Deep Single Image Camera Calibration With Radial Distortion

López, Manuel Lucena; Marí, Roger; Gargallo, Pau; Kuang, Yubin; González-Jiménez, Javier; Haro, Gloria

doi:10.1109/cvpr.2019.01209

Cited by 64 publications

(45 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…erefore, it can be said that the detection accuracy and detection speed of the algorithm in this paper are due to the other two detection algorithms, using standard gauge blocks. After camera calibration [38,39], the actual size of pix corresponding to each pixel is 0.007291 mm, and the actual diameter D of the brake master cylinder compensation hole is calculated by the formula D � 2 * pix * n � 2 * 0.007291 * 48.105 � 0.70151. (20) Finally, using random Hough transform, gradient Hough transform, and the algorithm in this paper, the same master cylinder compensation hole size is detected 30 times, and the pixel size of each time is recorded to form the experimental data shown in Table 7.…”

Section: And Vote On Point C Imentioning

confidence: 99%

A New Method for Detecting Compensation Hole Parameters of Automobile Brake Master Cylinder Based on Machine Vision

Zhao

Ding

Cao

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

The machining accuracy of the compensation hole of the automobile brake master cylinder directly determines the safety of the automobile and the reliability of parking. How to detect the parameters of the compensation hole with high precision becomes a crucial issue. In this paper, by analyzing the principle of Hough transform detection technology and several optimization algorithms, a new method combining Zernike moment and improved gradient Hough transform is proposed to detect the circular hole parameters. The simulation experiment shows that the proposed algorithm satisfies 0.1 pixels in the coordinate detection of the center position, and the radius detection accuracy is 0.05 pixels, with fast detection speed and good robustness. Compared with the random Hough transform algorithm and the gradient Hough transform algorithm, the algorithm proposed in this paper has higher detection accuracy, faster detection speed, and better robustness, which meets the online detection accuracy requirements of the brake master cylinder compensation hole.

show abstract

Section: And Vote On Point C Imentioning

confidence: 99%

A New Method for Detecting Compensation Hole Parameters of Automobile Brake Master Cylinder Based on Machine Vision

Zhao

Ding

Cao

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…CNN deep-learning approaches such as Bogdan et al [21] and Lopez et al [22] cannot rectify the distortion samples with illumination changes, and certain higher distortion ranges cannot be handled with consistency. Additionally, deep GANs such as Liao, Kang et al [23] are used for generating corresponding rectified samples for a distorted image.…”

Section: Previous Workmentioning

confidence: 99%

Automatic Distortion Rectification of Wide-Angle Images Using Outlier Refinement for Streamlining Vision Tasks

Kakani

Kim

Lee

et al. 2020

Sensors

View full text Add to dashboard Cite

The study proposes an outlier refinement methodology for automatic distortion rectification of wide-angle and fish-eye lens camera models in the context of streamlining vision-based tasks. The line-members sets are estimated in a scene through accumulation of line candidates emerging from the same edge source. An iterative optimization with an outlier refinement scheme was applied to the loss value, to simultaneously remove the extremely curved outliers from the line-members set and update the robust line members as well as estimating the best-fit distortion parameters with lowest possible loss. The proposed algorithm was able to rectify the distortions of wide-angle and fish-eye cameras even in extreme conditions such as heavy illumination changes and severe lens distortions. Experiments were conducted using various evaluation metrics both at the pixel-level (image quality, edge stretching effects, pixel-point error) as well as higher-level use-cases (object detection, height estimation) with respect to real and synthetic data from publicly available, privately acquired sources. The performance evaluations of the proposed algorithm have been investigated using an ablation study on various datasets in correspondence to the significance analysis of the refinement scheme and loss function. Several quantitative and qualitative comparisons were carried out on the proposed approach against various self-calibration approaches.

show abstract

“…Blue to yellow means small to large values. Different from previous works of camera calibration that leverage image contents from a noise-free image (e.g., [56,16,32]), our method exploits (free from image contents, Figure 1 row 4 as the calibration cue to estimate camera parameters.…”

Section: Motivationmentioning

confidence: 99%

“…Recently, learning-based methods are proposed to deal with a single image in the wild. These methods solve for different components of calibration parameters, such as vanishing points [63] (combined with geometric based methods), FoV [56], horizon line [58] (to estimate the extrinsic rotation matrix), the radial distortion parameter [38], the extrinsic rotation matrix and FoV [16], or the extrinsic rotation matrix together with intrinsic parameters of FoV and radial distortion [32].…”

Section: Single Image Camera Calibrationmentioning

confidence: 99%

“…Following strategies adopted in learning-based methods for single reflection separation (e.g., [49,53]) and single image camera calibration (e.g., [32,56]), we synthesize our training data, but in a physical rule-based way. We randomly select 22082 images with a focal length between 24mm and 105mm from the FocalLens dataset [60] and divide these images into two sets with paired focal length.…”

Section: Assumptionmentioning

confidence: 99%

See 1 more Smart Citation

Single image reflection removal with camera calibration

Chen¹

View full text Add to dashboard Cite

Deep Single Image Camera Calibration With Radial Distortion

Cited by 64 publications

References 18 publications

A New Method for Detecting Compensation Hole Parameters of Automobile Brake Master Cylinder Based on Machine Vision

A New Method for Detecting Compensation Hole Parameters of Automobile Brake Master Cylinder Based on Machine Vision

Automatic Distortion Rectification of Wide-Angle Images Using Outlier Refinement for Streamlining Vision Tasks

Single image reflection removal with camera calibration

Contact Info

Product

Resources

About