Detection of concentric circles for camera calibration

Jiang, Guang; Quan, Long

doi:10.1109/iccv.2005.73

Cited by 48 publications

(7 citation statements)

References 16 publications

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“…The second type of method is dependent on image cues, which include vanishing points [5,10,19], coplanar circles [7], concentric circles [17], etc. Li et al [19] obtain vanishing points by seeking peaks in the histogram of intersection points of image line segments, which demands scores of straight lines in the scene, hence technically needs to meet the Manhattan world assumption [9].…”

Section: Related Workmentioning

confidence: 99%

Camera focal length from distances in a single image

Lin

et al. 2021

Vis Comput

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Camera focal length estimation from a single image is of great importance for many computer vision tasks. Unfortunately, previous methods cannot achieve satisfactory accuracy yet. This paper proposes a focal length estimation approach based on distances among four points in the scene of a single image. The issue is cast into a nonlinear optimization by adapting a standard pinhole camera model under the constraints of distance information. Multiple algorithms are employed to solve the optimization, and the best solution is then regarded as the final solution. Experimental results show that our method is able to obtain a more accurate focal length than some state of the art in a single image setting. In addition, we provide some simple application examples and show the intuitive effects of focal length estimation errors. We also demonstrate experimentally that distance information has an improved meaning for the solution of the focal length.

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Section: Related Workmentioning

confidence: 99%

Camera focal length from distances in a single image

Lin

et al. 2021

Vis Comput

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“…Detection of patterns in outdoor settings is a challenging task that has deserved research attention in multiple fields, from face recognition to calibration patterns located under irregular lighting conditions [5,17,30]. Detection of concentric regions is another robust approach proposed for camera calibration in outdoor environments [16] as are 2D markers [19,20]. However, little attention has been given to the problem of locating optical markers (not just for camera calibration) in outdoor settings, where lighting may vary not only in intensity but also color and global histogram distribution.…”

Section: Related Workmentioning

confidence: 99%

“…With invariance to lighting conditions in mind and considering that GPU processing with OpenCL is a very efficient tool to deal with color images, no binarization [5] or seed point usage [16] is necessary. Instead, the proposed methodology groups together all pixels of contiguous color regions compute properties from these pixels and draw higher level conclusions from this information.…”

Section: Identification Of Contiguous Color Regionsmentioning

confidence: 99%

A robust methodology for outdoor optical mark recognition

Andrade

Trabasso

Eguti

et al. 2017

J Braz. Soc. Mech. Sci. Eng.

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Outdoor optical mark recognition is an extremely useful tool for recognition of large industrial equipment and application of computer vision-based systems for tracking and positioning. However, current algorithms rely on thresholding and corner detection to identify checkerboard-like patterns, which is not appropriate for non-uniform lighting conditions. This paper presents a robust methodology to identify optical markers in outdoor environments. A GPU-based region filling algorithm automatically detects all contiguous color regions without computing seed points. Post-processing steps extract highlevel information from these regions. Analysis of identified contiguous color region allows simultaneous identification of all checkerboard and targets (concentric regions) in the scene. Analysis of variance demonstrates that the proposed methodology is robust to lighting, environment, perspective, and occlusion. Tests indicate that precision and recall for checkerboard and target identification in outdoor conditions are expected to be above 97%. The parallel algorithm implementation using OpenCL yields better results and is two times faster than previous region filling algorithms, taking about 0.6 s to process a full-HD picture using modern hardware Keywords Computer vision Á Optical mark recognition Á OpenCL Á Parallel processing Á Design of experiments Á Heterogeneous computing

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“…Then, from the 1D objects in a single image, the basic constraint for camera calibration is derived so that the camera calibration problem can be solved. 3) Calibration based on 2D calibration objects [12], [13]:…”

Section: Introductionmentioning

confidence: 99%

Camera Calibration Using Projective Invariants of Sphere Images

2020

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Two novel linear camera calibration algorithms were investigated in this study. In general, an occluding contour circle of a sphere and the optical centre of a camera form a right circular cone, and the generalised eigenvectors of two right circular cones encode an infinity point that lies on two support planes containing the contour circle. In the image plane, a vanishing line can be determined by connecting two vanishing points. Furthermore, the direction of the diameter passing through the infinity point is orthogonal to the direction of its conjugate diameter; thus, a set of orthogonal vanishing points can be computed. The imaged circular points or orthogonal vanishing points provide constraints that enable the intrinsic parameters to be recovered completely. The results of simulations and comparison data obtained from actual experiments confirm the effectiveness and feasibility of the proposed algorithms. INDEX TERMS Camera calibration, conjugate diameters, generalized eigenvalue, right circular cone, vanishing point.

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Detection of concentric circles for camera calibration

Cited by 48 publications

References 16 publications

Camera focal length from distances in a single image

Camera focal length from distances in a single image

A robust methodology for outdoor optical mark recognition

Camera Calibration Using Projective Invariants of Sphere Images

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