Computation of the Euler Number of a Binary Image Composed of Hexagonal Cells

ABSTRACTMost of the proposals to compute the Euler number of a binary image have been designed to work with imagescomposed of squared cells. Only a few of these methods (in the case of images composed of hexagonal cells) havebeen reported in literature, although it is known that images composed of hexagonal cells do not suffer from theproblems of connectivity frequently found in the case of images composed of squared cells. In this paper, a new wayto compute the Euler number (E) of a binary image composed of hexagonal cells is presented. For this, the perimeterP of the isolated regions in the image, their contact perimeter c P and the type T of a cell are used to obtain thisimportant invariant. The proposal can be used alone or in combination with other features to describe any binaryplanar shape composed of hexagonal pixels for its further recognition

On the Topological Disparity Characterization of Square-Pixel Binary Image Data by a Labeled Bipartite Graph

Given an nD digital image I based on cubical n-xel, to fully characterize the degree of internal topological dissimilarity existing in I when using different adjacency relations (mainly, comparing 2n or 2n −1 adjacency relations) is a relevant issue in current problems of digital image processing relative to shape detection or identification. In this paper, we design and implement a new self-dual representation for a binary 2D image I, called {4, 8}-region adjacency forest of I ({4, 8}-RAF, for short), that allows a thorough analysis of the differences between the topology of the 4-regions and that of the 8-regions of I. This model can be straightforwardly obtained from the classical region adjacency tree of I and its binary complement image Ic, by a suitable region label identification. With these two labeled rooted trees, it is possible: (a) to compute Euler number of the set of foreground (resp. background) pixels with regard to 4-adjacency or 8-adjacency; (b) to identify new local and global measures and descriptors of topological dissimilarity not only for one image but also between two or more images. The parallelization of the algorithms to extract and manipulate these structures is complete, thus producing efficient and unsophisticated codes with a theoretical computing time near the logarithm of the width plus the height of an image. Some toy examples serve to explain the representation and some experiments with gray real images shows the influence of the topological dissimilarity when detecting feature regions, like those returned by the MSER (maximally stable extremal regions) method.Ministerio de Economía, Industria y Competitividad PID2019-110455GB-I00 (Par-HoT)Junta de Andalucía US-138107

Convolutional neural networks: a magic bullet for gravitational-wave detection?

In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature of merging black holes. However, to date, these methods have not yet been successfully applied to the analysis of long stretches of data recorded by the Advanced LIGO and Virgo gravitational-wave observatories. In this work, we critically examine the use of convolutional neural networks as a tool to search for merging black holes. We identify the strengths and limitations of this approach, highlight some common pitfalls in translating between machine learning and gravitational-wave astronomy, and discuss the interdisciplinary challenges. In particular, we explain in detail why convolutional neural networks alone cannot be used to claim a statistically significant gravitational-wave detection. However, we demonstrate how they can still be used to rapidly flag the times of potential signals in the data for a more detailed follow-up. Our convolutional neural network architecture as well as the proposed performance metrics are better suited for this task than a standard binary classifications scheme. A detailed evaluation of our approach on Advanced LIGO data demonstrates the potential of such systems as trigger generators. Finally, we sound a note of caution by constructing adversarial examples, which showcase interesting "failure modes" of our model, where inputs with no visible resemblance to real gravitational-wave signals are identified as such by the network with high confidence.Comment: First two authors contributed equally; appeared at Phys. Rev.

EgoFace: Egocentric Face Performance Capture and Videorealistic Reenactment

Face performance capture and reenactment techniques use multiple cameras and sensors, positioned at a distance from the face or mounted on heavy wearable devices. This limits their applications in mobile and outdoor environments. We present EgoFace, a radically new lightweight setup for face performance capture and front-view videorealistic reenactment using a single egocentric RGB camera. Our lightweight setup allows operations in uncontrolled environments, and lends itself to telepresence applications such as video-conferencing from dynamic environments. The input image is projected into a low dimensional latent space of the facial expression parameters. Through careful adversarial training of the parameter-space synthetic rendering, a videorealistic animation is produced. Our problem is challenging as the human visual system is sensitive to the smallest face irregularities that could occur in the final results. This sensitivity is even stronger for video results. Our solution is trained in a pre-processing stage, through a supervised manner without manual annotations. EgoFace captures a wide variety of facial expressions, including mouth movements and asymmetrical expressions. It works under varying illuminations, background, movements, handles people from different ethnicities and can operate in real time

Технологія CUDA для підвищення ефективності руху повітряного судна

Considered the method of parallel computing based on CUDA-architecture with detecting largeand small scale details of turbulence flow to adapt flight dynamics for motion control of the aircraft. Definedthe acceleration value of the parallel implementation relatively to series and the integraleffectiveness of parallel computing that allows to use the NVIDIA Tegra graphics processors toincrease the processing power of massively parallel calculationsРассмотрен метод параллельных вычислений, основанный на CUDA-архитектуре, с обнаружением больших имелких деталей турбулентного потока для адаптации динамики полета при управлении движением самолета.Определено значение ускорения параллельной реализации относительно последовательной и интегральнаяэффективность параллельных вычислений, что позволяет использовать графические процессоры NVIDIA Tegraдля увеличения вычислительной мощности массивно-параллельных расчетовРозглянуто метод паралельних обчислень на основі CUDA-архітектури з визначенням великих і малих деталей турбулентного потоку для адаптації динаміки польоту під час керування рухом повітряного судна. Визначено значення прискорення паралельної реалізації відносно послідовної та інтегральну ефективність паралельних обчислень, що дозволяє використовувати графічні процесори NVIDIA Tegra для збільшення обчислювальної потужності масивно-паралельних розрахункі

Automatic Structural Scene Digitalization

In this paper, we present an automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format. The proposed system applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets. Technically, a general rule-based filter parsing method is fist adopted to extract effective information from the original floor plan. Then, an image-processing based recovery method is employed to correct information extracted in the first step. Our proposed method is fully automatic and real-time. Such analysis system provides high accuracy and is also evaluated on a public website that, on average, archives more than ten thousands effective uses per day and reaches a relatively high satisfaction rate.Comment: paper submitted to PloS On