Automated Coronal Hole Detection using Local Intensity Thresholding Techniques

We identify coronal holes using a histogram-based intensity thresholding technique and compare their properties to fast solar wind streams at three different points in the heliosphere. The thresholding technique was tested on EUV and X-ray images obtained using instruments onboard STEREO, SOHO and Hinode. The full-disk images were transformed into Lambert equal-area projection maps and partitioned into a series of overlapping sub-images from which local histograms were extracted. The histograms were used to determine the threshold for the low intensity regions, which were then classified as coronal holes or filaments using magnetograms from the SOHO/MDI. For all three instruments, the local thresholding algorithm was found to successfully determine coronal hole boundaries in a consistent manner. Coronal hole properties extracted using the segmentation algorithm were then compared with in situ measurements of the solar wind at 1 AU from ACE and STEREO. Our results indicate that flux tubes rooted in coronal holes expand super-radially within 1 AU and that larger (smaller) coronal holes result in longer (shorter) duration high-speed solar wind streams

Real-Time Hyperbola Recognition and Fitting in GPR Data

The problem of automatically recognising and fitting hyperbolae from Ground Penetrating Radar (GPR) images is addressed, and a novel technique computationally suitable for real time on-site application is proposed. After pre-processing of the input GPR images, a novel thresholding method is applied to separate the regions of interest from background. A novel column-connection clustering (C3) algorithm is then applied to separate the regions of interest from each other. Subsequently, a machine learnt model is applied to identify hyperbolic signatures from outputs of the C3 algorithm and a hyperbola is fitted to each such signature with an orthogonal distance hyperbola fitting algorithm. The novel clustering algorithm C3 is a central component of the proposed system, which enables the identification of hyperbolic signatures and hyperbola fitting. Only two features are used in the machine learning algorithm, which is easy to train using a small set of training data. An orthogonal distance hyperbola fitting algorithm for ‘south-opening’ hyperbolae is introduced in this work, which is more robust and accurate than algebraic hyperbola fitting algorithms. The proposed method can successfully recognise and fit hyperbolic signatures with intersections with others, hyperbolic signatures with distortions and incomplete hyperbolic signatures with one leg fully or largely missed. As an additional novel contribution, formulae to compute an initial ‘south-opening’ hyperbola directly from a set of given points are derived, which make the system more efficient. The parameters obtained by fitting hyperbolae to hyperbolic signatures are very important features, they can be used to estimate the location, size of the related target objects, and the average propagation velocity of the electromagnetic wave in the medium. The effectiveness of the proposed system is tested on both synthetic and real GPR data

Skellam shrinkage: Wavelet-based intensity estimation for inhomogeneous Poisson data

The ubiquity of integrating detectors in imaging and other applications implies that a variety of real-world data are well modeled as Poisson random variables whose means are in turn proportional to an underlying vector-valued signal of interest. In this article, we first show how the so-called Skellam distribution arises from the fact that Haar wavelet and filterbank transform coefficients corresponding to measurements of this type are distributed as sums and differences of Poisson counts. We then provide two main theorems on Skellam shrinkage, one showing the near-optimality of shrinkage in the Bayesian setting and the other providing for unbiased risk estimation in a frequentist context. These results serve to yield new estimators in the Haar transform domain, including an unbiased risk estimate for shrinkage of Haar-Fisz variance-stabilized data, along with accompanying low-complexity algorithms for inference. We conclude with a simulation study demonstrating the efficacy of our Skellam shrinkage estimators both for the standard univariate wavelet test functions as well as a variety of test images taken from the image processing literature, confirming that they offer substantial performance improvements over existing alternatives.Comment: 27 pages, 8 figures, slight formatting changes; submitted for publicatio

Detection thresholding using mutual information

In this paper, we introduce a novel non-parametric thresholding method that we term Mutual-Information Thresholding. In our approach, we choose the two detection thresholds for two input signals such that the mutual information between the thresholded signals is maximised. Two efficient algorithms implementing our idea are presented: one using dynamic programming to fully explore the quantised search space and the other method using the Simplex algorithm to perform gradient ascent to significantly speed up the search, under the assumption of surface convexity. We demonstrate the effectiveness of our approach in foreground detection (using multi-modal data) and as a component in a person detection system