Efficient conic fitting with an analytical Polar-N-Direction geometric distance

Wu, Yihong; Wang, Haoren; Tang, Fulin; Wang, Zhiheng

doi:10.1016/j.patcog.2019.01.023

Cited by 17 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Especially for the case of Sampson-based methods, the visual observations corroborate that Sampson distance may only be appropriate for data with smaller noise levels as was mentioned in[12]. Therefore, careful attention must be given when integrating the Sampson distance into ellipse fitting cost functions, especially at larger noise levels (see Figure3of[34] as an example). The confocal hyperbola method on the other hand, is almost indistinguishable from the true geometric distance in the presented three arrangements (the confocal hyperbola, shown in red lies almost exactly underneath Chernov's distance shown in blue).…”

supporting

confidence: 53%

New confocal hyperbola-based ellipse fitting with applications to estimating parameters of mechanical pipes from point clouds

Maalek

Lichti

2021

Pattern Recognition

View full text Add to dashboard Cite

supporting

confidence: 53%

New confocal hyperbola-based ellipse fitting with applications to estimating parameters of mechanical pipes from point clouds

Maalek

Lichti

2021

Pattern Recognition

View full text Add to dashboard Cite

“…An additional limitation of these algorithms is that they cannot obtain successful results in images where the center circle does not appear complete. Alternatively, some authors [35] have proposed strategies using Least Squares Fitting (LSF) methods [36]. However, they are also too sensitive to the presence of data that do not belong to the ellipse.…”

Section: Line Mark Detectionmentioning

confidence: 99%

Soccer line mark segmentation and classification with stochastic watershed transform

Berjón¹,

Cuevas²,

García³

2021

Preprint

View full text Add to dashboard Cite

Augmented reality applications are beginning to change the way sports are broadcast, providing richer experiences and valuable insights to fans. The first step of augmented reality systems is camera calibration, possibly based on detecting the line markings of the field of play. Most existing proposals for line detection rely on edge detection and Hough transform, but optical distortion and extraneous edges cause inaccurate or spurious detections of line markings. We propose a novel strategy to automatically and accurately segment line markings based on a stochastic watershed transform that is robust to optical distortions, since it makes no assumptions about line straightness, and is unaffected by the presence of players or the ball in the field of play. Firstly, the playing field as a whole is segmented completely eliminating the stands and perimeter boards. Then the line markings are extracted.The strategy has been tested on a new and public database composed by 60 annotated images from matches in five stadiums. The results obtained have proven that the proposed segmentation algorithm allows successful and precise detection of most line mark pixels.

show abstract

“…A circle is imaged as a conic by a perspective camera. The conic from our markers is fitted by a polar-n-direction geometric distance method [28]. Then, the representation matrix defined in (1) is obtained.…”

Section: Designed Markersmentioning

confidence: 99%

“…where π is denoted as the camera model. If K(R, t) are accurate and the fitted conics C on the image plane have no noise, the distance [28] between p and C, denoted as d 2 (p, C), is zero. Otherwise, d 2 (p, C) isn't.…”

Section: Camera Pose Tracking With Nonlinear Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Circle-Based Camera Pose Tracking Free of PnP

Tang¹,

Wu²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Camera pose tracking attracts much interest both from academic and industrial communities, of which the methods based on planar markers are easy to be implemented. However, most of the existing methods need to identify multiple points in the marker images for matching to space points. Then, PnP and RANSAC are used to compute the camera pose. If cameras move fast or are far away from markers, matching is easy to generate errors and even RANSAC cannot remove incorrect matching. Then, the result by PnP cannot have good performance. To solve this problem, we design circular markers and represent 6D camera pose analytically and unifiedly as very concise forms from each of the marker by projective invariance. Afterwards, the pose is further optimized by a proposed nonlinear cost function based on a polar-n-direction geometric distance. The method is from imaged circle edges and without PnP/RANSAC, making pose tracking robust and accurate. Experimental results show that the proposed method outperforms the state of the arts in terms of noise, blur, and distance from camera to marker. Simultaneously, it can still run at about 100 FPS on a consumer computer with only CPU.

show abstract

Efficient conic fitting with an analytical Polar-N-Direction geometric distance

Cited by 17 publications

References 39 publications

New confocal hyperbola-based ellipse fitting with applications to estimating parameters of mechanical pipes from point clouds

New confocal hyperbola-based ellipse fitting with applications to estimating parameters of mechanical pipes from point clouds

Soccer line mark segmentation and classification with stochastic watershed transform

Efficient Circle-Based Camera Pose Tracking Free of PnP

Contact Info

Product

Resources

About