Um algoritmo morfologico para processamento de fotomosaicos

Orientador: Neucimar Jeronimo LeiteDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Ciencia da ComputaçãoResumo: Nós definimos, neste trabalho, um método morfológico para combinar imagens que se interceptam através de um processo denominado photomosaicking. Um fotomosaico é definido considerando a região de interseção, entre duas imagens, na qual uma linha de costura é detectada. O método descrito aqui utiliza a noção de linha de divisor de águas de uma função para extrair informações globais de uma imagem representando a homogeneidade entre as regiões de interseção. A partir destas informações globais, nós obtemos uma linha de costura conexa e irregular, e portanto mais realista do que aquelas definidas pelos métodos existentes.Abstract: We define a morphological algorithm for combining two overlapping images into a single one by a process named photomosaicking. A photomosaic is defined considering an overlap region between two or more images, in which we detect a join line or seam. The method described here considers the notion of the watersheds of a function to extract global informations of an image denoting the homogeneity between two overlapping regions. By taking into account global informations of the overlap region, the watershed algorithm defines a seam which is connected, irregular and thus more realistic than the existing methods for photomosaicking.MestradoMestre em Ciência da Computaçã

Algoritmo de fotomosaico para imagens coloridas por morfologia matematica

Orientador: Neucimar Jeronimo LeiteDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Esta dissertação apresenta um algoritmo para combinar duas imagens coloridas, contendo uma região de interseção em comum, através de um método denominado fotomosaico. A combinação das imagens é estabelecida a partir de uma linha de costura que não deve ser perceptível a um observador e que determina onde uma imagem começa e a outra termina. O algoritmo apresentado neste trabalho é baseado nas transformações morfológicas de linha divisora de águas (watershed) e reconstrução.Abstract: This work introduces an algorithm for combining two color images with common intersection region by a method named photomosaicking. In this method, we search for a non-perceptible seam line which indicates in the final photomosaic where one image begins and other one ends. The algorithm presented here is based mainly on the watershed and reconstruction morphological transformations.MestradoMestre em Ciência da Computaçã

Application of structured light imaging for high resolution mapping of underwater archaeological sites

This paper presents results from recent work using structured light laser profile imaging to create high resolution bathymetric maps of underwater archaeological sites. Documenting the texture and structure of submerged sites is a difficult task and many applicable acoustic and photographic mapping techniques have recently emerged. This effort was completed to evaluate laser profile imaging in comparison to stereo imaging and high frequency multibeam mapping. A ROV mounted camera and inclined 532 nm sheet laser were used to create profiles of the bottom that were then merged into maps using platform navigation data. These initial results show very promising resolution in comparison to multibeam and stereo reconstructions, particularly in low contrast scenes. At the test sites shown here there were no significant complications related to scattering or attenuation of the laser sheet by the water. The resulting terrain was gridded at 0.25 cm and shows overall centimeter level definition. The largest source of error was related to the calibration of the laser and camera geometry. Results from three small areas show the highest resolution 3D models of a submerged archaeological site to date and demonstrate that laser imaging will be a viable method for accurate three dimensional site mapping and documentation

Using vector building maps to aid in generating seams for low-attitude aerial orthoimage mosaicking: Advantages in avoiding the crossing of buildings

A novel seam detection approach based on vector building maps is presented for low-attitude aerial orthoimage mosaicking. The approach tracks the centerlines between vector buildings to generate the candidate seams that avoid crossing buildings existing in maps. The candidate seams are then refined by considering their surrounding pixels to minimize the visual transition between the images to be mosaicked. After the refinement of the candidate seams, the final seams further bypass most of the buildings that are not updated into vector maps. Finally, three groups of aerial imagery from different urban densities are employed to test the proposed approach. The experimental results illustrate the advantages of the proposed approach in avoiding the crossing of buildings. The computational efficiency of the proposed approach is also significantly higher than that of Dijkstra’s algorithm

Lossy compression and real-time geovisualization for ultra-low bandwidth telemetry from untethered underwater vehicles

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2008Oceanographic applications of robotics are as varied as the undersea environment itself. As underwater robotics moves toward the study of dynamic processes with multiple vehicles, there is an increasing need to distill large volumes of data from underwater vehicles and deliver it quickly to human operators. While tethered robots are able to communicate data to surface observers instantly, communicating discoveries is more difficult for untethered vehicles. The ocean imposes severe limitations on wireless communications; light is quickly absorbed by seawater, and tradeoffs between frequency, bitrate and environmental effects result in data rates for acoustic modems that are routinely as low as tens of bits per second. These data rates usually limit telemetry to state and health information, to the exclusion of mission-specific science data. In this thesis, I present a system designed for communicating and presenting science telemetry from untethered underwater vehicles to surface observers. The system's goals are threefold: to aid human operators in understanding oceanographic processes, to enable human operators to play a role in adaptively responding to mission-specific data, and to accelerate mission planning from one vehicle dive to the next. The system uses standard lossy compression techniques to lower required data rates to those supported by commercially available acoustic modems (O(10)-O(100) bits per second). As part of the system, a method for compressing time-series science data based upon the Discrete Wavelet Transform (DWT) is explained, a number of low-bitrate image compression techniques are compared, and a novel user interface for reviewing transmitted telemetry is presented. Each component is motivated by science data from a variety of actual Autonomous Underwater Vehicle (AUV) missions performed in the last year.National Science Foundation Center for Subsurface Sensing and Imaging (CenSSIS ERC

Computational strategies for understanding underwater optical image datasets

Thesis: Ph. D. in Mechanical and Oceanographic Engineering, Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 117-135).A fundamental problem in autonomous underwater robotics is the high latency between the capture of image data and the time at which operators are able to gain a visual understanding of the survey environment. Typical missions can generate imagery at rates hundreds of times greater than highly compressed images can be transmitted acoustically, delaying that understanding until after the vehicle has been recovered and the data analyzed. While automated classification algorithms can lessen the burden on human annotators after a mission, most are too computationally expensive or lack the robustness to run in situ on a vehicle. Fast algorithms designed for mission-time performance could lessen the latency of understanding by producing low-bandwidth semantic maps of the survey area that can then be telemetered back to operators during a mission. This thesis presents a lightweight framework for processing imagery in real time aboard a robotic vehicle. We begin with a review of pre-processing techniques for correcting illumination and attenuation artifacts in underwater images, presenting our own approach based on multi-sensor fusion and a strong physical model. Next, we construct a novel image pyramid structure that can reduce the complexity necessary to compute features across multiple scales by an order of magnitude and recommend features which are fast to compute and invariant to underwater artifacts. Finally, we implement our framework on real underwater datasets and demonstrate how it can be used to select summary images for the purpose of creating low-bandwidth semantic maps capable of being transmitted acoustically.by Jeffrey W. Kaeli.Ph. D. in Mechanical and Oceanographic Engineerin

Semantic Segmentation for Real-World Applications

En visión por computador, la comprensión de escenas tiene como objetivo extraer información útil de una escena a partir de datos de sensores. Por ejemplo, puede clasificar toda la imagen en una categoría particular o identificar elementos importantes dentro de ella. En este contexto general, la segmentación semántica proporciona una etiqueta semántica a cada elemento de los datos sin procesar, por ejemplo, a todos los píxeles de la imagen o, a todos los puntos de la nube de puntos. Esta información es esencial para muchas aplicaciones de visión por computador, como conducción, aplicaciones médicas o robóticas. Proporciona a los ordenadores una comprensión sobre el entorno que es necesaria para tomar decisiones autónomas.El estado del arte actual de la segmentación semántica está liderado por métodos de aprendizaje profundo supervisados. Sin embargo, las condiciones del mundo real presentan varias restricciones para la aplicación de estos modelos de segmentación semántica. Esta tesis aborda varios de estos desafíos: 1) la cantidad limitada de datos etiquetados disponibles para entrenar modelos de aprendizaje profundo, 2) las restricciones de tiempo y computación presentes en aplicaciones en tiempo real y/o en sistemas con poder computacional limitado, y 3) la capacidad de realizar una segmentación semántica cuando se trata de sensores distintos de la cámara RGB estándar.Las aportaciones principales en esta tesis son las siguientes:1. Un método nuevo para abordar el problema de los datos anotados limitados para entrenar modelos de segmentación semántica a partir de anotaciones dispersas. Los modelos de aprendizaje profundo totalmente supervisados lideran el estado del arte, pero mostramos cómo entrenarlos usando solo unos pocos píxeles etiquetados. Nuestro enfoque obtiene un rendimiento similar al de los modelos entrenados con imágenescompletamente etiquetadas. Demostramos la relevancia de esta técnica en escenarios de monitorización ambiental y en dominios más generales.2. También tratando con datos de entrenamiento limitados, proponemos un método nuevo para segmentación semántica semi-supervisada, es decir, cuando solo hay una pequeña cantidad de imágenes completamente etiquetadas y un gran conjunto de datos sin etiquetar. La principal novedad de nuestro método se basa en el aprendizaje por contraste. Demostramos cómo el aprendizaje por contraste se puede aplicar a la tarea de segmentación semántica y mostramos sus ventajas, especialmente cuando la disponibilidad de datos etiquetados es limitada logrando un nuevo estado del arte.3. Nuevos modelos de segmentación semántica de imágenes eficientes. Desarrollamos modelos de segmentación semántica que son eficientes tanto en tiempo de ejecución, requisitos de memoria y requisitos de cálculo. Algunos de nuestros modelos pueden ejecutarse en CPU a altas velocidades con alta precisión. Esto es muy importante para configuraciones y aplicaciones reales, ya que las GPU de gama alta nosiempre están disponibles.4. Nuevos métodos de segmentación semántica con sensores no RGB. Proponemos un método para la segmentación de nubes de puntos LiDAR que combina operaciones de aprendizaje eficientes tanto en 2D como en 3D. Logra un rendimiento de segmentación excepcional a velocidades realmente rápidas. También mostramos cómo mejorar la robustez de estos modelos al abordar el problema de sobreajuste y adaptaciónde dominio. Además, mostramos el primer trabajo de segmentación semántica con cámaras de eventos, haciendo frente a la falta de datos etiquetados.Estas contribuciones aportan avances significativos en el campo de la segmentación semántica para aplicaciones del mundo real. Para una mayor contribución a la comunidad cientfíica, hemos liberado la implementación de todas las soluciones propuestas.----------------------------------------In computer vision, scene understanding aims at extracting useful information of a scene from raw sensor data. For instance, it can classify the whole image into a particular category (i.e. kitchen or living room) or identify important elements within it (i.e., bottles, cups on a table or surfaces). In this general context, semantic segmentation provides a semantic label to every single element of the raw data, e.g., to all image pixels or to all point cloud points.This information is essential for many applications relying on computer vision, such as AR, driving, medical or robotic applications. It provides computers with understanding about the environment needed to make autonomous decisions, or detailed information to people interacting with the intelligent systems. The current state of the art for semantic segmentation is led by supervised deep learning methods.However, real-world scenarios and conditions introduce several challenges and restrictions for the application of these semantic segmentation models. This thesis tackles several of these challenges, namely, 1) the limited amount of labeled data available for training deep learning models, 2) the time and computation restrictions present in real time applications and/or in systems with limited computational power, such as a mobile phone or an IoT node, and 3) the ability to perform semantic segmentation when dealing with sensors other than the standard RGB camera.The general contributions presented in this thesis are following:A novel approach to address the problem of limited annotated data to train semantic segmentation models from sparse annotations. Fully supervised deep learning models are leading the state-of-the-art, but we show how to train them by only using a few sparsely labeled pixels in the training images. Our approach obtains similar performance than models trained with fully-labeled images. We demonstrate the relevance of this technique in environmental monitoring scenarios, where it is very common to have sparse image labels provided by human experts, as well as in more general domains. Also dealing with limited training data, we propose a novel method for semi-supervised semantic segmentation, i.e., when there is only a small number of fully labeled images and a large set of unlabeled data. We demonstrate how contrastive learning can be applied to the semantic segmentation task and show its advantages, especially when the availability of labeled data is limited. Our approach improves state-of-the-art results, showing the potential of contrastive learning in this task. Learning from unlabeled data opens great opportunities for real-world scenarios since it is an economical solution. Novel efficient image semantic segmentation models. We develop semantic segmentation models that are efficient both in execution time, memory requirements, and computation requirements. Some of our models able to run in CPU at high speed rates with high accuracy. This is very important for real set-ups and applications since high-end GPUs are not always available. Building models that consume fewer resources, memory and time, would increase the range of applications that can benefit from them. Novel methods for semantic segmentation with non-RGB sensors.We propose a novel method for LiDAR point cloud segmentation that combines efficient learning operations both in 2D and 3D. It surpasses state-of-the-art segmentation performance at really fast rates. We also show how to improve the robustness of these models tackling the overfitting and domain adaptation problem. Besides, we show the first work for semantic segmentation with event-based cameras, coping with the lack of labeled data. To increase the impact of this contributions and ease their application in real-world settings, we have made available an open-source implementation of all proposed solutions to the scientific community.<br /

Remote sensing and GIS-based analysis of hydrocarbon seeps: Detection, mapping, and quantification

The thesis aims to elucidate the transport and fate of hydrocarbon emissions from deep-sea seeps through the water column towards the atmosphere. An array of hydroacoustic, satellite, and optical imaging techniques was employed to detect, map, and quantify such seeps and accompanying oil and gas emissions. The major finding is that gas transport via bubbles is the overwhelming mechanism, to transfer hydrocarbons to the hydrosphere. However, only at seeps that discharge oil and gas (oily gas bubbles) these emissions might reach the sea surface and atmosphere. At other sites gas dissolves in the water column, thus not representing a primary source of atmospheric methane and higher hydrocarbon concentrations. Therefore it is suggested to focus research on oil seeps when aiming to study the potential effect of marine hydrocarbon seeps on the present climate

Autonomous marine environmental monitoring: Application in decommissioned oil fields

Hundreds of Oil & Gas Industry structures in the marine environment are approaching decommissioning. In most areas decommissioning operations will need to be supported by environmental assessment and monitoring, potentially over the life of any structures left in place. This requirement will have a considerable cost for industry and the public. Here we review approaches for the assessment of the primary operating environments associated with decommissioning — namely structures, pipelines, cuttings piles, the general seabed environment and the water column — and show that already available marine autonomous systems (MAS) offer a wide range of solutions for this major monitoring challenge. Data of direct relevance to decommissioning can be collected using acoustic, visual, and oceanographic sensors deployed on MAS. We suggest that there is considerable potential for both cost savings and a substantial improvement in the temporal and spatial resolution of environmental monitoring. We summarise the trade-offs between MAS and current conventional approaches to marine environmental monitoring. MAS have the potential to successfully carry out much of the monitoring associated with decommissioning and to offer viable alternatives where a direct match for the conventional approach is not possible