Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
A linear algorithm for incremental digital display of circular arcs
Communications of the ACM
Camera-Based Calibration Techniques for Seamless Multiprojector Displays
IEEE Transactions on Visualization and Computer Graphics
Modern approaches to augmented reality
ACM SIGGRAPH 2006 Courses
A robust and dynamic scene geometry acquisition technique for a mobile projector-camera system
ACM SIGGRAPH ASIA 2009 Sketches
Charting the audience perceptions of projected 3D media installations
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
A depth cue method based on blurring effect in augmented reality
Proceedings of the 4th Augmented Human International Conference
ChESS - Quick and robust detection of chess-board features
Computer Vision and Image Understanding
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Projector-camera systems always need complicated geometry calibration to get a correct display result on nonplanar projection surface. Geometry registration of most calibration methods dealing with arbitrary surfaces is done by projecting a set of structure light patterns or by manually 3D modeling, which are both time-consuming. In this paper, we propose a robust checkerboard calibration pattern recognition method to help nonplanar surface geometry registration. By approximating the nonplanar surface to be composite of many planar quad patches, pixels mapping between the calibration camera and a projector can be got by projecting only one checkerboard calibration pattern recognized by our method. Compared with geometry registration with structure light or encoded points, which need project many images, our method can be more efficient. Our recognition method has two steps: corner detection and checkerboard pattern match. Checkerboard internal corners are defined as special conjunction points of four alternating dark and bright regions. A candidate corner's neighbor points within a rectangular or a circular window are treated as in different one-point-width layers. By processing the points layers in corner detection, we transform the 2D points distribution into 1D, which simplifies the regions amount counting and also improves the robustness against noises caused by deformation and complex illumination. After corner detection, the pre-known checkerboard grids rows and columns amounts are used to match and decide the right checkerboard corners from the results that have found. Regions boundary data produced during the corner detection also assist the matching process.