Point Source Extraction with MOPEX

MOPEX (MOsaicking and Point source EXtraction) is a package developed at the Spitzer Science Center for astronomical image processing. We report on the point source extraction capabilities of MOPEX. Point source extraction is implemented as a two step process: point source detection and profile fitting. Non-linear matched filtering of input images can be performed optionally to increase the signal-to-noise ratio and improve detection of faint point sources. Point Response Function (PRF) fitting of point sources produces the final point source list which includes the fluxes and improved positions of the point sources, along with other parameters characterizing the fit. Passive and active deblending allows for successful fitting of confused point sources. Aperture photometry can also be computed for every extracted point source for an unlimited number of aperture sizes. PRF is estimated directly from the input images. Implementation of efficient methods of background and noise estimation, and modified Simplex algorithm contribute to the computational efficiency of MOPEX. The package is implemented as a loosely connected set of perl scripts, where each script runs a number of modules written in C/C++. Input parameter setting is done through namelists, ASCII configuration files. We present applications of point source extraction to the mosaic images taken at 24 and 70 micron with the Multiband Imaging Photometer (MIPS) as part of the Spitzer extragalactic First Look Survey and to a Digital Sky Survey image. Completeness and reliability of point source extraction is computed using simulated data.Comment: 20 pages, 13 Postscript figures, accepted for publication in PAS

A Chronology of Interpolation: From Ancient Astronomy to Modern Signal and Image Processing

This paper presents a chronological overview of the developments in interpolation theory, from the earliest times to the present date. It brings out the connections between the results obtained in different ages, thereby putting the techniques currently used in signal and image processing into historical perspective. A summary of the insights and recommendations that follow from relatively recent theoretical as well as experimental studies concludes the presentation

Multiple sparse representations classification

Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy.We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and sparsity level

Gravitational Wilson Loop and Large Scale Curvature

In a quantum theory of gravity the gravitational Wilson loop, defined as a suitable quantum average of a parallel transport operator around a large near-planar loop, provides important information about the large-scale curvature properties of the geometry. Here we shows that such properties can be systematically computed in the strong coupling limit of lattice regularized quantum gravity, by performing a local average over rotations, using an assumed near-uniform measure in group space. We then relate the resulting quantum averages to an expected semi-classical form valid for macroscopic observers, which leads to an identification of the gravitational correlation length appearing in the Wilson loop with an observed large-scale curvature. Our results suggest that strongly coupled gravity leads to a positively curved (De Sitter-like) quantum ground state, implying a positive effective cosmological constant at large distances.Comment: 22 pages, 6 figure

Head-up display with dynamic depth-variable viewing effect

Head-Up Displays (HUDs) can reduce duration and frequency of drivers looking away from traffic scenes, but information contents of different importance are usually displayed at the same time in contemporary HUD models. Such configurations increase the time that a driver searches for more critical information and it is essential that the said information can quickly attract driver’s attention without affecting his focus on the road. We introduce an alternative approach of displaying critical information with a variable depth in a designated local area of a HUD image. The variations are engineered to create a dynamic pop-up effect for hazard warnings, such as a car exceeding the speed-limit or approaching certain road signs. The image depth of the corresponding area is designed to vary by about half a metre and the image size by 1.4 times for a natural viewing experience, using an off-the-shelf liquid lens with electrically tuneable focus depths. The HUD optics can be adjusted to have an extended eye-box to accommodate driver’s head movement and a uniformity image brightness across the eye-box

Cell Detection with Star-convex Polygons

Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications. Recent successful learning-based approaches include per-pixel cell segmentation with subsequent pixel grouping, or localization of bounding boxes with subsequent shape refinement. In situations of crowded cells, these can be prone to segmentation errors, such as falsely merging bordering cells or suppressing valid cell instances due to the poor approximation with bounding boxes. To overcome these issues, we propose to localize cell nuclei via star-convex polygons, which are a much better shape representation as compared to bounding boxes and thus do not need shape refinement. To that end, we train a convolutional neural network that predicts for every pixel a polygon for the cell instance at that position. We demonstrate the merits of our approach on two synthetic datasets and one challenging dataset of diverse fluorescence microscopy images.Comment: Conference paper at MICCAI 201

Towards a feasible algorithm for tight glycaemic control in critically ill patients: a systematic review of the literature

INTRODUCTION: Tight glycaemic control is an important issue in the management of intensive care unit (ICU) patients. The glycaemic goals described by Van Den Berghe and colleagues in their landmark study of intensive insulin therapy appear difficult to achieve in a real life ICU setting. Most clinicians and nurses are concerned about a potentially increased frequency of severe hypoglycaemic episodes with more stringent glycaemic control. One of the steps we took before we implemented a glucose regulation protocol was to review published trials employing insulin/glucose algorithms in critically ill patients. METHODS: We conducted a search of the PubMed, Embase and Cochrane databases using the following terms: 'glucose', 'insulin', 'protocol', 'algorithm', 'nomogram', 'scheme', 'critically ill' and 'intensive care'. Our search was limited to clinical trials conducted in humans. The aim of the papers selected was required to be glycaemic control in critically ill patients; the blood glucose target was required to be 10 mmol/l or under (or use of a protocol that resulted in a mean blood glucose = 10 mmol/l). The studies were categorized according to patient type, desired range of blood glucose values, method of insulin administration, frequency of blood glucose control, time taken to achieve the desired range for glucose, proportion of patients with glucose in the desired range, mean blood glucose and frequency of hypoglycaemic episodes. RESULTS: A total of twenty-four reports satisfied our inclusion criteria. Most recent studies (nine) were conducted in an ICU; nine others were conducted in a perioperative setting and six were conducted in patients with acute myocardial infarction or stroke. Studies conducted before 2001 did not include normoglycaemia among their aims, which changed after publication of the study by Van Den Berghe and coworkers in 2001; glycaemic goals became tighter, with a target range between 4 and 8 mmol/l in most studies. CONCLUSION: Studies using a dynamic scale protocol combining a tight glucose target and the last two blood glucose values to determine the insulin infusion rate yielded the best results in terms of glycaemic control and reported low frequencies of hypoglycaemic episodes

Quantitative Comparison of Sinc-Approximating Kernels for Medical Image Interpolation

Abstract. Interpolation is required in many medical image processing operations. From sampling theory, it follows that the ideal interpolation kernel is the sinc function, which is of infinite extent. In the attempt to obtain practical and computationally efficient image processing al-gorithms, many sinc-approximating interpolation kernels have been de-vised. In this paper we present the results of a quantitative comparison of 84 different sinc-approximating kernels, with spatial extents ranging from 2 to 10 grid points in each dimension. The evaluation involves the application of geometrical transformations to medical images from dif-ferent modalities (CT, MR, and PET), using the different kernels. The results show very clearly that, of all kernels with a spatial extent of 2 grid points, the linear interpolation kernel performs best. Of all kernels with an extent of 4 grid points, the cubic convolution kernel is the best (28 %- 75 % reduction of the errors as compared to linear interpolation). Even better results (44 %- 95 % reduction) are obtained with kernels of larger extent, notably the Welch, Cosine, Lanczos, and Kaiser windowed sinc kernels. In general, the truncated sinc kernel is one of the worst performing kernels.

З'єднання зварюванням

Метою методичних вказівок «З’єднання зварюванням» є ознайомлен-ня зі способами нероз’ємних з’єднань, вивчення правил зображення та поз-начення швів з’єднань зварюванням на кресленні, набуття навиків виконан-ня, оформлення та читання креслень з’єднань зварюванням, які необхідні для вивчення загально-інженерних та спеціальних технічних дисциплін.Вступ...3 1.Загальні відомості....4 2.Класифікація з’єднаньзварюванням...4 3.Умовне зображення і позначення швів з’єднань, які зварюють....7 4.Спрощене позначення швів з’єднаньякі зварюють...15 5.Оформлення креслень з’єднань зварюванням...23 6.Вказівки до виконання графічних робіт та індивідуальні завдання...24 7.Додаток А...27 8.Додаток Б...28 9.Додаток В...29 10.Використана література...4

Asymptotic statistics of the n-sided planar Poisson-Voronoi cell. I. Exact results

We achieve a detailed understanding of the $n$-sided planar Poisson-Voronoi cell in the limit of large $n$. Let ${p}\_n$ be the probability for a cell to have $n$ sides. We construct the asymptotic expansion of $\log {p}\_n$ up to terms that vanish as $n\to\infty$. We obtain the statistics of the lengths of the perimeter segments and of the angles between adjoining segments: to leading order as $n\to\infty$, and after appropriate scaling, these become independent random variables whose laws we determine; and to next order in $1/n$ they have nontrivial long range correlations whose expressions we provide. The $n$-sided cell tends towards a circle of radius (n/4\pi\lambda)^{\half}, where $\lambda$ is the cell density; hence Lewis' law for the average area $A\_n$ of the $n$-sided cell behaves as $A\_n \simeq cn/\lambda$ with $c=1/4$. For $n\to\infty$ the cell perimeter, expressed as a function $R(\phi)$ of the polar angle $\phi$, satisfies $d^2 R/d\phi^2 = F(\phi)$, where $F$ is known Gaussian noise; we deduce from it the probability law for the perimeter's long wavelength deviations from circularity. Many other quantities related to the asymptotic cell shape become accessible to calculation.Comment: 54 pages, 3 figure