Shadow Optimization from Structured Deep Edge Detection

Local structures of shadow boundaries as well as complex interactions of image regions remain largely unexploited by previous shadow detection approaches. In this paper, we present a novel learning-based framework for shadow region recovery from a single image. We exploit the local structures of shadow edges by using a structured CNN learning framework. We show that using the structured label information in the classification can improve the local consistency of the results and avoid spurious labelling. We further propose and formulate a shadow/bright measure to model the complex interactions among image regions. The shadow and bright measures of each patch are computed from the shadow edges detected in the image. Using the global interaction constraints on patches, we formulate a least-square optimization problem for shadow recovery that can be solved efficiently. Our shadow recovery method achieves state-of-the-art results on the major shadow benchmark databases collected under various conditions.Comment: 8 pages. CVPR 201

Sciences and audiences along the lastcentury: the impact of Astronomy Education

Just as in the past, the development of the natural sciences and in particular of astronomy has changed the history of humanity. If we think about the role of our discipline into the future, it shows its enormous power in the field of education, owing to the possibility of awakening interest in science in very varied audiences. Within the framework of the enormous progress made in the technologies related to astronomy, many of them of daily use, the role of the astronomer in the era of Communications acquires fundamental importance. In this presentation, we will try to make a journey through the different ways of presenting astronomical topics for different audiences over the last 100 years. In turn, we will show some specific achievements, associated with education programmes of the discipline. We discuss the impact produced by proposals that are both rigorous in terms of content, and also appeal to the development of the human being in an integral manner, within the framework of citizen science activities. For this research, we have taken into account the uninterrupted development of the NASE programme, which has performed 112 courses in 24 countries throughout the world and in different languages. NASE has involved 4966 secondary teachers in the last eight years.Fil: Barros, Rosario Magdalena. Universidad Politécnica de Catalunya; EspañaFil: Garcia, Beatriz Elena. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa pque. Centenario. Instituto de Tecnología en Detección y Astropartículas. Itedam - subsede del Instituto de tec. En Detección y Astropartículas Mendoza | Comisión Nacional de Energía Atómica. Instituto de Tecnología en Detección y Astropartículas. Itedam - subsede del instituto de tec. En Detección y Astropartículas Mendoza | Universidad Nacional de san Martín. Instituto de Tecnología en Detección y Astropartículas. Itedam - subsede del instituto de tec. En Detección y Astropartículas Mendoza; ArgentinaInternational Astronomical Union SymposiumVienaAustriaInternational Astronomical Unio

From cosmic ray physics to cosmic ray astronomy: Bruno Rossi and the opening of new windows on the universe

Bruno Rossi is considered one of the fathers of modern physics, being also a pioneer in virtually every aspect of what is today called high-energy astrophysics. At the beginning of 1930s he was the pioneer of cosmic ray research in Italy, and, as one of the leading actors in the study of the nature and behavior of the cosmic radiation, he witnessed the birth of particle physics and was one of the main investigators in this fields for many years. While cosmic ray physics moved more and more towards astrophysics, Rossi continued to be one of the inspirers of this line of research. When outer space became a reality, he did not hesitate to leap into this new scientific dimension. Rossi's intuition on the importance of exploiting new technological windows to look at the universe with new eyes, is a fundamental key to understand the profound unity which guided his scientific research path up to its culminating moments at the beginning of 1960s, when his group at MIT performed the first in situ measurements of the density, speed and direction of the solar wind at the boundary of Earth's magnetosphere, and when he promoted the search for extra-solar sources of X rays. A visionary idea which eventually led to the breakthrough experiment which discovered Scorpius X-1 in 1962, and inaugurated X-ray astronomy.Comment: This work was presented at the conference "100 Years Cosmic Ray Physics - Anniversary of the V.F. Hess Discovery", 6-8 August, Bad Saarow/Pieskow, Germany, where Hess landed on August 7, 1912, after discovery of the "H\"ohenstrahlung". To be published in the Astroparticle Journa

Automatic indoor/outdoor scene classification

The advent and wide acceptance of digital imaging technology has motivated an upsurge in research focused on managing the ever-growing number of digital images. Current research in image manipulation represents a general shift in the field of computer vision from traditional image analysis based on low-level features (e.g. color and texture) to semantic scene understanding based on high-level features (e.g. grass and sky). One particular area of investigation is scene categorization, where the organization of a large number of images is treated as a classification problem. Generally, the classification involves mapping a set of traditional low-level features to semantically meaningful categories, such as indoor and outdoor scenes, using a classifier engine. Successful indoor/outdoor scene categorization is beneficial to a number of image manipulation applications, as indoor and outdoor scenes represent among the most general scene types. In content-based image retrieval, for example, a query for a scene containing a sunset can be restricted to images in the database pre-categorized as outdoor scenes. Also, in image enhancement, categorization of a scene as indoor vs. outdoor can lead to improved color balancing and tone reproduction. Prior research in scene classification has shown that high-level information can, in fact, be inferred from low-level image features. Classification rates of roughly 90% have been reported using low-level features to predict indoor scenes vs. outdoor scenes. However, the high classification rates are often achieved by using computationally expensive, high-dimensional feature sets, thus limiting the practical implementation of such systems. To address this problem, a low complexity, low-dimensional feature set was extracted in a variety of configurations in the work presented here. Due to their excellent generalization performance, Support Vector Machines (SVMs) were used to manage the tradeoff between reduced dimensionality and increased classification accuracy. It was determined that features extracted from image subblocks, as opposed to the full image, can yield better classification rates when combined in a second stage. In particular, applying SVMs in two stages led to an indoor/outdoor classification accuracy of 90.2% on a large database of consumer photographs provided by Kodak. Finally, it was also shown that low-level and semantic features can be integrated efficiently using Bayesian networks for increased accuracy. Specifically, the integration of grass and sky semantic features with color and texture low-level features increased the indoor/outdoor classification rate to 92.8% on the same database of images

Machine-learning identification of galaxies in the WISExSuperCOSMOS all-sky catalogue

The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS, were cross-matched by Bilicki et al. (2016) (B16) to construct a novel photometric redshift catalogue on 70% of the sky. Galaxies were therein separated from stars and quasars through colour cuts, which may leave imperfections because of mixing different source types which overlap in colour space. The aim of the present work is to identify galaxies in the WISExSuperCOSMOS catalogue through an alternative approach of machine learning. This allows us to define more complex separations in the multi-colour space than possible with simple colour cuts, and should provide more reliable source classification. For the automatised classification we use the support vector machines learning algorithm, employing SDSS spectroscopic sources cross-matched with WISExSuperCOSMOS as the training and verification set. We perform a number of tests to examine the behaviour of the classifier (completeness, purity and accuracy) as a function of source apparent magnitude and Galactic latitude. We then apply the classifier to the full-sky data and analyse the resulting catalogue of candidate galaxies. We also compare thus produced dataset with the one presented in B16. The tests indicate very high accuracy, completeness and purity (>95%) of the classifier at the bright end, deteriorating for the faintest sources, but still retaining acceptable levels of 85%. No significant variation of classification quality with Galactic latitude is observed. Application of the classifier to all-sky WISExSuperCOSMOS data gives 15 million galaxies after masking problematic areas. The resulting sample is purer than the one in B16, at a price of lower completeness over the sky. The automatic classification gives a successful alternative approach to defining a reliable galaxy sample as compared to colour cuts.Comment: 12 pages, 15 figures, accepted for publication in A&A. Obtained catalogue will be included in the public release of the WISExSuperCOSMOS galaxy catalogue available from http://ssa.roe.ac.uk/WISExSCO

Gravitational Lensing by Spinning Black Holes in Astrophysics, and in the Movie Interstellar

Interstellar is the first Hollywood movie to attempt depicting a black hole as it would actually be seen by somebody nearby. For this we developed a code called DNGR (Double Negative Gravitational Renderer) to solve the equations for ray-bundle (light-beam) propagation through the curved spacetime of a spinning (Kerr) black hole, and to render IMAX-quality, rapidly changing images. Our ray-bundle techniques were crucial for achieving IMAX-quality smoothness without flickering. This paper has four purposes: (i) To describe DNGR for physicists and CGI practitioners . (ii) To present the equations we use, when the camera is in arbitrary motion at an arbitrary location near a Kerr black hole, for mapping light sources to camera images via elliptical ray bundles. (iii) To describe new insights, from DNGR, into gravitational lensing when the camera is near the spinning black hole, rather than far away as in almost all prior studies. (iv) To describe how the images of the black hole Gargantua and its accretion disk, in the movie \emph{Interstellar}, were generated with DNGR. There are no new astrophysical insights in this accretion-disk section of the paper, but disk novices may find it pedagogically interesting, and movie buffs may find its discussions of Interstellar interesting.Comment: 46 pages, 17 figure

Revealing the Dark TeV Sky: The Atmospheric Cherenkov Imaging Technique for Very High Energy Gamma-ray Astronomy

The Atmospheric Cherenkov Imaging Technique has opened up the gamma-ray spectrum from 100 GeV to 50 TeV to astrophysical exploration. The development of the technique (with emphasis on the early days) is described as are the basic principles underlying its application to gamma-ray astronomy. The current generation of arrays of telescopes, in particular, VERITAS is briefly described.Comment: To be published in the Proceedings of the International Workshop on "Energy Budget in the High Energy Universe", Kashiwa, Japan, February 22-24, 200