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The need for growth and the social impact of assistive technology for the visually impaired and blind people is driven by the increasing ageing of the population and mainly of the blind population. Blind or visually impaired people cannot visually enjoy the magnificent astronomical objects and the universe and have great difficulty in engaging in the field of astronomy, which makes them feel even more socially excluded than they already are due to their condition, thus, enhancing their social introvertness. This work depicts a system which translates astronomical images into audible sounds. It involves recognition of the astronomical objects of interest inside a picture and separation from background sky and stars as well as detection of their shape, color and brightness. The detected images are then translated into audible sounds and vibration that correspond to the visual information detected. Our approach utilizes principles of state-of-the-art work, thus making sure the proposed system is embraced by the Visually Impaired and Blind People offering them the chance to significantly mitigate their social introvertness. [ABSTRACT FROM AUTHOR]

The next generation of radio telescopes will generate exabytes of data on hundreds of millions of objects, making automated methods for the detection of astronomical objects ("sources") essential. Of particular importance are faint, diffuse objects embedded in noise. There is a pressing need for source finding software that identifies these sources, involves little manual tuning, yet is tractable to calculate. We first give a novel image discretisation method that incorporates uncertainty about how an image should be discretised. We then propose a hierarchical prior for astronomical images, which leads to a Bayes factor indicating how well a given region conforms to a model of source that is exceptionally unconstrained, compared to a model of background. This enables the efficient localisation of regions that are "suspiciously different" from the background distribution, so our method looks not for brightness but for anomalous distributions of intensity, which is much more general. The model of background can be iteratively improved by removing the influence on it of sources as they are discovered. The approach is evaluated by identifying sources in real and simulated data, and performs well on these measures: the Bayes factor is maximized at most real objects, while returning only a moderate number of false positives. In comparison to a catalogue constructed by widely-used source detection software with manual post-processing by an astronomer, our method found a number of dim sources that were missing from the "ground truth" catalogue. [ABSTRACT FROM AUTHOR]

The Background-Source separation technique is a statistical method in image processing. The technique makes use of Bayesian probability theory combined with a probabilistic mixture model. Background and source signals are jointly estimated, allowing for a proper propagation of uncertainties. Posterior probability density functions of background and source signals are calculated and their rms uncertainties are derived from the Hessian matrix. Poisson statistics is rigorously accounted throughout the whole algorithm. The technique is flexible and can easily be adapted to tackle a wide range of open questions in image analysis. Applications to astronomical data are addressed in this paper, with emphasis to data from observations of high-energy astrophysics in the X-ray spectral regime. We show that the technique improves efficiency and decreases contamination with respect to previous methods. [ABSTRACT FROM AUTHOR]