Results derived from data obtained by Wilkinson Microwave Anisotropy Probe (WMAP) are extensively used in many areas of physics. It has been claimed recently that the published WMAP calibrated data and maps might be in question because of an undocumented timing offset in the official processing pipeline [The origin of the WMAP quadrupole, Hao Liu, Shao-Lin Xiong, Ti-Pei Li]. This timing error was shown to induce a quadrupole pattern in the final maps that is very similar to the officially published quadrupole mode. It is clear that a timing offset at the map-making stage will strongly affect the quadrupole scale, since the map-making in [The origin of the WMAP quadrupole, Hao Liu, Shao-Lin Xiong, Ti-Pei Li] was based on the official WMAP calibrated TOD. But there is also a possibility that the calibration process itself could be affected as well and we test this here. In this work we approximately reproduce the original dipole-based iterative calibration procedure to produce a calibrated data set starting from raw uncalibrated data. Using the calibrated data we generate a set of sky maps that we compare to the officially released maps and note some differences between our and official results. We also investigate the effects of various timing offsets introduced in the calibration stage on the final products. We find that a timing offset in the calibration process has little effect on the calibrated data and induced quadrupole.
Before you start working with photo images on your computer (or even view them), you should really make sure that your video subsystem is capable and is displaying things correctly. These pages will assist you in adjusting your monitor by providing various test patterns. They also give a brief introduction to issues involved in calibrating a monitor
Changes in measured image irradiance have many physical causes and are the primary cue for several visual processes, such as edge detection and shape from shading. Using physical models for charged-coupled device (CCD) video cameras and material reflectance, we quantify the variation in digitized pixel values that is due to sensor noise and scene variation. This analysis forms the basis of algorithms for camera characterization and calibration and for scene description. Specifically, algorithms are developed for estimating the parameters of camera noise and for calibrating a camera to remove the effects of fixed pattern nonuniformity and spatial variation in dark current. While these techniques have many potential uses, we describe in particular how they can be used to estimate a measure of scene variation. This measure is independent of image irradiance and can be used to identify a surface from a single sensor band over a range of situations.
This is a release of a Camera Calibration Toolbox for Matlab® with a complete documentation. This document may also be used as a tutorial on camera calibration since it includes general information about calibration, references and related links.
Data from occasional radiosonde campaigns and routine laboratory lamp measurements are utilized. A method has been devised to ensure stable, long-term calibration of Raman lidar measurements that are used to determine the altitude-dependent mixing ratio of water vapor in the upper tropo
PFScalibration package provides an implementation of the Robertson et al. 2003 method for the photometric calibration of cameras and for the recovery of high dynamic range (HDR) images from the set of low dynamic range (LDR) exposures.
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