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Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/16/2015 Terms of Use: http://spiedl.org/terms Abstract. Traditionally, most camera calibrations rely on a planar target with well-known marks. However, the localization error of the marks in the image is a source of inaccuracy. We propose the use of high-resolution digital displays as active calibration targets to obtain more accurate calibration results for all types of cameras. The display shows a series of coded patterns to generate correspondences between world points and image points. This has several advantages. No special calibration hardware is necessary because suitable displays are practically ubiquitious. The method is fully automatic, and no identification of marks is necessary. For a coding scheme based on phase shifting, the localization accuracy is approximately independent of the camera's focus settings. Most importantly, higher accuracy can be achieved compared to passive targets, such as printed checkerboards. A rigorous evaluation is performed to substantiate this claim. Our active target method is compared to standard calibrations using a checkerboard target. We perform camera, calibrations with different combinations of displays, cameras, and lenses, as well as with simulated images and find markedly lower reprojection errors when using active targets. For example, in a stereo reconstruction task, the accuracy of a system calibrated with an active target is five times better. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

Abstract. Single-Shot Structured Light is a well-known method for acquiring 3D surface data of moving scenes with simple and compact hardware setups. Some of the biggest challenges in these systems is their sensitivity to textured scenes, subsurface scattering and low-contrast illumination. Recently, a graphbased method has been proposed that largely eliminates these shortcomings. A key step in the graph-based pattern decoding algorithm is the estimation of color of local image regions which correspond to the vertex colors of the graph. In this work we propose a new method for estimating the color of a vertex based on belief propagation (BP). The BP framework allows the explicit inclusion of cues from neigboring vertices in the color estimation. This is especially beneficial for low-contrast input images. The augmented method is evaluated using typical lowquality real-world test sequences of the interior of a pig stomach. We demonstrate a significant improvement in robustness. The number of 3D data points generated increases by 30 to 50 percent over the plain decoding.

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