A Variational Stereo Method for the Three-Dimensional Reconstruction of Ocean Waves

We develop a novel remote sensing technique for the observation of waves on the ocean surface. Our method infers the 3-D waveform and radiance of oceanic sea states via a variational stereo imagery formulation. In this setting, the shape and radiance of the wave surface are given by minimizers of a composite energy functional that combines a photometric matching term along with regularization terms involving the smoothness of the unknowns. The desired ocean surface shape and radiance are the solution of a system of coupled partial differential equations derived from the optimality conditions of the energy functional. The proposed method is naturally extended to study the spatiotemporal dynamics of ocean waves and applied to three sets of stereo video data. Statistical and spectral analysis are carried out. Our results provide evidence that the observed omnidirectional wavenumber spectrum S(k) decays as k-2.5 is in agreement with Zakharov's theory (1999). Furthermore, the 3-D spectrum of the reconstructed wave surface is exploited to estimate wave dispersion and currents

A Variational Wave Acquisition Stereo System for the 3-D Reconstruction of Oceanic Sea States

We propose a novel remote sensing technique that infers the three-dimensional wave form and radiance of oceanic sea states via a variational stereo imagery formulation. In this setting, the shape and radiance of the wave surface are minimizers of a composite cost functional which combines a data fidelity term and smoothness priors on the unknowns. The solution of a system of coupled partial differential equations derived from the cost functional yields the desired ocean surface shape and radiance. The proposed method is naturally extended to study the spatio-temporal dynamics of ocean waves, and applied to three sets of video data. Statistical and spectral analysis are carried out. The results shows evidence of the fact that the omni-directional wavenumber spectrum S(k) of the reconstructed waves decays as k^{-2.5} in agreement with Zakharov's theory (1999). Further, the three-dimensional spectrum of the reconstructed wave surface is exploited to estimate wave dispersion and currents

Angular variation as a monocular cue for spatial percepcion

Monocular cues are spatial sensory inputs which are picked up exclusively from one eye. They are in majority static features that provide depth information and are extensively used in graphic art to create realistic representations of a scene. Since the spatial information contained in these cues is picked up from the retinal image, the existence of a link between it and the theory of direct perception can be conveniently assumed. According to this theory, spatial information of an environment is directly contained in the optic array. Thus, this assumption makes possible the modeling of visual perception processes through computational approaches. In this thesis, angular variation is considered as a monocular cue, and the concept of direct perception is adopted by a computer vision approach that considers it as a suitable principle from which innovative techniques to calculate spatial information can be developed. The expected spatial information to be obtained from this monocular cue is the position and orientation of an object with respect to the observer, which in computer vision is a well known field of research called 2D-3D pose estimation. In this thesis, the attempt to establish the angular variation as a monocular cue and thus the achievement of a computational approach to direct perception is carried out by the development of a set of pose estimation methods. Parting from conventional strategies to solve the pose estimation problem, a first approach imposes constraint equations to relate object and image features. In this sense, two algorithms based on a simple line rotation motion analysis were developed. These algorithms successfully provide pose information; however, they depend strongly on scene data conditions. To overcome this limitation, a second approach inspired in the biological processes performed by the human visual system was developed. It is based in the proper content of the image and defines a computational approach to direct perception. The set of developed algorithms analyzes the visual properties provided by angular variations. The aim is to gather valuable data from which spatial information can be obtained and used to emulate a visual perception process by establishing a 2D-3D metric relation. Since it is considered fundamental in the visual-motor coordination and consequently essential to interact with the environment, a significant cognitive effect is produced by the application of the developed computational approach in environments mediated by technology. In this work, this cognitive effect is demonstrated by an experimental study where a number of participants were asked to complete an action-perception task. The main purpose of the study was to analyze the visual guided behavior in teleoperation and the cognitive effect caused by the addition of 3D information. The results presented a significant influence of the 3D aid in the skill improvement, which showed an enhancement of the sense of presence.Las señales monoculares son entradas sensoriales capturadas exclusivamente por un solo ojo que ayudan a la percepción de distancia o espacio. Son en su mayoría características estáticas que proveen información de profundidad y son muy utilizadas en arte gráfico para crear apariencias reales de una escena. Dado que la información espacial contenida en dichas señales son extraídas de la retina, la existencia de una relación entre esta extracción de información y la teoría de percepción directa puede ser convenientemente asumida. De acuerdo a esta teoría, la información espacial de todo le que vemos está directamente contenido en el arreglo óptico. Por lo tanto, esta suposición hace posible el modelado de procesos de percepción visual a través de enfoques computacionales. En esta tesis doctoral, la variación angular es considerada como una señal monocular, y el concepto de percepción directa adoptado por un enfoque basado en algoritmos de visión por computador que lo consideran un principio apropiado para el desarrollo de nuevas técnicas de cálculo de información espacial. La información espacial esperada a obtener de esta señal monocular es la posición y orientación de un objeto con respecto al observador, lo cual en visión por computador es un conocido campo de investigación llamado estimación de la pose 2D-3D. En esta tesis doctoral, establecer la variación angular como señal monocular y conseguir un modelo matemático que describa la percepción directa, se lleva a cabo mediante el desarrollo de un grupo de métodos de estimación de la pose. Partiendo de estrategias convencionales, un primer enfoque implanta restricciones geométricas en ecuaciones para relacionar características del objeto y la imagen. En este caso, dos algoritmos basados en el análisis de movimientos de rotación de una línea recta fueron desarrollados. Estos algoritmos exitosamente proveen información de la pose. Sin embargo, dependen fuertemente de condiciones de la escena. Para superar esta limitación, un segundo enfoque inspirado en los procesos biológicos ejecutados por el sistema visual humano fue desarrollado. Está basado en el propio contenido de la imagen y define un enfoque computacional a la percepción directa. El grupo de algoritmos desarrollados analiza las propiedades visuales suministradas por variaciones angulares. El propósito principal es el de reunir datos de importancia con los cuales la información espacial pueda ser obtenida y utilizada para emular procesos de percepción visual mediante el establecimiento de relaciones métricas 2D- 3D. Debido a que dicha relación es considerada fundamental en la coordinación visuomotora y consecuentemente esencial para interactuar con lo que nos rodea, un efecto cognitivo significativo puede ser producido por la aplicación de métodos de L estimación de pose en entornos mediados tecnológicamente. En esta tesis doctoral, este efecto cognitivo ha sido demostrado por un estudio experimental en el cual un número de participantes fueron invitados a ejecutar una tarea de acción-percepción. El propósito principal de este estudio fue el análisis de la conducta guiada visualmente en teleoperación y el efecto cognitivo causado por la inclusión de información 3D. Los resultados han presentado una influencia notable de la ayuda 3D en la mejora de la habilidad, así como un aumento de la sensación de presencia

Análisis del comportamiento hidrodinámico fluvial mediante razonamiento causal y visión artificial

Tesis por compendio de publicaciones[ES]La alteración de los patrones climáticos y meteorológicos así como su repercusión en el ciclo hidrológico, en gran medida atribuible al fenómeno del Calentamiento Global y puesto de manifiesto en las últimas décadas en numerosos estudios, son una realidad global, con dramáticas consecuencias ambientales, sociales y económicas. Eventos extremos tales como precipitaciones, sequias e inundaciones, resultan cada vez menos “anómalos”, y por tanto más recurrentes en el tiempo. Estas nuevas realidades, derivadas de una creciente no-estacionalidad de los procesos hidrológicos e hidráulicos, están provocando una mayor incertidumbre a la hora de modelar y predecir los comportamientos hidrológicos dentro un contexto de gestión sostenible y segura de los recursos hídricos de una cuenca, en el marco del paradigma internacional para la gestión integrada del agua, conocido como “Integrated Water Resources Management” (IWRM). Por otro lado, como sociedad constatamos que las medidas tomadas por parte de instituciones internacionales y gobiernos para revertir los efectos del Calentamiento Global están teniendo un efecto limitado a corto y medio plazo, como consecuencia de la persistencia de los efectos antropogénicos. Dichas medidas mayoritariamente son de adaptación y mitigación frente a estas nuevas realidades hidrológicas. Frente a esta situación, se hace necesario disponer de métodos y técnicas de análisis y observación, eficientes en tiempo y coste, que permitan generar modelos predictivos precisos que agilicen el proceso de toma de decisiones en el ámbito fluvial. Esta Tesis Doctoral, afronta este reto global desde la doble componente hidrológica-geomática y su interdependencia, dado que la cuenca hidrológica se configura como el “medio” donde se pone de manifiesto el “comportamiento” de ésta frente al ciclo hidrológico. En primer lugar se propone una metodología hidrológica de análisis de las series temporales de aportaciones con el fin de profundizar en el conocimiento de las relaciones de dependencia que explique, más en profundidad, su comportamiento. Esto se ha llevado a cabo a través de un enfoque conjunto mediante técnicas tradicionales, basadas en modelos paramétricos (genéricamente expresados como ARIMA; modelos Autorregresivos Integrados de Medias Móviles) y el Razonamiento Causal mediante Inferencia Bayesiana, en consonancia con las nuevas tendencias de la investigación estocástica hidrológica. En segundo lugar, se ha evaluado la idoneidad, entre la hidráulica y la geomática, de nuevos modelos 3D, de coste reducido e incluso bajo coste, continuos, detallados y precisos, generados de modo eficiente (tiempo-coste), y aplicables a la Hidrodinámica y la Hidráulica Fluvial. Estos nuevos modelos 3D (basados en la Visión Artificial y fundamentados en la Geometría Epipolar y la Fotogrametría Digital) son obtenidos mediante la aplicación de técnicas SfM (Structure from Motion). Por otro lado, se proponen dos nuevos métodos para la evaluación de la incertidumbre altimétrica de los modelos digitales de elevación; el primero fundamentado en un enfoque metrológico mediante una evaluación Tipo A de la incertidumbre, y el segundo en los Estimadores Robustos de la centralidad y la dispersión de los errores. En definitiva, se pretende ofrecer un marco metodológico, para incrementar el conocimiento sobre el comportamiento hidrológico-hidráulico de la cuenca hidrográfica, encaminado a una gestión más objetiva, sostenible y segura de los recursos hídricos de una cuenca y de los riesgos naturales de origen fluvial. En esencia, se aspira a conocer para predecir y prevenir

Relative Pose Estimation Using Non-overlapping Multicamera Clusters

This thesis considers the Simultaneous Localization and Mapping (SLAM) problem using a set of perspective cameras arranged such that there is no overlap in their fields-of-view. With the known and fixed extrinsic calibration of each camera within the cluster, a novel real-time pose estimation system is presented that is able to accurately track the motion of a camera cluster relative to an unknown target object or environment and concurrently generate a model of the structure, using only image-space measurements. A new parameterization for point feature position using a spherical coordinate update is presented which isolates system parameters dependent on global scale, allowing the shape parameters of the system to converge despite the scale parameters remaining uncertain. Furthermore, a flexible initialization scheme is proposed which allows the optimization to converge accurately using only the measurements from the cameras at the first time step. An analysis is presented identifying the configurations of the cluster motions and target structure geometry for which the optimization solution becomes degenerate and the global scale is ambiguous. Results are presented that not only confirm the previously known critical motions for a two-camera cluster, but also provide a complete description of the degeneracies related to the point feature constellations. The proposed algorithms are implemented and verified in experiments with a camera cluster constructed using multiple perspective cameras mounted on a quadrotor vehicle and augmented with tracking markers to collect high-precision ground-truth motion measurements from an optical indoor positioning system. The accuracy and performance of the proposed pose estimation system are confirmed for various motion profiles in both indoor and challenging outdoor environments

Recovering Scale in Relative Pose and Target Model Estimation Using Monocular Vision

A combined relative pose and target object model estimation framework using a monocular camera as the primary feedback sensor has been designed and validated in a simulated robotic environment. The monocular camera is mounted on the end-effector of a robot manipulator and measures the image plane coordinates of a set of point features on a target workpiece object. Using this information, the relative position and orientation, as well as the geometry, of the target object are recovered recursively by a Kalman filter process. The Kalman filter facilitates the fusion of supplemental measurements from range sensors, with those gathered with the camera. This process allows the estimated system state to be accurate and recover the proper environment scale. Current approaches in the research areas of visual servoing control and mobile robotics are studied in the case where the target object feature point geometry is well-known prior to the beginning of the estimation. In this case, only the relative pose of target object frames is estimated over a sequence of frames from a single monocular camera. An observability analysis was carried out to identify the physical configurations of camera and target object for which the relative pose cannot be recovered by measuring only the camera image plane coordinates of the object point features. A popular extension to this is to concurrently estimate the target object model concurrently with the relative pose of the camera frame, a process known as Simultaneous Localization and Mapping (SLAM). The recursive framework was augmented to facilitate this larger estimation problem. The scale of the recovered solution is ambiguous using measurements from a single camera. A second observability analysis highlights more configurations for which the relative pose and target object model are unrecoverable from camera measurements alone. Instead, measurements which contain the global scale are required to obtain an accurate solution. A set of additional sensors are detailed, including range finders and additional cameras. Measurement models for each are given, which facilitate the fusion of this supplemental data with the original monocular camera image measurements. A complete framework is then derived to combine a set of such sensor measurements to recover an accurate relative pose and target object model estimate. This proposed framework is tested in a simulation environment with a virtual robot manipulator tracking a target object workpiece through a relative trajectory. All of the detailed estimation schemes are executed: the single monocular camera cases when the target object geometry are known and unknown, respectively; a two camera system in which the measurements are fused within the Kalman filter to recover the scale of the environment; a camera and point range sensor combination which provides a single range measurement at each system time step; and a laser pointer and camera hybrid which concurrently tries to measure the feature point images and a single range metric. The performance of the individual test cases are compared to determine which set of sensors is able to provide robust and reliable estimates for use in real world robotic applications. Finally, some conclusions on the performance of the estimators are drawn and directions for future work are suggested. The camera and range finder combination is shown to accurately recover the proper scale for the estimate and warrants further investigation. Further, early results from the multiple monocular camera setup show superior performance to the other sensor combinations and interesting possibilities are available for wide field-of-view super sensors with high frame rates, built from many inexpensive devices

A Survey of Motion-Parallax-Based 3-D Reconstruction Algorithms

The task of recovering three-dimensional (3-D) geometry from two-dimensional views of a scene is called 3-D reconstruction. It is an extremely active research area in computer vision. There is a large body of 3-D reconstruction algorithms available in the literature. These algorithms are often designed to provide different tradeoffs between speed, accuracy, and practicality. In addition, even the output of various algorithms can be quite different. For example, some algorithms only produce a sparse 3-D reconstruction while others are able to output a dense reconstruction. The selection of the appropriate 3-D reconstruction algorithm relies heavily on the intended application as well as the available resources. The goal of this paper is to review some of the commonly used motion-parallax-based 3-D reconstruction techniques and make clear the assumptions under which they are designed. To do so efficiently, we classify the reviewed reconstruction algorithms into two large categories depending on whether a prior calibratio