Efficient Invariant Representations
International Journal of Computer Vision
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Real-Time Hand and Head Tracking for Virtual Environments Using Infrared Beacons
CAPTECH '98 Proceedings of the International Workshop on Modelling and Motion Capture Techniques for Virtual Environments
An Invitation to 3-D Vision: From Images to Geometric Models
An Invitation to 3-D Vision: From Images to Geometric Models
A convenient multicamera self-calibration for virtual environments
Presence: Teleoperators and Virtual Environments
D-Calib: Calibration Software for Multiple Cameras System
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Augmented Reality Using Projective Invariant Patterns
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
A novel optical tracking algorithm for point-based projective invariant marker patterns
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Editorial: Special Section on Virtual Reality in Brazil
Computers and Graphics
| Hi-index | 0.00 |
In this paper we present a method for the calibration of multiple cameras based on the extraction and use of the physical characteristics of a one-dimensional invariant pattern which is defined by four collinear markers. The advantages of this kind of pattern stand out in two key steps of the calibration process. In the initial step of camera calibration methods, related to sample points capture, the proposed method takes advantage of using a new technique for the capture and recognition of a robust sample of projective invariant patterns, which allows to capture simultaneously more than one invariant pattern in the tracking area and recognize each pattern individually as well as each marker that composes them. This process is executed in real time while capturing our sample of calibration points in the cameras of our system. This new feature allows to capture a more numerous and robust set of sample points than other patterns used for multi-camera calibration methods. In the last step of the calibration process, related to camera parameters' optimization, we explore the collinearity feature of the invariant pattern and add this feature in the camera parameters optimization model. This approach obtains better results in the computation of camera parameters. We present the results obtained with the calibration of two multi-camera systems using the proposed method and compare them with other methods from the literature.