB features, which offer significantly faster computingspeeds compared to Convolutional Neural Networks (CNNs). Additionally, we introduce the dark channel beforethe fog removal algorithm, resulting in a 20 dB increase in image definition, which significantly benefits distanceestimation. Our system offers the advantages of stability and low device load, making it an asset for public securityaffairs and providing a reference point for future developments in surveillance hardware.

Automatic Miscalibration Detection and Correction of LiDAR and Camera Using Motion Cues

This paper aims to develop an automatic miscalibration detection and correction framework to maintain accurate calibration of LiDAR and camera for autonomous vehicle after the sensor drift.First,a monitoring algorithm that can continuously detect the miscalibration in each frame is designed,leveraging the rotational motion each individual sensor observes.Then,as sensor drift occurs,the projection constraints between visual feature points and LiDAR 3-D points are used to compute the scaled camera motion,which is further utilized to align the drifted LiDAR scan with the camera image.Finally,the proposed method is sufficiently compared with two representative approaches in the online experiments with varying levels of random drift,then the method is further extended to the offline calibration experiment and is demonstrated by a comparison with two existing benchmark methods.

Imaging simulation and analysis of attitude jitter effect on topographic mapping for lunar orbiter stereo optical cameras

The geometric accuracy of topographic mapping with high-resolution remote sensing images is inevita-bly affected by the orbiter attitude jitter.Therefore,it is necessary to conduct preliminary research on the stereo mapping camera equipped on lunar orbiter before launching.In this work,an imaging simulation method consid-ering the attitude jitter is presented.The impact analysis of different attitude jitter on terrain undulation is conduct-ed by simulating jitter at three attitude angles,respectively.The proposed simulation method is based on the rigor-ous sensor model,using the lunar digital elevation model(DEM)and orthoimage as reference data.The orbit and attitude of the lunar stereo mapping camera are simulated while considering the attitude jitter.Two-dimensional simulated stereo images are generated according to the position and attitude of the orbiter in a given orbit.Experi-mental analyses were conducted by the DEM with the simulated stereo image.The simulation imaging results demonstrate that the proposed method can ensure imaging efficiency without losing the accuracy of topographic mapping.The effect of attitude jitter on the stereo mapping accuracy of the simulated images was analyzed through a DEM comparison.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

Anger-camera型中子探测器位置分辨性能研究

本文描述用于中国散裂中子源(CSNS)谱仪上,基于Anger-camera位置重建原理的闪烁体中子探测器样机位置分辨性能研究。在BL20中子测试束线上完成了探测器关键器件性能测试,包括硅光电倍增管(Silicon Photon Multiplier Tube, SiPM)阵列位置重建能力测试和耐辐照特性研究,同时对不同光导厚度下探测器位置分辨能力开展测试,发现基于SiPM阵列读出的Anger-camera型闪烁体中子探测器采用300μm的^(6)LiF/ZnS(Ag)闪烁屏和5 mm空气作为光导,其位置分辨最佳可达到0.6 mm。该探测器样机后期可通过大面积阵列拼接,实现中子衍射实验对大面积高位置分辨探测器的需求,其成功研制为加快谱仪建设提供有力的技术支撑。

Blackmagic Camera 8.5公测版更新发布

2023年12月,Blackmagic Camera8.5公测版正式发布。安装更新后,为Blackmagic URSA Broadcast G2添加了Blackmagic Cloud工作流程支持。可以从摄影机的新媒体池向Blackmagic Cloud上传片段,也可以直接上传到DaVinci Resolve项目。

Blackmagic Camera 8.5.1正式发布

针对Blackmagic URSA Broadcast G2的Blackmagic Camera 8.5.1版本,提供了Blackmagic Cloud流程支持,用户可从摄影机的新媒体池向Blackmagic Cloud上传片段,也可以直接上传到Da Vinci Resolve项目。新添加的同时代理记录功能加上Blackmagic RAW或ProRes能够助力影像迅速送进新闻演播室或剪辑室,提高工作流程的效率。

Pocket Cinema Camera系列和全画幅6K摄影机加入推流等新功能

Blackmagic Design最新发布的摄影机软件Blackmagic Camera 8.6公共测试版为Blackmagic Pocket Cinema Camera和Cinema Camera 6K机型增加了云流程支持,让用户可以将素材从摄影机媒体池上传到Blackmagic Cloud,甚至直接上传到DaVinci Resolve项目中。并为两款机型的USB-C接口增加了网络摄像头功能,即可把摄影机连接到电脑,并使用任何主流推流软件或平台以全高清1080P质量推流直播。

Blackmagic Camera iOS 2.1版软件更新

Blackmagic Camera iOS 2.1版软件更新,为Blackmagic Camera结合使用iPhone 16最新的相机控制功能添加了支持,可以通过单击或双击将其打开。还可以通过控制中心、锁定屏幕或操作按钮打开Blackmagic Camera,获得更多快速进入Blackmagic Camera并开始录制的方式。此外,可以使用iPhone 16内置的相机控制更轻松地导航显示并调整变焦、曝光、对焦、ISO和选择LUT等功能。

安卓版Blackmagic Camera发布

Blackmagic Design日前发布了安卓版Blackmagic Camera,将大幅改进拍摄效果,拍摄成果可用于影视制作。安卓版Blackmagic Camera采用和Blackmagic Design获奖摄影机一样的操作界面,直观易懂且对用户友好,可提供如同使用专业级数字电影摄影机一般的操控体验。通过简单的手指点按操作,就可以调整帧率、快门角度、白平衡以及ISO等设置。同时,还支持使用行业标准文件格式直接录制到Blackmagic Cloud,上至8K。

A Fractional-N CMOS DPLL with Self-Calibration

A digital phase-locked loop （DPLL） based on a new digital phase-frequency detector is presented. The self-calibration technique is employed to acquire wide lock range,low jitter, and fast acquisition. The DPLL works from 60 to 600MHz at a supply voltage of 1.8V. It also features a fraetional-N synthesizer with digital 2nd-order sigma-delta noise shaping, which can achieve a short lock time,a high frequency resolution,and an improved phase-noise spectrum. The DPLL has been implemented in SMIC 0. 18μm 1.8V 1P6M CMOS technology. The peak-to-peak jitter is less than 0. 8% of the output clock period and the lock time is less than 150 times of the reference clock period after the pre-divider.

Capacitor self-calibration technique used in time-interleaved successive approximation ADC

A capacitor self-calibration circuit used in a successive approximation analog-to-digital converter （SA-ADC） is presented. This capacitor self-calibration circuit can calibrate erroneous data and work with the ADC by adding an additional clock period. This circuit is used in a 10 bit 32 Msample/s time-interleaved SA- ADC. The chip is implemented with Chart 0. 25 μm 2. 5 V process and totally occupies an area of 1.4 mm× 1.3 mm. After calibration, the simulated signal-to-noise ratio （SNR） is 59. 586 1 dB and the spurious-free dynamic range （SFDR） is 70. 246 dB at 32 MHz. The measured signal-to-noise and distortion ratio （SINAD） is 44. 82 dB and the SFDR is 63. 760 4 dB when the ADC samples a 5.8 MHz sinusoid wave.

Self-calibration and Compensation of Setting Errors for Surface Profile Measurement of a Microstructured Roll Workpiece

Microstructured roll workpieces have been widely used as functional components in the precision industries. Current researches on quality control have focused on surface profile measurement of microstructured roll workpieces, and types of measurement systems and measurement methods have been developed. However, low measurement efficiency and low measurement accuracy caused by setting errors are the common disadvantages for surface profile measurement of microstructured roll workpieces. In order to shorten the measurement time and enhance the measurement accuracy, a method for self-calibration and compensation of setting errors is proposed for surface profile measurement of microstructured roll workpieces. A measurement system is constructed for the measurement, in which a precision spindle is employed to rotate the roll workpiece and an air-bearing displacement sensor with a micro-stylus probe is employed to scan the microstructured surface of the roll workpiece. The resolution of the displacement sensor is 0.14 nm and that of the rotary encoder of the spindle was 0.15r~. Geometrical and mathematical models are established for analyzing the influences of the setting errors of the roll workpiece and the displacement sensor with respect to the axis of the spindle, including the eccentric error of the roll workpiece, the offset error of the sensor axis and the zero point error of the sensor output. Measurement experiments are carded out on a roll workpiece on which periodic microstructures are a period of 133 i^m along the circumferential direction. Experimental results demonstrate the feasibility of the self-compensation method. The proposed method can be used to detect and compensate the setting errors without using any additional accurate artifact.

Integration of Tracer Test Data to Refine Geostatistical Hydraulic Conductivity Fields Using Sequential Self-Calibration Method

On the basis of local measurements of hydraulic conductivity, geostatistical methods have been found to be useful in heterogeneity characterization of a hydraulic conductivity field on a regional scale. However, the methods are not suited to directly integrate dynamic production data, such as, hydraulic head and solute concentration, into the study of conductivity distribution. These data, which record the flow and transport processes in the medium, are closely related to the spatial distribution of hydraulic conductivity. In this study, a three-dimensional gradient-based inverse method--the sequential self-calibration （SSC） method--is developed to calibrate a hydraulic conductivity field, initially generated by a geostatistical simulation method, conditioned on tracer test results. The SSC method can honor both local hydraulic conductivity measurements and tracer test data. The mismatch between the simulated hydraulic conductivity field and the reference true one, measured by its mean square error （MSE）, is reduced through the SSC conditional study. In comparison with the unconditional results, the SSC conditional study creates the mean breakthrough curve much closer to the reference true curve, and significantly reduces the prediction uncertainty of the solute transport in the observed locations. Further, the reduction of uncertainty is spatially dependent, which indicates that good locations, geological structure, and boundary conditions will affect the efficiency of the SSC study results.

New self-calibration approach to space robots based on hand-eye vision

To overcome the influence of on-orbit extreme temperature environment on the tool pose(position and orientation) accuracy of a space robot,a new self-calibration method based on a measurement camera(hand-eye vision) attached to its end-effector was presented.Using the relative pose errors between the two adjacent calibration positions of the space robot,the cost function of the calibration was built,which was different from the conventional calibration method.The particle swarm optimization algorithm(PSO) was used to optimize the function to realize the geometrical parameter identification of the space robot.The above calibration method was carried out through self-calibration simulation of a six-DOF space robot whose end-effector was equipped with hand-eye vision.The results showed that after calibration there was a significant improvement of tool pose accuracy in a set of independent reference positions,which verified the feasibility of the method.At the same time,because it was unnecessary for this method to know the transformation matrix from the robot base to the calibration plate,it reduced the complexity of calibration model and shortened the error propagation chain,which benefited to improve the calibration accuracy.