Underwater Localization in Complex Environments

A capacidade de um veículo autónomo submarino (AUV) se localizar num ambiente complexo, bem como de extrair características relevantes do mesmo, é de grande importância para o sucesso da navegação. No entanto, esta tarefa é particularmente desafiante em ambientes subaquáticos devido à rápida atenuação sofrida pelos sinais de sistemas de posicionamento global ou outros sinais de radiofrequência, dispersão e reflexão, sendo assim necessário o uso de processos de filtragem. Ambiente complexo é definido aqui como um cenário com objetos destacados das paredes, por exemplo, o objeto pode ter uma certa variabilidade de orientação, portanto a sua posição nem sempre é conhecida. Exemplos de cenários podem ser um porto, um tanque ou mesmo uma barragem, onde existem paredes e dentro dessas paredes um AUV pode ter a necessidade de se localizar de acordo com os outros veículos na área e se posicionar em relação ao mesmo e analisá-lo. Os veículos autónomos empregam muitos tipos diferentes de sensores para localização e percepção dos seus ambientes e dependem dos computadores de bordo para realizar tarefas de direção autónoma. Para esta dissertação há um problema concreto a resolver, localizar um cabo suspenso numa coluna de água em uma região conhecida do mar e navegar de acordo com ela. Embora a posição do cabo no mundo seja bem conhecida, a dinâmica do cabo não permite saber exatamente onde ele está. Assim, para que o veículo se localize de acordo com este para que possa ser inspecionado, a localização deve ser baseada em sensores ópticos e acústicos. Este estudo explora o processamento e a análise de imagens óticas e acústicas, por meio dos dados adquiridos através de uma câmara e por um sonar de varrimento mecânico (MSIS),respetivamente, a fim de extrair características ambientais relevantes que possibilitem a estimação da localização do veículo. Os pontos de interesse extraídos de cada um dos sensores são utilizados para alimentar um estimador de posição, implementando um Filtro de Kalman Extendido (EKF), de modo a estimar a posição do cabo e através do feedback do filtro melhorar os processos de extração de pontos de interesse utilizados.The ability of an autonomous underwater vehicle (AUV) to locate itself in a complex environment as well as to detect relevant environmental features is of crucial importance for successful navigation. However, it's particularly challenging in underwater environments due to the rapid attenuation suffered by signals from global positioning systems or other radio frequency signals, dispersion and reflection thus needing a filtering process. Complex environment is defined here as a scenario with objects detached from the walls, for example the object can have a certain orientation variability therefore its position is not always known. Examples of scenarios can be a harbour, a tank or even a dam reservoir, where there are walls and within those walls an AUV may have the need to localize itself according to the other vehicles in the area and position itself relative to one to observe, analyse or scan it. Autonomous vehicles employ many different types of sensors for localization and perceiving their environments and they depend on the on-board computers to perform autonomous driving tasks. For this dissertation there is a concrete problem to solve, which is to locate a suspended cable in a water column in a known region in the sea and navigate according to it. Although the cable position in the world is well known, the cable dynamics does not allow knowing where it is exactly. So, in order to the vehicle localize itself according to it so it can be inspected, the localization has to be based on optical and acoustic sensors. This study explores the processing and analysis of optical and acoustic images, through the data acquired through a camera and by a mechanical scanning sonar (MSIS), respectively, in order to extract relevant environmental characteristics that allow the estimation of the location of the vehicle. The points of interest extracted from each of the sensors are used to feed a position estimator, by implementing an Extended Kalman Filter (EKF), in order to estimate the position of the cable and through the feedback of the filter improve the extraction processes of points of interest used

Combining crowd worker, algorithm, and expert efforts to find boundaries of objects in images

While traditional approaches to image analysis have typically relied upon either manual annotation by experts or purely-algorithmic approaches, the rise of crowdsourcing now provides a new source of human labor to create training data or perform computations at run-time. Given this richer design space, how should we utilize algorithms, crowds, and experts to better annotate images? To answer this question for the important task of finding the boundaries of objects or regions in images, I focus on image segmentation, an important precursor to solving a variety of fundamental image analysis problems, including recognition, classification, tracking, registration, retrieval, and 3D visualization. The first part of the work includes a detailed analysis of the relative strengths and weaknesses of three different approaches to demarcate object boundaries in images: by experts, by crowdsourced laymen, and by automated computer vision algorithms. The second part of the work describes three hybrid system designs that integrate computer vision algorithms and crowdsourced laymen to demarcate boundaries in images. Experiments revealed that hybrid system designs yielded more accurate results than relying on algorithms or crowd workers alone and could yield segmentations that are indistinguishable from those created by biomedical experts. To encourage community-wide effort to continue working on developing methods and systems for image-based studies which can have real and measurable impact that benefit society at large, datasets and code are publicly-shared (http://www.cs.bu.edu/~betke/BiomedicalImageSegmentation/)

Combining crowd worker, algorithm, and expert efforts to find boundaries of objects in images

While traditional approaches to image analysis have typically relied upon either manual annotation by experts or purely-algorithmic approaches, the rise of crowdsourcing now provides a new source of human labor to create training data or perform computations at run-time. Given this richer design space, how should we utilize algorithms, crowds, and experts to better annotate images? To answer this question for the important task of finding the boundaries of objects or regions in images, I focus on image segmentation, an important precursor to solving a variety of fundamental image analysis problems, including recognition, classification, tracking, registration, retrieval, and 3D visualization. The first part of the work includes a detailed analysis of the relative strengths and weaknesses of three different approaches to demarcate object boundaries in images: by experts, by crowdsourced laymen, and by automated computer vision algorithms. The second part of the work describes three hybrid system designs that integrate computer vision algorithms and crowdsourced laymen to demarcate boundaries in images. Experiments revealed that hybrid system designs yielded more accurate results than relying on algorithms or crowd workers alone and could yield segmentations that are indistinguishable from those created by biomedical experts. To encourage community-wide effort to continue working on developing methods and systems for image-based studies which can have real and measurable impact that benefit society at large, datasets and code are publicly-shared (http://www.cs.bu.edu/~betke/BiomedicalImageSegmentation/)

An Adaptive Algorithm to Identify Ambiguous Prostate Capsule Boundary Lines for Three-Dimensional Reconstruction and Quantitation

Currently there are few parameters that are used to compare the efficiency of different methods of cancerous prostate surgical removal. An accurate assessment of the percentage and depth of extra-capsular soft tissue removed with the prostate by the various surgical techniques can help surgeons determine the appropriateness of surgical approaches. Additionally, an objective assessment can allow a particular surgeon to compare individual performance against a standard. In order to facilitate 3D reconstruction and objective analysis and thus provide more accurate quantitation results when analyzing specimens, it is essential to automatically identify the capsule line that separates the prostate gland tissue from its extra-capsular tissue. However the prostate capsule is sometimes unrecognizable due to the naturally occurring intrusion of muscle and connective tissue into the prostate gland. At these regions where the capsule disappears, its contour can be arbitrarily reconstructed by drawing a continuing contour line based on the natural shape of the prostate gland. Presented here is a mathematical model that can be used in deciding the missing part of the capsule. This model approximates the missing parts of the capsule where it disappears to a standard shape by using a Generalized Hough Transform (GHT) approach to detect the prostate capsule. We also present an algorithm based on a least squares curve fitting technique that uses a prostate shape equation to merge previously detected capsule parts with the curve equation to produce an approximated curve that represents the prostate capsule. We have tested our algorithms using three shapes on 13 prostate slices that are cut at different locations from the apex and the results are promisin