A single-image linear calibration method for camera

Zhuang, Zhitao; Zhao, Rujin; Liu, Enhai; Yan, Kun; Ma, Yuebo

doi:10.1016/j.measurement.2018.07.085

Cited by 30 publications

(9 citation statements)

References 20 publications

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“…The value of K can be found in the KITTI dataset. The transformed equation is expressed as equation (1), where is the inverse matrix of the internal parameters of the camera [20], [21].…”

Section: Methodsmentioning

confidence: 99%

Real-time Estimation of Road Surfaces using Fast Monocular Depth Estimation and Normal Vector Clustering

Cho

2021

JOIV : Int. J. Inform. Visualization

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Estimating a road surface or planes for applying AR(Augmented Reality) or an autonomous vehicle using a camera requires significant computation. Vision sensors have lower accuracy in distance measurement than other types of sensor, and have the difficulty that additional algorithms for estimating data must be included. However, using a camera has the advantage of being able to extract various information such as weather conditions, sign information, and road markings that are difficult to measure with other sensors. Various methods differing in sensor type and configuration have been applied. Many of the existing studies had generally researched by performing the depth estimation after the feature extraction. However, recent studies have suggested using deep learning to skip multiple processes and use a single DNN(Deep Neural Network). Also, a method using a limited single camera instead of a method using a plurality of sensors has been proposed. This paper presents a single-camera method that performs quickly and efficiently by employing a DNN to extract distance information using a single camera, and proposes a modified method for using a depth map to obtain real-time surface characteristics. First, a DNN is used to estimate the depth map, and then for quick operation, normal vector that can connect similar planes to depth is calculated, and a clustering method that can be connected is provided. An experiment is used to show the validity of our method, and to evaluate the calculation time.

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“…The value of K can be found in the KITTI dataset. The transformed equation is expressed as equation (1), where is the inverse matrix of the internal parameters of the camera [20], [21].…”

Section: Methodsmentioning

confidence: 99%

Real-time Estimation of Road Surfaces using Fast Monocular Depth Estimation and Normal Vector Clustering

Cho

2021

JOIV : Int. J. Inform. Visualization

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“…The camera was fixed on the C-arm throughout the experiment. The angle and height could be adjusted by the rotation of the C-arm to provide a better position [61,62]. The functions of the hardware in the system are shown in Fig.…”

Section: Hardware Composition Of the System Platformmentioning

confidence: 99%

Optimization of virtual and real registration technology based on augmented reality in a surgical navigation system

et al. 2020

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Background: The traditional navigation interface was intended only for two-dimensional observation by doctors; thus, this interface does not display the total spatial information for the lesion area. Surgical navigation systems have become essential tools that enable for doctors to accurately and safely perform complex operations. The image navigation interface is separated from the operating area, and the doctor needs to switch the field of vision between the screen and the patient's lesion area. In this paper, augmented reality (AR) technology was applied to spinal surgery to provide more intuitive information to surgeons. The accuracy of virtual and real registration was improved via research on AR technology. During the operation, the doctor could observe the AR image and the true shape of the internal spine through the skin. Methods: To improve the accuracy of virtual and real registration, a virtual and real registration technique based on an improved identification method and robotassisted method was proposed. The experimental method was optimized by using the improved identification method. X-ray images were used to verify the effectiveness of the puncture performed by the robot. Results: The final experimental results show that the average accuracy of the virtual and real registration based on the general identification method was 9.73 ± 0.46 mm (range 8.90-10.23 mm). The average accuracy of the virtual and real registration based on the improved identification method was 3.54 ± 0.13 mm (range 3.36-3.73 mm). Compared with the virtual and real registration based on the general identification method, the accuracy was improved by approximately 65%. The highest accuracy of the virtual and real registration based on the robot-assisted method was 2.39 mm. The accuracy was improved by approximately 28.5% based on the improved identification method. Conclusion: The experimental results show that the two optimized methods are highly very effective. The proposed AR navigation system has high accuracy and stability. This system may have value in future spinal surgeries.

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“…Therefore, the pinhole model is suitable for three-dimensional measurement based on the two-dimensional image. However, the pinhole model has perspective projection distortion [27][28][29][30][31]. To solve the influence of the projection distortion on the measurement, an oblique elliptic cone representing the actual imaging process is derived, and then a universal vision measurement model for the shaft diameter is established.…”

Section: Introductionmentioning

confidence: 99%

A Model of Diameter Measurement Based on the Machine Vision

et al. 2021

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If the shaft diameter can be measured in-situ during the finishing process, the closed-loop control of the shaft diameter processing process can be realized and the machining accuracy can be improved. Present work studies the measurement of shaft diameter with the structured light system composed of a laser linear light source and a camera. The shaft is a kind of part with rotationally symmetric structure. When the linear structured light irradiates the surface of the shaft, a light stripe will be formed, and the light stripe is a part of the ellipse. Therefore, the in-situ measurement of the shaft diameter can be realized by the light stripe and the rotational symmetry of the shaft. The measurement model of shaft diameter is established by the ellipse formed by the intersection of the light plane and the measured shaft surface. Firstly, in the camera coordinate system, normal vector of the light plane and the coordinates of the ellipse center are obtained by the calibration; then, the equation of oblique elliptic cone is established by taking the ellipse as the bottom and the optical center of the camera as the top. Next, the measurement model of shaft diameter is obtained by the established oblique elliptic cone equation and theoretical image plane equation. Finally, the accuracy of the measurement model of shaft diameter is tested by the checkerboard calibration plate and a lathe. The test results show that the measurement model of shaft diameter is correct, and when the shaft diameter is 36.162mm, the speed is 1250r/min, the maximum average measurement error is 0.019mm. The measurement accuracy meets the engineering requirement.

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A single-image linear calibration method for camera

Cited by 30 publications

References 20 publications

Real-time Estimation of Road Surfaces using Fast Monocular Depth Estimation and Normal Vector Clustering

Real-time Estimation of Road Surfaces using Fast Monocular Depth Estimation and Normal Vector Clustering

Optimization of virtual and real registration technology based on augmented reality in a surgical navigation system

A Model of Diameter Measurement Based on the Machine Vision

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