Vision algorithms utilizing camera networks with a common field of view are becoming increasingly feasible and important. Calibration of such camera networks is a challenging and cumbersome task. The current approaches for calibration using planes or a known 3D target may not be feasible as these objects may not be simultaneously visible in all the cameras. In this paper, we present a new algorithm to calibrate cameras using occluding contours of spheres. In general, an occluding contour of a sphere projects to an ellipse in the image. Our algorithm uses the projection of the occluding contours of three spheres and solves for the intrinsic parameters and the locations of the spheres. The problem is formulated in the dual space and the parameters are solved for optimally and efficiently using semi-definite programming. The technique is flexible, accurate and easy to use. In addition, since the contour of a sphere is simultaneously visible in all the cameras, our approach can greatly simplify calibration of multiple cameras with a common field of view. Experimental results from computer simulated data and real world data, both for a single camera and multiple cameras, are presented.

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Camera calibration is a fundamental problem in computer vision and photogrammetry. We present a new algorithm to calibrate cameras using spheres. In general, an occluding contour of a sphere projects to an ellipse in the image. Our algorithm uses the projection of the occluding contours of three spheres and solves for the intrinsic parameters and the locations of the spheres. The problem is formulated in the dual space and the parameters are solved for optimally and efficiently using semi-definite programming. The technique is flexible, accurate and easy to use. In addition, it can be used to simultaneously calibrate multiple cameras with a common field of view. Experimental results from computer simulated data and real world data, both for a single camera and multiple cameras, are presented.

This paper addresses the problem of calibrating a pinhole camera from images of an isoceles trapezoid. Assuming a unit aspect ratio and zero skew, we introduce a novel and simple camera calibration approach. The key features of the proposed technique are its simplicity and the lack of need for 3D coordinate information about the calibrating object- i.e. the isosceles trapezoid. By ultilizing the symmetry of such trapezoid, we show that one can obtain both the internal and the external camera parameters. To demonstrate the effectiveness of the algorithm, we present the processing results on synthetic and real images, and compare our results to Zhang’s flexible calibration method. 1

Abstract. Robots perceiving their environment using cameras usually need a good representation of how the camera is aligned to the body and how the camera is rotated relative to the ground. This is especially important for bearing-based distance measurements. In this paper we show how to use reference objects to improve vision-based distance measurements to objects of unknown size. Several methods for different kinds of reference objects are introduced. These are objects of known size (like a ball), objects extending over the horizon (like goals and beacons), and objects with known shape on the ground (like field lines). We give a detailed description how to determine the rotation of the robot’s camera relative to the ground, provide an error-estimation for all methods and describe the experiments we performed on an Aibo robot.

To meet the demands of the many emerging multiple camera studio systems in entertainment content production, a novel wand-based system is presented for calibration of both intrinsic (focal length,lens distortion) and extrinsic (position, orientation) parameters of multiple cameras. Full metric calibration is obtained solely from observations of a wand comprising two visible markers at a known fixed distance. It is not necessary for all cameras to see the wand simultaneously, cameras may face each other, and have non-overlapping fields of view. High accuracy is achieved by using iterative bundle adjustment of tracked feature points across multiple views to refine calibration parameters until re-projection errors are minimised over the required measurement volume. The approach involves a new automatic initialisation procedure and novel application of bundle adjustment to refine calibration estimates. Evaluation of wand-calibration is performed using an eight-camera system. Results demonstrate a reprojection error of approximately 0.5 pixels rms and 3D reconstruction error of less than 2mm rms for a capture volume of 2x3x2m. Advantages of wand-based calibration over conventional chart-based calibration include time-efficient calibration of multiple camera systems and calibration of camera configurations without all cameras having to view the same objects or having overlapping fields of view. I.

We present a multicamera real-time 3D modeling system that aims at enabling new immersive and interactive environments. This system, called Grimage, allows to retrieve in real-time a 3D mesh of the observed scene as well as the associated textures. This information enables a strong visual presence of the user into virtual worlds. The 3D shape information is also used to compute collisions and reaction forces with virtual objects, enforcing the mechanical presence of the user in the virtual world. The innovation is a fully integrated system with both immersive and interactive capabilities. It embeds a parallel version of the EPVH modeling algorithm inside a distributed vision pipeline. It also adopts the hierarchical component approach of the FlowVR middleware to enforce software modularity and enable distributed executions. Results show high refresh rates and low latencies obtained by taking advantage of the I/O and computing resources of PC clusters. The applications we have developed demonstrate

In this paper, we describe how camera parameters and light source orientation can be recovered from two perspective views of a scene given only two vertical lines and their cast shadows. Compared to the traditional calibration methods that involve images of some precisely machined calibration pattern, our method uses new calibration objects: the vertical objects and their parallel shadow lines, which are common in natural environments. In addition to the benefit of increasing accessibility of the calibration objects, the proposed method is also especially useful in cases where only limited information is available. To demonstrate the accuracy and the applications of the proposed algorithm, we present results on both synthetic and real images. 1

This paper introduces a novel approach for solving the problem of camera calibration from spheres. By exploiting the relationship between the dual images of spheres and the dual image of the absolute conic (IAC), it is shown that the common pole and polar w.r.t. the conic images of 2 spheres are also the pole and polar w.r.t the IAC. This provides 2 constraints for estimating the IAC and hence allows a camera to be calibrated from an image of at least 3 spheres. Experimental results show the feasibility of the proposed approach. Index Terms Calibration, sphere, silhouette, surface of revolution (SOR).

Surveillance systems with multiple cameras have become increasingly important. The space relationship between cameras offers useful information for surveillance applications, such as object tracking or 3-D positioning. In this paper, we propose a visionbased approach to infer the relative positioning and orientation among multiple PTZ cameras. The relationship between the tilt angle of a PTZ camera and the 3D-to-2D coordinate transformation is built first. Then, the tilt angles and the altitudes of PTZ cameras are estimated based on the observation of some simple objects lying on a horizontal plane, with known intrinsic parameters of these PTZ cameras. With the estimated tilt angles and altitudes, the calibration of multiple cameras can be accomplished by comparing the back-projected world coordinates of a common vector in the 3-D space. The whole procedure does not need a huge amount of data and the computational load is light. Experimental results over real images have demonstrated the efficiency and feasibility of this approach. 1.