Non-parametric synthesis of laminar volumetric texture

International audienceThe goal of this paper is to evaluate several extensions of Wei and Levoy's algorithm for the synthesis of laminar volumetric textures constrained only by a single 2D sample. Hence, we shall also review in a unified form the improved algorithm proposed by Kopf et al. and the particular histogram matching approach of Chen and Wang. Developing a genuine quantitative study we are able to compare the performances of these algorithms that we have applied to the synthesis of volumetric structures of dense carbons. The 2D samples are lattice fringe images obtained by high resolution transmission electronic microscopy (HRTEM)

Theory and Validation of Magnetic Resonance Fluid Motion Estimation Using Intensity Flow Data

15 p.Background Motion tracking based on spatial-temporal radio-frequency signals from the pixel representation of magnetic resonance (MR) imaging of a non-stationary fluid is able to provide two dimensional vector field maps. This supports the underlying fundamentals of magnetic resonance fluid motion estimation and generates a new methodology for flow measurement that is based on registration of nuclear signals from moving hydrogen nuclei in fluid. However, there is a need to validate the computational aspect of the approach by using velocity flow field data that we will assume as the true reference information or ground truth. Methodology/Principal Findings In this study, we create flow vectors based on an ideal analytical vortex, and generate artificial signal-motion image data to verify our computational approach. The analytical and computed flow fields are compared to provide an error estimate of our methodology. The comparison shows that the fluid motion estimation approach using simulated MR data is accurate and robust enough for flow field mapping. To verify our methodology, we have tested the computational configuration on magnetic resonance images of cardiac blood and proved that the theory of magnetic resonance fluid motion estimation can be applicable practically. Conclusions/Significance The results of this work will allow us to progress further in the investigation of fluid motion prediction based on imaging modalities that do not require velocity encoding. This article describes a novel theory of motion estimation based on magnetic resonating blood, which may be directly applied to cardiac flow imaging.Kelvin Kian Loong Wong, Richard Malcolm Kelso, Stephen Grant Worthley, Prashanthan Sanders, Jagannath Mazumdar, Derek Abbot

Analysis, estimation and control for perturbed and singular systems for systems subject to discrete events.

"The principle investigator for this effort is Professor Alan S. Willsky, and Professor George C. Verghese is co-principal investigator."--P. [3].Includes bibliographical references (p. [20]-[25]).Final technical report for grant AFOSR-88-0032.Supported by the AFOSR. AFOSR-88-003

Super-resolução em vídeos de baixa qualidade para aplicações forenses, de vigilância e móveis

Orientadores: Siome Klein Goldenstein, Anderson de Rezende RochaTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Algoritmos de super-resolução (SR) são métodos para obter um aumento da resolução de imagens compostas por pixels. Na super-resolução por múltiplas imagens, um conjunto de imagens de baixa resolução de uma cena é combinado para construir uma imagem de resolução superior. Super-resolução é uma solução barata para superar as limitações dos sistemas de aquisição de imagens, e pode ser útil em diversos casos em que o dispositivo não pode ser melhorado ou substituído - mas em que é possível obter diversas capturas da mesma cena. Neste trabalho, é explorada a super-resolução por múltiplas imagens para imagens naturais, em cenários nos quais é possível obter diversas imagens de uma cena. São propostas cinco variações de um método que explora propriedades geométricas de múltiplas imagens de baixa resolução para combiná-las em uma imagem de resolução superior; duas variações de um método que combina técnicas de inpainting e super-resolução; e mais três variações de um método que utiliza filtros adaptativos e regularização para resolver um problema de mínimos quadrados. Super-resolução por múltiplas imagens é possível quando existe movimento e informações não redundantes entre as imagens de baixa resolução. Entretanto, combiná-las em uma imagem de resolução superior pode não ser computacionalmente viável por técnicas complexas de super-resolução. A primeira aplicação dos métodos propostos é para um conjunto de imagens capturadas pelos dispositivos móveis mais recentes. Este tipo de ambiente requer algoritmos eficazes que sejam executados rapidamente e utilizando baixo consumo de memória. A segunda aplicação é na Ciência Forense. Câmeras de vigilância espalhadas pelas cidades poderiam fornecer dicas importantes para identificar um suspeito, por exemplo, em uma cena de crime. Entretanto, o reconhecimento dos caracteres de placas veiculares é especialmente difícil quando a resolução das imagens é baixa. Por isso, este trabalho também propõe um arcabouço que realiza a super-resolução de placas veiculares em vídeos reais de vigilância, capturados por câmeras de baixa qualidade e não projetadas especificamente para esta tarefa, ajudando o especialista forense a compreender um evento de interesse. O arcabouço realiza todas as etapas necessárias para rastrear, alinhar, reconstruir e reconhecer automaticamente os caracteres de uma placa suspeita. O usuário recebe, como saída, a imagem de alta resolução reconstruída, mais rica em detalhes, e também a sequência de caracteres reconhecida automaticamente nesta imagem. São apresentadas validações quantitativas e qualitativas dos algoritmos propostos e de suas aplicações. Os experimentos mostram, por exemplo, que é possível aumentar o número de caracteres reconhecidos corretamente, colocando o arcabouço proposto como uma ferramenta importante para fornecer aos peritos uma solução para o reconhecimento de placas veiculares sob condições adversas de aquisição. Por fim, também é sugerido o número mínimo de imagens a ser utilizada como entrada em cada aplicaçãoAbstract: Super-resolution (SR) algorithms are methods for achieving high-resolution (HR) enlargements of pixel-based images. In multi-frame super resolution, a set of low-resolution (LR) images of a scene are combined to construct an image with higher resolution. Super resolution is an inexpensive solution to overcome the limitations of image acquisition hardware systems, and can be useful in several cases in which the device cannot be upgraded or replaced, but multiple frames of the same scene can be obtained. In this work, we explore SR possibilities for natural images, in scenarios wherein we have multiple frames of a same scene. We design and develop five variations of an algorithm which rely on exploring geometric properties in order to combine pixels from LR observations into an HR grid; two variations of a method that combines inpainting techniques to multi-frame super resolution; and three variations of an algorithm that uses adaptive filtering and Tikhonov regularization to solve a least-square problem. Multi-frame super resolution is possible when there is motion and non-redundant information from LR observations. However, combining a large number of frames into a higher resolution image may not be computationally feasible by complex super-resolution techniques. The first application of the proposed methods is in consumer-grade photography with a setup in which several low-resolution images gathered by recent mobile devices can be combined to create a much higher resolution image. Such always-on low-power environment requires effective high-performance algorithms, that run fastly and with a low-memory footprint. The second application is in Digital Forensic, with a setup in which low-quality surveillance cameras throughout the cities could provide important cues to identify a suspect vehicle, for example, in a crime scene. However, license-plate recognition is especially difficult under poor image resolutions. Hence, we design and develop a novel, free and open-source framework underpinned by SR and Automatic License-Plate Recognition (ALPR) techniques to identify license-plate characters in low-quality real-world traffic videos, captured by cameras not designed for the ALPR task, aiding forensic analysts in understanding an event of interest. The framework handles the necessary conditions to identify a target license plate, using a novel methodology to locate, track, align, super resolve, and recognize its alphanumerics. The user receives as outputs the rectified and super-resolved license-plate, richer in details, and also the sequence of license-plates characters that have been automatically recognized in the super-resolved image. We present quantitative and qualitative validations of the proposed algorithms and its applications. Our experiments show, for example, that SR can increase the number of correctly recognized characters posing the framework as an important step toward providing forensic experts and practitioners with a solution for the license-plate recognition problem under difficult acquisition conditions. Finally, we also suggest a minimum number of images to use as input in each applicationDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação1197478,146886153996/3-2015CAPESCNP

Keyframe-based monocular SLAM: design, survey, and future directions

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery