Estimation Of Multiple Local Orientations In Image Signals

Local orientation estimation can be posed as the problem of finding the minimum grey level variance axis within a local neighbourhood. In 2D image signals, this corresponds to the eigensystem analysis of a 22-tensor, which yields valid results for single orientations. We describe extensions to multiple overlaid orientations, which may be caused by transparent objects, crossings, bifurcations, corners etc. Multiple orientation detection is based on the eigensystem analysis of an appropriately extended tensor, yielding so-called mixed orientation parameters. These mixed orientation parameters can be regarded as another tensor built from the sought individual orientation parameters. We show how the mixed orientation tensor can be decomposed into the individual orientations by finding the roots of a polynomial. Applications are, e.g., in directional filtering and interpolation, feature extraction for corners or crossings, and signal separation

Source finding, parametrization and classification for the extragalactic Effelsberg-Bonn HI Survey

Context. Source extraction for large-scale HI surveys currently involves large amounts of manual labor. For data volumes expected from future HI surveys with upcoming facilities, this approach is not feasible any longer. Aims. We describe the implementation of a fully automated source finding, parametrization, and classification pipeline for the Effelsberg-Bonn HI Survey (EBHIS). With future radio astronomical facilities in mind, we want to explore the feasibility of a completely automated approach to source extraction for large-scale HI surveys. Methods. Source finding is implemented using wavelet denoising methods, which previous studies show to be a powerful tool, especially in the presence of data defects. For parametrization, we automate baseline fitting, mask optimization, and other tasks based on well-established algorithms, currently used interactively. For the classification of candidates, we implement an artificial neural network which is trained on a candidate set comprised of false positives from real data and simulated sources. Using simulated data, we perform a thorough analysis of the algorithms implemented. Results. We compare the results from our simulations to the parametrization accuracy of the HI Parkes All-Sky Survey (HIPASS) survey. Even though HIPASS is more sensitive than EBHIS in its current state, the parametrization accuracy and classification reliability match or surpass the manual approach used for HIPASS data.Comment: 13 Pages, 13 Figures, 1 Table, accepted for publication in A&