Feature points extraction of defocused images using deep learning for camera calibration

Measurement ◽  
2021 ◽  
pp. 110563
Author(s):  
Junzhou Huo ◽  
Zhichao Meng ◽  
Haidong Zhang ◽  
Shangqi Chen ◽  
Fan Yang
2021 ◽  
Vol 13 (11) ◽  
pp. 2208
Author(s):  
Yi Yang ◽  
Zongxu Pan ◽  
Yuxin Hu ◽  
Chibiao Ding

Ship detection is a significant and challenging task in remote sensing. At present, due to the faster speed and higher accuracy, the deep learning method has been widely applied in the field of ship detection. In ship detection, targets usually have the characteristics of arbitrary-oriented property and large aspect ratio. In order to take full advantage of these features to improve speed and accuracy on the base of deep learning methods, this article proposes an anchor-free method, which is referred as CPS-Det, on ship detection using rotatable bounding box. The main improvements of CPS-Det as well as the contributions of this article are as follows. First, an anchor-free based deep learning network was used to improve speed with fewer parameters. Second, an annotation method of oblique rectangular frame is proposed, which solves the problem that periodic angle and bounded coordinates in conjunction with the regression calculation can lead to the problem of loss anomalies. For the annotation scheme proposed in this paper, a scheme for calculating Angle Loss is proposed, which makes the loss function of angle near the boundary value more accurate and greatly improves the accuracy of angle prediction. Third, the centerness calculation of feature points is optimized in this article so that the center weight distribution of each point is suitable for the rotation detection. Finally, a scheme combining centerness and positive sample screening is proposed and its effectiveness in ship detection is proved. Experiments on remote sensing public dataset HRSC2016 show the effectiveness of our approach.


Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 421 ◽  
Author(s):  
Gwon An ◽  
Siyeong Lee ◽  
Min-Woo Seo ◽  
Kugjin Yun ◽  
Won-Sik Cheong ◽  
...  

In this paper, we propose a Charuco board-based omnidirectional camera calibration method to solve the problem of conventional methods requiring overly complicated calibration procedures. Specifically, the proposed method can easily and precisely provide two-dimensional and three-dimensional coordinates of patterned feature points by arranging the omnidirectional camera in the Charuco board-based cube structure. Then, using the coordinate information of the feature points, an intrinsic calibration of each camera constituting the omnidirectional camera can be performed by estimating the perspective projection matrix. Furthermore, without an additional calibration structure, an extrinsic calibration of each camera can be performed, even though only part of the calibration structure is included in the captured image. Compared to conventional methods, the proposed method exhibits increased reliability, because it does not require additional adjustments to the mirror angle or the positions of several pattern boards. Moreover, the proposed method calibrates independently, regardless of the number of cameras comprising the omnidirectional camera or the camera rig structure. In the experimental results, for the intrinsic parameters, the proposed method yielded an average reprojection error of 0.37 pixels, which was better than that of conventional methods. For the extrinsic parameters, the proposed method had a mean absolute error of 0.90° for rotation displacement and a mean absolute error of 1.32 mm for translation displacement.


2013 ◽  
Vol 427-429 ◽  
pp. 1939-1943 ◽  
Author(s):  
Qian Bian ◽  
Sui Yang Chen ◽  
Yang Chuan Liu

During camera calibration, the calibration pattern image is always skew. This brings much difficult to feature points sorting, which affects calibration accuracy. In this study, a rotation based sorting method is proposed. First, detect the skew angle accurately; then, transform the original coordinates to the rotated coordinates and establish the mapping relation; then, sort the rotated coordinates; finally, sort the original coordinates using the mapping relation. To verify the feasibility of this method, an experiment is carried out. The result shows that the rotation based sorting method can sort the feature points accurately at different skew angles. Its accuracy makes this method suitable for high accurate camera calibration.


2015 ◽  
Vol 741 ◽  
pp. 697-700 ◽  
Author(s):  
Li Lun Huang ◽  
Wen Guo Li ◽  
Qi Le Yang ◽  
Ying Chun Chen

The basic principles of camera calibration are first analyzed, and the method of camera calibrate based on 2D plane circular array is presented. The first process is the use of the canny edge detection operator, and get the edge coordinates of ellipse. Then the ellipse is fitted to obtain the center point of the ellipse, and the centre point coordinates of ellipse is used to regard the feature points to implement camera caliblation. Finally, Zhang Zhengyou's method is used to obtain internal and external parameters of camera. This calibration method can be used to calbration of robot system.


Author(s):  
V. V. Kniaz ◽  
L. Grodzitskiy ◽  
V. A. Knyaz

Abstract. Coded targets are physical optical markers that can be easily identified in an image. Their detection is a critical step in the process of camera calibration. A wide range of coded targets was developed to date. The targets differ in their decoding algorithms. The main limitation of the existing methods is low robustness to new backgrounds and illumination conditions. Modern deep learning recognition-based algorithms demonstrate exciting progress in object detection performance in low-light conditions or new environments. This paper is focused on the development of a new deep convolutional network for automatic detection and recognition of the coded targets and sub-pixel estimation of their centers.


2020 ◽  
Vol 32 (3) ◽  
pp. 1005
Author(s):  
Masahiko Minamoto ◽  
Shigeki Hori ◽  
Hideyuki Kobayashi ◽  
Toshihiro Kawase ◽  
Tetsuro Miyazaki ◽  
...  

2012 ◽  
Vol 472-475 ◽  
pp. 968-973
Author(s):  
Hong Ru Wang ◽  
Wen Ding

To improve accuracy of computer visual inspection in keyboard automatic assembly line, a new two-stage camera calibration method was presented. 2D circle array was used as calibration plate, and centers of the circles were taken as feature points. And feature point coordinates were extracted without human interference. The proposed camera calibration method was divided into two stages. First, lens distortion was neglected, internal and external parameters of the camera were obtained by modified camera calibration toolbox for MATLAB. Then, lens distortion was taken into account, and improved genetic algorithm (GA) was adopted to optimize camera parameters gotten in the first stage. Experiment results indicate the proposed method is feasible, and can meet with requirements of the given application.


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