Contribuciones sobre métodos óptimos y subóptimos de aproximaciones poligonales de curvas 2-D

Esta tesis versa sobre el an álisis de la forma de objetos 2D. En visión articial existen numerosos aspectos de los que se pueden extraer información. Uno de los más usados es la forma o el contorno de esos objetos. Esta característica visual de los objetos nos permite, mediante el procesamiento adecuado, extraer información de los objetos, analizar escenas, etc. No obstante el contorno o silueta de los objetos contiene información redundante. Este exceso de datos que no aporta nuevo conocimiento debe ser eliminado, con el objeto de agilizar el procesamiento posterior o de minimizar el tamaño de la representación de ese contorno, para su almacenamiento o transmisión. Esta reducción de datos debe realizarse sin que se produzca una pérdida de información importante para representación del contorno original. Se puede obtener una versión reducida de un contorno eliminando puntos intermedios y uniendo los puntos restantes mediante segmentos. Esta representación reducida de un contorno se conoce como aproximación poligonal. Estas aproximaciones poligonales de contornos representan, por tanto, una versión comprimida de la información original. El principal uso de las mismas es la reducción del volumen de información necesario para representar el contorno de un objeto. No obstante, en los últimos años estas aproximaciones han sido usadas para el reconocimiento de objetos. Para ello los algoritmos de aproximaci ón poligonal se han usado directamente para la extracci ón de los vectores de caracter ísticas empleados en la fase de aprendizaje. Las contribuciones realizadas por tanto en esta tesis se han centrado en diversos aspectos de las aproximaciones poligonales. En la primera contribución se han mejorado varios algoritmos de aproximaciones poligonales, mediante el uso de una fase de preprocesado que acelera estos algoritmos permitiendo incluso mejorar la calidad de las soluciones en un menor tiempo. En la segunda contribución se ha propuesto un nuevo algoritmo de aproximaciones poligonales que obtiene soluciones optimas en un menor espacio de tiempo que el resto de métodos que aparecen en la literatura. En la tercera contribución se ha propuesto un algoritmo de aproximaciones que es capaz de obtener la solución óptima en pocas iteraciones en la mayor parte de los casos. Por último, se ha propuesto una versi ón mejorada del algoritmo óptimo para obtener aproximaciones poligonales que soluciona otro problema de optimización alternativo.This thesis focus on the analysis of the shape of objects. In computer vision there are several sources from which we can extract information. One of the most important source of information is the shape or contour of objects. This visual characteristic can be used to extract information, analyze the scene, etc. However, the contour of the objects contains redundant information. This redundant data does not add new information and therefore, must be deleted in order to minimize the processing burden and reducing the amount of data to represent that shape. This reduction of data should be done without losing important information to represent the original contour. A reduced version of a contour can be obtained by deleting some points of the contour and linking the remaining points by using line segments. This reduced version of a contour is known as polygonal approximation in the literature. Therefore, these polygonal approximation represent a compressed version of the original information. The main use of polygonal approximations is to reduce the amount of information needed to represent the contour of an object. However, in recent years polygonal approximations have been used to recognize objects. For this purpose, the feature vectors have been extracted from the polygonal approximations. The contributions proposed in this thesis have focused on several aspects of polygonal approximations. The rst contribution has improved several algorithms to obtain polygonal approximations, by adding a new stage of preprocessing which boost the whole method. The quality of the solutions obtained has also been improved and the computation time reduced. The second contribution proposes a novel algorithm which obtains optimal polygonal approximations in a shorter time than the optimal methods found in the literature. The third contribution proposes a new method which may obtain the optimal solution after few iterations in most cases. Finally, an improved version of the optimal polygonal approximation algorithm has been proposed to solve an alternative optimization problem

The Influence of Measurement Scale and Uncertainty on Interpretations of River Migration

Environmental scientists increasingly use remotely-sensed images to measure how rivers develop over time and respond to upstream changes in environmental drivers such as land use, urbanization, deforestation and agricultural practices. These measurements are subject to uncertainty that can bias conclusions. The first step towards accurate interpretation of river channel change is properly quantifying and accounting for uncertainty involved in measuring changes in river morphology. In Chapter 2 we develop a comprehensive framework for quantifying uncertainty in measurements of river change derived from aerial images. The framework builds upon previous uncertainty research by describing best practices and context-specific strategies, comparing each approach and outlining how to best handle measurements that fall below the minimum level of detection. We use this framework in subsequent chapters to reduce the impact of erroneous measurements. Chapter 3 evaluates how the time interval between aerial images influences the rates at which river channels appear to laterally migrate across their floodplains. Multiple lines of evidence indicate that river migration measurements obtained over longer time intervals (20+ years) will underestimate the ‘true’ rate because the river channel is more likely to have reversed the direction of migration, which erases part of the record of gross erosion as seen from aerial images. If the images don’t capture channel reversals and periodic episodes of fast erosion, the river appears to have migrated a shorter distance (which corresponds to a slower rate) than reality. Obtaining multiple measurements over shorter time intervals (\u3c 5 years) and limiting direct comparisons to similar time intervals can reduce bias when inferring how river migration rates may have changed over time. Chapter 4 explores the physical processes governing the relationship between river curvature and the rate of river migration along a series of meander bends. We used fine-scale empirical measurements and geospatial analyses to confirm theory and models indicating that migration and curvature exhibit a monotonic relationship. The results will improve models seeking to emulate river meander migration patterns

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Proceedings of the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008

This volume contains full papers presented at the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008, held in Braga, Portugal, between September 4th and 6th, 2008.FC