16,331 research outputs found

    Astrometry with the MCAO instrument MAD - An analysis of single-epoch data obtained in the layer-oriented mode

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    Context: Current instrument developments at the largest telescopes worldwide have provisions for Multi-Conjugated Adaptive Optics (MCAO) modules. The large field of view and more uniform correction provided by these systems is not only highly beneficial for photometric studies but also for astrometric analysis of, e.g., large dense clusters and exoplanet detection and characterization. The Multi-conjugated Adaptive optics Demonstrator (MAD) is the first such instrument and was temporarily installed and tested at the ESO/VLT in 2007. We analyzed two globular cluster data sets in terms of achievable astrometric precision. Data were obtained in the layer-oriented correction mode, one in full MCAO correction mode with two layers corrected (NGC 6388) and the other applying ground-layer correction only (47 Tuc). Aims: We aim at analyzing the first available MCAO imaging data in the layer-oriented mode obtained with the MAD instrument in terms of astrometric precision and stability. Methods: We calculated Strehl maps for each frame in both data sets. Distortion corrections were performed and the astrometric precision was analyzed by calculating mean stellar positions over all frames and by investigation of the positional residuals present in each frame after transformation to a master-coordinate-frame. Results: The mean positional precision for stars between K = 14-18 mag is ~1.2 mas in the full MCAO correction mode data of the cluster NGC 6388. The precision measured in the GLAO data (47 Tuc) reaches ~1.0 mas for stars corresponding to 2MASS K magnitudes between 9 and 12. The observations were such that stars in these magnitude ranges correspond to the same detector flux range. The jitter movement used to scan a larger field of view introduced additional distortions in the frames, leading to a degradation of the achievable precision.Comment: 11 pages, 6 figures, accepted for publication in Astronomy & Astrophysic

    On-barn pig weight estimation based on body measurements by structure-from-motion (SfM)

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    Information on the body shape of pigs is a key indicator to monitor their performance and health and to control or predict their market weight. Manual measurements are among the most common ways to obtain an indication of animal growth. However, this approach is laborious and difficult, and it may be stressful for both the pigs and the stockman. The present paper proposes the implementation of a Structure from Motion (SfM) photogrammetry approach as a new tool for on-barn animal reconstruction applications. This is possible also to new software tools allowing automatic estimation of camera parameters during the reconstruction process even without a preliminary calibration phase. An analysis on pig body 3D SfM characterization is here proposed, carried out under different conditions in terms of number of camera poses and animal movements. The work takes advantage of the total reconstructed surface as reference index to quantify the quality of the achieved 3D reconstruction, showing how as much as 80% of the total animal area can be characterized

    Correction of distortion for optimal image stacking in Wide Field Adaptive Optics: Application to GeMS data

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    The advent of Wide Field Adaptive Optics (WFAO) systems marks the beginning of a new era in high spatial resolution imaging. The newly commissioned Gemini South Multi-Conjugate Adaptive Optics System (GeMS) combined with the infrared camera Gemini South Adaptive Optics Imager (GSAOI), delivers quasi diffraction-limited images over a field of 2 arc-minutes across. However, despite this excellent performance, some variable residues still limit the quality of the analyses. In particular, distortions severely affect GSAOI and become a critical issue for high-precision astrometry and photometry. In this paper, we investigate an optimal way to correct for the distortion following an inverse problem approach. Formalism as well as applications on GeMS data are presented.Comment: 10 pages, 6 figure

    Pigment Melanin: Pattern for Iris Recognition

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    Recognition of iris based on Visible Light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, unavailable in Near-Infrared (NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive feature-code for each subject. An important question is how much the melanin patterns, extracted from VL, are independent of iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost the recognition performance. We have collected our own database (UTIRIS) consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of cromophores and improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on Instruments and Measurements, Volume 59, Issue number 4, April 201
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