Self-calibration of projective camera based on trajectory basis

Liu, Shigang; Peng, Yali; Sun, Zengguo; Wang, Xili

doi:10.1016/j.jocs.2018.12.002

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…By building a conditional random field, Optree‐idsia [37] selects a collection of nodes from the watershed merging tree to enforce structure consistency to segmentation outputs. Nevertheless, accurate EM image segmentation is still a challenging graph cut algorithm [42–44] for EM image segmentation methods are direct and short time‐consuming in the testing stage. However, those algorithms are complex and depend much on experts' knowledge.…”

Section: Related Workmentioning

confidence: 99%

DenseUNet: densely connected UNet for electron microscopy image segmentation

et al. 2020

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Section: Related Workmentioning

confidence: 99%

DenseUNet: densely connected UNet for electron microscopy image segmentation

et al. 2020

Self Cite

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“…Faugeras [ 5 ] and Maybank et al [ 6 ] proposed a method based on Kruppa’s equation to solve the camera parameters directly. Liu [ 7 ] proposed a linear self-calibration method for projection cameras based on the representing of deforming object by the trajectory basis. The camera self-calibration method is excellently flexible, but has low accuracy, low robustness, and requires solving nonlinear equations.…”

Section: Introductionmentioning

confidence: 99%

A Distortion Correction Method Based on Actual Camera Imaging Principles

Yin,

Zang,

et al. 2024

Sensors

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In the human–robot collaboration system, the high-precision distortion correction of the camera as an important sensor is a crucial prerequisite for accomplishing the task. The traditional correction process is to calculate the lens distortion with the camera model parameters or separately from the camera model. However, in the optimization process calculate with the camera model parameters, the mutual compensation between the parameters may lead to numerical instability, and the existing distortion correction methods separated from the camera model are difficult to ensure the accuracy of the correction. To address this problem, this study proposes a model-independent lens distortion correction method based on the image center area from the perspective of the actual camera lens distortion principle. The proposed method is based on the idea that the structured image preserves its ratios through perspective transformation, and uses the local image information in the central area of the image to correct the overall image. The experiments are verified from two cases of low distortion and high distortion under simulation and actual experiments. The experimental results show that the accuracy and stability of this method are better than other methods in training and testing results.

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“…These constraints are composed of Kruppa equations and are used to obtain the intrinsic parameters of the camera [15,16]. Faced with the difficulty of obtaining nonlinear equations, scholars have proposed various methods for self-calibration of cameras [17][18][19]. The biggest advantage of the camera self-calibration technique is its excellent flexibility.…”

Section: Introductionmentioning

confidence: 99%

A New Camera Calibration Technique for Serious Distortion

Huang

Zou

2022

Processes

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A new camera calibration technique based on serious distortion is proposed, which only requires the camera to observe the plane pattern in an arbitrary azimuth. It uses the geometrical imaging principle and radial distortion model to acquire radial lens distortion coefficient and the image coordinate (u0, v0), and then solves the linear equation aiming at the other parameters of the camera. This method has the following characteristics: Firstly, the position of the camera and the plane is arbitrary, and the technique needs only a single observation for plane pattern. Secondly, it is suitable for camera calibration with serious distortion. Thirdly, it does not need expensive ancillary equipment, accurate movement, or lots of photos observed from different orientations. Having been authenticated by computer emulation and actual experiment, the results of the proposed technique have proved to be satisfactory. The research has also paved a new way in camera calibration for further studies.

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Self-calibration of projective camera based on trajectory basis

Cited by 8 publications

References 24 publications

DenseUNet: densely connected UNet for electron microscopy image segmentation

DenseUNet: densely connected UNet for electron microscopy image segmentation

A Distortion Correction Method Based on Actual Camera Imaging Principles

A New Camera Calibration Technique for Serious Distortion

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