Automated detection of extended sources in radio maps: progress from the SCORPIO survey

Automated source extraction and parameterization represents a crucial challenge for the next-generation radio interferometer surveys, such as those performed with the Square Kilometre Array (SKA) and its precursors. In this paper we present a new algorithm, dubbed CAESAR (Compact And Extended Source Automated Recognition), to detect and parametrize extended sources in radio interferometric maps. It is based on a pre-filtering stage, allowing image denoising, compact source suppression and enhancement of diffuse emission, followed by an adaptive superpixel clustering stage for final source segmentation. A parameterization stage provides source flux information and a wide range of morphology estimators for post-processing analysis. We developed CAESAR in a modular software library, including also different methods for local background estimation and image filtering, along with alternative algorithms for both compact and diffuse source extraction. The method was applied to real radio continuum data collected at the Australian Telescope Compact Array (ATCA) within the SCORPIO project, a pathfinder of the ASKAP-EMU survey. The source reconstruction capabilities were studied over different test fields in the presence of compact sources, imaging artefacts and diffuse emission from the Galactic plane and compared with existing algorithms. When compared to a human-driven analysis, the designed algorithm was found capable of detecting known target sources and regions of diffuse emission, outperforming alternative approaches over the considered fields.Comment: 15 pages, 9 figure

How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging

We present the results of applying new object classification techniques to difference images in the context of the Nearby Supernova Factory supernova search. Most current supernova searches subtract reference images from new images, identify objects in these difference images, and apply simple threshold cuts on parameters such as statistical significance, shape, and motion to reject objects such as cosmic rays, asteroids, and subtraction artifacts. Although most static objects subtract cleanly, even a very low false positive detection rate can lead to hundreds of non-supernova candidates which must be vetted by human inspection before triggering additional followup. In comparison to simple threshold cuts, more sophisticated methods such as Boosted Decision Trees, Random Forests, and Support Vector Machines provide dramatically better object discrimination. At the Nearby Supernova Factory, we reduced the number of non-supernova candidates by a factor of 10 while increasing our supernova identification efficiency. Methods such as these will be crucial for maintaining a reasonable false positive rate in the automated transient alert pipelines of upcoming projects such as PanSTARRS and LSST.Comment: 25 pages; 6 figures; submitted to Ap

The Application of Metadata Standards to Multimedia in Museums

This paper first describes the application of a multi-level indexing approach, based on Dublin Core extensions and the Resource Description Framework (RDF), to a typical museum video. The advantages and disadvantages of this approach are discussed in the context of the requirements of the proposed MPEG-7 ("Multimedia Content Description Interface") standard. The work on SMIL (Synchronized Multimedia Integration Language) by the W3C SYMM working group is then described. Suggestions for how this work can be applied to video metadata are made. Finally a hybrid approach is proposed based on the combined use of Dublin Core and the currently undefined MPEG-7 standard within the RDF which will provide a solution to the problem of satisfying widely differing user requirements

Quantitative assessment of the discrimination potential of class and randomly acquired characteristics for crime scene quality shoeprints

Footwear evidence has tremendous forensic value; it can focus a criminal investigation, link suspects to scenes, help reconstruct a series of events, or otherwise provide information vital to the successful resolution of a case. When considering the specific utility of a linkage, the strength of the connection between the source footwear and an impression left at the scene of a crime varies with the known rarity of the shoeprint itself, which is a function of the class characteristics, as well as the complexity, clarity, and quality of randomly acquired characteristics (RACs) available for analysis. To help elucidate the discrimination potential of footwear as a source of forensic evidence, the aim of this research was three-fold.;The first (and most time consuming obstacle) of this study was data acquisition. In order to efficiently process footwear exemplar inputs and extract meaningful data, including information about randomly acquired characteristics, a semi-automated image processing chain was developed. To date, 1,000 shoes have been fully processed, yielding a total of 57,426 RACs characterized in terms of position (theta, r, rnorm), shape (circle, line/curve, triangle, irregular) and complex perimeter (e.g., Fourier descriptor). A plot of each feature versus position allowed for the creation of a heat map detailing coincidental RAC co-occurrence in position and shape. Results indicate that random chance association is as high as 1:756 for lines/curves and as low as 1:9,571 for triangular-shaped features. However, when a detailed analysis of the RAC\u27s geometry is evaluated, each feature is distinguishable.;The second goal of this project was to ascertain the baseline performance of an automated footwear classification algorithm. A brief literature review reveals more than a dozen different approaches to automated shoeprint classification over the last decade. Unfortunately, despite the multitude of options and reports on algorithm inter-comparisons, few studies have assessed accuracy for crime-scene-like prints. To remedy this deficit, this research quantitatively assessed the baseline performance of a single metric, known as Phase Only Correlation (POC), on both high quality and crime-scene-like prints. The objective was to determine the baseline performance for high quality exemplars with high signal-to-noise ratios, and then determine the degree to which this performance declined as a function of variations in mixed media (blood and dust), transfer mechanisms (gel lifters), enhancement techniques (digital and chemical) and substrates (ceramic tiles, vinyl tiles, and paper). The results indicate probabilities greater than 0.850 (and as high as 0.989) that known matches will exhibit stochastic dominance, and probabilities of 0.99 with high quality exemplars (Handiprints or outsole edge images).;The third and final aim of this research was to mathematically evaluate the frequency and similarity of RACs in high quality exemplars versus crime-scene-like impressions as a function of RAC shape, perimeter, and area. This was accomplished using wet-residue impressions (created in the laboratory, but generated in a manner intended to replicate crime-scene-like prints). These impressions were processed in the same manner as their high quality exemplar mates, allowing for the determination of RAC loss and correlation of the entire RAC map between crime scene and high quality images. Results show that the unpredictable nature of crime scene print deposition causes RAC loss that varies from 33-100% with an average loss of 85%, and that up to 10% of the crime scene impressions fully lacked any identifiable RACs. Despite the loss of features present in the crime-scene-like impressions, there was a 0.74 probability that the actual shoe\u27s high quality RAC map would rank higher in an ordered list than a known non-match map when queried with the crime-scene-like print. Moreover, this was true despite the fact that 64% of the crime-scene-like impressions exhibit 10 or fewer RACs

Statistical Shape Analysis of Galactic Hii Regions

Hii regions are diffuse nebulae of ionised hydrogen, excited by the extreme ultraviolet emission from massive stars. Due to the embedded nature of massive star formation, there are many observational difficulties involved when investigating such stars. Hii regions, however, are readily observed via their infrared and radio emission. As such, they highlight the location of their massive star sources. Furthermore, Hii region properties are directly resultant of their progenitors and environment. The overall aim of the work presented herein, is to determine whether statistical shape analysis of observational and numerically modelled Hii region data can be used to probe the associated astrophysical properties. Radio continuum and computer simulated synthetic images of Hii regions were analysed using the shape extraction and statistical comparison methods constructed in this work. For the radio data, six morphological groups were identified. Visual inspection and quantitative ordinance techniques confirmed that the shape analysis and grouping procedure were working as intended. It was found that in the first Galactic quadrant, location is mostly independent of group, with a small preference for regions of similar Galactic longitudes to share common morphologies. The shapes are homogeneously distributed across Galactocentric distance and latitude. One group contained regions that are all younger than 0.5 Myr and ionised by relatively low- to intermediate-mass sources. Those in another group are all driven by intermediate- to high-mass sources. One group was distinctly separated from the other five and contained regions at the surface brightness detection limit for the survey. The hierarchical procedure employed was most sensitive to the spatial sampling resolution used, which is determined for each region from its heliocentric distance. The numerical Hii region data was the result of photoionisation and feedback of a 34 M⊙ star, in a 1000 M⊙ cloud. Synthetic observations (SOs) were provided, comprising four evolutionary snapshots (0.1, 0.2, 0.4 and 0.6Myr), and multiple viewing projection angles. The shape analysis results provided conclusive evidence of the efficacy of the numerical simulations. When comparing the shapes of the synthetic regions to their observational counterparts, the SOs were grouped in amongst the Galactic Hii regions by the hierarchical procedure. There was also an association between the evolutionary distribution of regions of the respective samples. This suggested that this method could be further developed for classification of the observational regions by using the synthetic data, with its well defined parameters

Multi-scale active shape description in medical imaging

Shape description in medical imaging has become an increasingly important research field in recent years. Fast and high-resolution image acquisition methods like Magnetic Resonance (MR) imaging produce very detailed cross-sectional images of the human body - shape description is then a post-processing operation which abstracts quantitative descriptions of anatomically relevant object shapes. This task is usually performed by clinicians and other experts by first segmenting the shapes of interest, and then making volumetric and other quantitative measurements. High demand on expert time and inter- and intra-observer variability impose a clinical need of automating this process. Furthermore, recent studies in clinical neurology on the correspondence between disease status and degree of shape deformations necessitate the use of more sophisticated, higher-level shape description techniques. In this work a new hierarchical tool for shape description has been developed, combining two recently developed and powerful techniques in image processing: differential invariants in scale-space, and active contour models. This tool enables quantitative and qualitative shape studies at multiple levels of image detail, exploring the extra image scale degree of freedom. Using scale-space continuity, the global object shape can be detected at a coarse level of image detail, and finer shape characteristics can be found at higher levels of detail or scales. New methods for active shape evolution and focusing have been developed for the extraction of shapes at a large set of scales using an active contour model whose energy function is regularized with respect to scale and geometric differential image invariants. The resulting set of shapes is formulated as a multiscale shape stack which is analysed and described for each scale level with a large set of shape descriptors to obtain and analyse shape changes across scales. This shape stack leads naturally to several questions in regard to variable sampling and appropriate levels of detail to investigate an image. The relationship between active contour sampling precision and scale-space is addressed. After a thorough review of modem shape description, multi-scale image processing and active contour model techniques, the novel framework for multi-scale active shape description is presented and tested on synthetic images and medical images. An interesting result is the recovery of the fractal dimension of a known fractal boundary using this framework. Medical applications addressed are grey-matter deformations occurring for patients with epilepsy, spinal cord atrophy for patients with Multiple Sclerosis, and cortical impairment for neonates. Extensions to non-linear scale-spaces, comparisons to binary curve and curvature evolution schemes as well as other hierarchical shape descriptors are discussed

COBE's search for structure in the Big Bang

The launch of Cosmic Background Explorer (COBE) and the definition of Earth Observing System (EOS) are two of the major events at NASA-Goddard. The three experiments contained in COBE (Differential Microwave Radiometer (DMR), Far Infrared Absolute Spectrophotometer (FIRAS), and Diffuse Infrared Background Experiment (DIRBE)) are very important in measuring the big bang. DMR measures the isotropy of the cosmic background (direction of the radiation). FIRAS looks at the spectrum over the whole sky, searching for deviations, and DIRBE operates in the infrared part of the spectrum gathering evidence of the earliest galaxy formation. By special techniques, the radiation coming from the solar system will be distinguished from that of extragalactic origin. Unique graphics will be used to represent the temperature of the emitting material. A cosmic event will be modeled of such importance that it will affect cosmological theory for generations to come. EOS will monitor changes in the Earth's geophysics during a whole solar color cycle

Compact Symmetric Objects -- III Evolution of the High-Luminosity Branch and a Possible Connection with Tidal Disruption Events

We use a sample of 54 Compact Symmetric Objects (CSOs) to confirm that there are two unrelated CSO classes: an edge-dimmed, low-luminosity class (CSO~1), and an edge-brightened, high-luminosity class (CSO~2). Using blind tests, we show that CSO~2s consist of three sub-classes: CSO 2.0, having prominent hot-spots at the leading edges of narrow jets and/or narrow lobes; CSO~2.2, without prominent hot-spots, and with broad jets and/or lobes; and CSO~2.1, which exhibit mixed properties. Most CSO 2s do not evolve into larger jetted-AGN, but spend their whole life-cycle as CSOs of size $\lesssim$500 pc and age $\lesssim$5000 yr. The minimum energies needed to produce the radio luminosity and structure in CSO~2s range from $\sim~10^{-4}\,M_\odot{c}^2$ to $\sim7\,M_\odot{c}^2$. We show that the transient nature of most CSO~2s, and their birthrate, can be explained through ignition in the tidal disruption events of giant stars. We also consider possibilities of tapping the spin energy of the supermassive black hole, and tapping the energy of the accretion disk. Our results demonstrate that CSOs constitute a large family of AGN in which we have thus far studied only the brightest. More comprehensive CSO studies, with higher sensitivity, resolution, and dynamic range, will revolutionize our understanding of AGN and the central engines that power them.Comment: 44 pages, 16 figures, 9 tables, accepted for publicatio