Abridged Abstracts: Rushing the Research Race

Mediante un análisis de corpus manual y electrónico exploro la estructura retórica y los rasgos discursivos de los resúmenes abreviados en dos campos diferentes: la Lingüística Aplicada y la Ingeniería Electrónica, Informática, y de Telecomunicaciones. Mi objetivo es analizar hasta qué punto estos resúmenes abreviados, que curiosamente incluyen elementos en apariencia superfluos, son informativos. Según mis hallazgos, este tipo de resumen soporta una tensión entre dos fuerzas opuestas: economía retórica por una parte, y meta-referencia y autopromoción, por otra. Mi conclusión es que estos dos últimos elementos, aunque prescindibles, balizan contenidos específicos, facilitando con ello la escritura y agilizando la criba de información.Through manual and electronic corpus analysis, I explore the rhetorical structure and discursive features of abridged abstracts in two distinct fields: Applied Linguistics and Electronic, Telecommunications and Computer Engineering. My goal is to discuss whether shortened abstracts, which do include some apparently superfluous elements, are truly informative, and to compare trends in the two broad fields mentioned. According to my findings, abridged abstracts withstand a tension between two opposing forces: rhetorical economy on the one hand, and meta-reference and self-promotion on the other. My claim is that, although dispensable, meta-reference and self-promotion fulfil important beaconing functions that facilitate text production and accelerate research screening

Object-based audio for interactive football broadcast

An end-to-end AV broadcast system providing an immersive, interactive experience for live events is the development aim for the EU FP7 funded project, FascinatE. The project has developed real time audio object event detection and localisation, scene modelling and processing methods for multimedia data including 3D audio, which will allow users to navigate the event by creating their own unique user-defined scene. As part of the first implementation of the system a test shoot was carried out capturing a live Premier League football game and methods have been developed to detect, analyse, extract and localise salient audio events from a range of sensors and represent them within an audio scene in order to allow free navigation within the scene

Multi-Clip Video Editing from a Single Viewpoint

International audienceWe propose a framework for automatically generating multiple clips suitable for video editing by simulating pan-tilt-zoom camera movements within the frame of a single static camera. Assuming important actors and objects can be localized using computer vision techniques, our method requires only minimal user input to define the subject matter of each sub-clip. The composition of each sub-clip is automatically computed in a novel L1-norm optimization framework. Our approach encodes several common cinematographic practices into a single convex cost function minimization problem, resulting in aesthetically pleasing sub-clips which can easily be edited together using off-the-shelf multi-clip video editing software. We demonstrate our approach on five video sequences of a live theatre performance by generating multiple synchronized subclips for each sequence

Real-Time Computational Gigapixel Multi-Camera Systems

The standard cameras are designed to truthfully mimic the human eye and the visual system. In recent years, commercially available cameras are becoming more complex, and offer higher image resolutions than ever before. However, the quality of conventional imaging methods is limited by several parameters, such as the pixel size, lens system, the diffraction limit, etc. The rapid technological advancements, increase in the available computing power, and introduction of Graphics Processing Units (GPU) and Field-Programmable-Gate-Arrays (FPGA) open new possibilities in the computer vision and computer graphics communities. The researchers are now focusing on utilizing the immense computational power offered on the modern processing platforms, to create imaging systems with novel or significantly enhanced capabilities compared to the standard ones. One popular type of the computational imaging systems offering new possibilities is a multi-camera system. This thesis will focus on FPGA-based multi-camera systems that operate in real-time. The aim of themulti-camera systems presented in this thesis is to offer a wide field-of-view (FOV) video coverage at high frame rates. The wide FOV is achieved by constructing a panoramic image from the images acquired by the multi-camera system. Two new real-time computational imaging systems that provide new functionalities and better performance compared to conventional cameras are presented in this thesis. Each camera system design and implementation are analyzed in detail, built and tested in real-time conditions. Panoptic is a miniaturized low-cost multi-camera system that reconstructs a 360 degrees view in real-time. Since it is an easily portable system, it provides means to capture the complete surrounding light field in dynamic environment, such as when mounted on a vehicle or a flying drone. The second presented system, GigaEye II , is a modular high-resolution imaging system that introduces the concept of distributed image processing in the real-time camera systems. This thesis explains in detail howsuch concept can be efficiently used in real-time computational imaging systems. The purpose of computational imaging systems in the form of multi-camera systems does not end with real-time panoramas. The application scope of these cameras is vast. They can be used in 3D cinematography, for broadcasting live events, or for immersive telepresence experience. The final chapter of this thesis presents three potential applications of these systems: object detection and tracking, high dynamic range (HDR) imaging, and observation of multiple regions of interest. Object detection and tracking, and observation of multiple regions of interest are extremely useful and desired capabilities of surveillance systems, in security and defense industry, or in the fast-growing industry of autonomous vehicles. On the other hand, high dynamic range imaging is becoming a common option in the consumer market cameras, and the presented method allows instantaneous capture of HDR videos. Finally, this thesis concludes with the discussion of the real-time multi-camera systems, their advantages, their limitations, and the future predictions

Widening the view angle of auto-multiscopic display, denoising low brightness light field data and 3D reconstruction with delicate details

This doctoral thesis will present the results of my work into widening the viewing angle of the auto-multiscopic display, denoising light filed data the enhancement of captured light filed data captured in low light circumstance, and the attempts on reconstructing the subject surface with delicate details from microscopy image sets. The automultiscopic displays carefully control the distribution of emitted light over space, direction (angle) and time so that even a static image displayed can encode parallax across viewing directions (light field). This allows simultaneous observation by multiple viewers, each perceiving 3D from their own (correct) perspective. Currently, the illusion can only be effectively maintained over a narrow range of viewing angles. We propose and analyze a simple solution to widen the range of viewing angles for automultiscopic displays that use parallax barriers. We insert a refractive medium, with a high refractive index, between the display and parallax barriers. The inserted medium warps the exitant lightfield in a way that increases the potential viewing angle. We analyze the consequences of this warp and build a prototype with a 93% increase in the effective viewing angle. Additionally, we developed an integral images synthesis method that can address the refraction introduced by the inserted medium efficiently without the use of ray tracing. Capturing light field image with a short exposure time is preferable for eliminating the motion blur but it also leads to low brightness in a low light environment, which results in a low signal noise ratio. Most light field denoising methods apply regular 2D image denoising method to the sub-aperture images of a 4D light field directly, but it is not suitable for focused light field data whose sub-aperture image resolution is too low to be applied regular denoising methods. Therefore, we propose a deep learning denoising method based on micro lens images of focused light field to denoise the depth map and the original micro lens image set simultaneously, and achieved high quality total focused images from the low focused light field data. In areas like digital museum, remote researching, 3D reconstruction with delicate details of subjects is desired and technology like 3D reconstruction based on macro photography has been used successfully for various purposes. We intend to push it further by using microscope rather than macro lens, which is supposed to be able to capture the microscopy level details of the subject. We design and implement a scanning method which is able to capture microscopy image set from a curve surface based on robotic arm, and the 3D reconstruction method suitable for the microscopy image set

Content-Adaptive Non-Stationary Projector Resolution Enhancement

For any projection system, one goal will surely be to maximize the quality of projected imagery at a minimized hardware cost, which is considered a challenging engineering problem. Experience in applying different image filters and enhancements to projected video suggests quite clearly that the quality of a projected enhanced video is very much a function of the content of the video itself. That is, to first order, whether the video contains content which is moving as opposed to still plays an important role in the video quality, since the human visual system tolerates much more blur in moving imagery but at the same time is significantly sensitive to the flickering and aliasing caused by moving sharp textures. Furthermore, the spatial and statistical characteristics of text and non-text images are quite distinct. We would, therefore, assert that the text-like, moving and background pixels of a given video stream should be enhanced differently using class-dependent video enhancement filters to achieve maximum visual quality. In this thesis, we present a novel text-dependent content enhancement scheme, a novel motion-dependent content enhancement scheme and a novel content-adaptive resolution enhancement scheme based on a text-like / non-text-like classification and a pixel-wise moving / non-moving classification, with the actual enhancement obtained via class--dependent Wiener deconvolution filtering. Given an input image, the text and motion detection methods are used to generate binary masks to indicate the location of the text and moving regions in the video stream. Then enhanced images are obtained by applying a plurality of class-dependent enhancement filters, with text-like regions sharpened more than the background and moving regions sharpened less than the background. Later, one or more resulting enhanced images are combined into a composite output image based on the corresponding mask of different features. Finally, a higher resolution projected video stream is conducted by controlling one or more projectors to project the plurality of output frame streams in a rapid overlapping way. Experimental results on the test images and videos show that the proposed schemes all offer improved visual quality over projection without enhancement as well as compared to a recent state-of-the-art enhancement method. Particularly, the proposed content-adaptive resolution enhancement scheme increases the PSNR value by at least 18.2% and decreases MSE value by at least 25%

Deep learning & remote sensing : pushing the frontiers in image segmentation

Dissertação (Mestrado em Informática) — Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, Brasília, 2022.A segmentação de imagens visa simplificar o entendimento de imagens digitais e métodos de aprendizado profundo usando redes neurais convolucionais permitem a exploração de diferentes tarefas (e.g., segmentação semântica, instância e panóptica). A segmentação semântica atribui uma classe a cada pixel em uma imagem, a segmentação de instância classifica objetos a nível de pixel com um identificador exclusivo para cada alvo e a segmentação panóptica combina instâncias com diferentes planos de fundo. Os dados de sensoriamento remoto são muito adequados para desenvolver novos algoritmos. No entanto, algumas particularidades impedem que o sensoriamento remoto com imagens orbitais e aéreas cresça quando comparado às imagens tradicionais (e.g., fotos de celulares): (1) as imagens são muito extensas, (2) apresenta características diferentes (e.g., número de canais e formato de imagem), (3) um grande número de etapas de préprocessamento e pós-processamento (e.g., extração de quadros e classificação de cenas grandes) e (4) os softwares para rotulagem e treinamento de modelos não são compatíveis. Esta dissertação visa avançar nas três principais categorias de segmentação de imagens. Dentro do domínio de segmentação de instâncias, propusemos três experimentos. Primeiro, aprimoramos a abordagem de segmentação de instância baseada em caixa para classificar cenas grandes. Em segundo lugar, criamos um método sem caixas delimitadoras para alcançar resultados de segmentação de instâncias usando modelos de segmentação semântica em um cenário com objetos esparsos. Terceiro, aprimoramos o método anterior para cenas aglomeradas e desenvolvemos o primeiro estudo considerando aprendizado semissupervisionado usando sensoriamento remoto e dados GIS. Em seguida, no domínio da segmentação panóptica, apresentamos o primeiro conjunto de dados de segmentação panóptica de sensoriamento remoto e dispomos de uma metodologia para conversão de dados GIS no formato COCO. Como nosso primeiro estudo considerou imagens RGB, estendemos essa abordagem para dados multiespectrais. Por fim, melhoramos o método box-free inicialmente projetado para segmentação de instâncias para a tarefa de segmentação panóptica. Esta dissertação analisou vários métodos de segmentação e tipos de imagens, e as soluções desenvolvidas permitem a exploração de novas tarefas , a simplificação da rotulagem de dados e uma forma simplificada de obter previsões de instância e panópticas usando modelos simples de segmentação semântica.Image segmentation aims to simplify the understanding of digital images. Deep learning-based methods using convolutional neural networks have been game-changing, allowing the exploration of different tasks (e.g., semantic, instance, and panoptic segmentation). Semantic segmentation assigns a class to every pixel in an image, instance segmentation classifies objects at a pixel level with a unique identifier for each target, and panoptic segmentation combines instancelevel predictions with different backgrounds. Remote sensing data largely benefits from those methods, being very suitable for developing new DL algorithms and creating solutions using top-view images. However, some peculiarities prevent remote sensing using orbital and aerial imagery from growing when compared to traditional ground-level images (e.g., camera photos): (1) The images are extensive, (2) it presents different characteristics (e.g., number of channels and image format), (3) a high number of pre-processes and post-processes steps (e.g., extracting patches and classifying large scenes), and (4) most open software for labeling and deep learning applications are not friendly to remote sensing due to the aforementioned reasons. This dissertation aimed to improve all three main categories of image segmentation. Within the instance segmentation domain, we proposed three experiments. First, we enhanced the box-based instance segmentation approach for classifying large scenes, allowing practical pipelines to be implemented. Second, we created a bounding-box free method to reach instance segmentation results by using semantic segmentation models in a scenario with sparse objects. Third, we improved the previous method for crowded scenes and developed the first study considering semi-supervised learning using remote sensing and GIS data. Subsequently, in the panoptic segmentation domain, we presented the first remote sensing panoptic segmentation dataset containing fourteen classes and disposed of software and methodology for converting GIS data into the panoptic segmentation format. Since our first study considered RGB images, we extended our approach to multispectral data. Finally, we leveraged the box-free method initially designed for instance segmentation to the panoptic segmentation task. This dissertation analyzed various segmentation methods and image types, and the developed solutions enable the exploration of new tasks (such as panoptic segmentation), the simplification of labeling data (using the proposed semi-supervised learning procedure), and a simplified way to obtain instance and panoptic predictions using simple semantic segmentation models

Working Papers: Astronomy and Astrophysics Panel Reports

The papers of the panels appointed by the Astronomy and Astrophysics survey Committee are compiled. These papers were advisory to the survey committee and represent the opinions of the members of each panel in the context of their individual charges. The following subject areas are covered: radio astronomy, infrared astronomy, optical/IR from ground, UV-optical from space, interferometry, high energy from space, particle astrophysics, theory and laboratory astrophysics, solar astronomy, planetary astronomy, computing and data processing, policy opportunities, benefits to the nation from astronomy and astrophysics, status of the profession, and science opportunities

The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

3rd International Workshop on Instrumentation for Planetary Missions : October 24–27, 2016, Pasadena, California

The purpose of this workshop is to provide a forum for collaboration, exchange of ideas and information, and discussions in the area of the instruments, subsystems, and other payload-related technologies needed to address planetary science questions. The agenda will compose a broad survey of the current state-of-the-art and emerging capabilities in instrumentation available for future planetary missions.Universities Space Research Association (USRA); Lunar and Planetary Institute (LPI); Jet Propulsion Laboratory (JPL)Conveners: Sabrina Feldman, Jet Propulsion Laboratory, David Beaty, Jet Propulsion Laboratory ; Science Organizing Committee: Carlton Allen, Johnson Space Center (retired) [and 12 others