Detection of image structures using the Fisher information and the Rao metric

In many detection problems, the structures to be detected are parameterized by the points of a parameter space. If the conditional probability density function for the measurements is known, then detection can be achieved by sampling the parameter space at a finite number of points and checking each point to see if the corresponding structure is supported by the data. The number of samples and the distances between neighboring samples are calculated using the Rao metric on the parameter space. The Rao metric is obtained from the Fisher information which is, in turn, obtained from the conditional probability density function. An upper bound is obtained for the probability of a false detection. The calculations are simplified in the low noise case by making an asymptotic approximation to the Fisher information. An application to line detection is described. Expressions are obtained for the asymptotic approximation to the Fisher information, the volume of the parameter space, and the number of samples. The time complexity for line detection is estimated. An experimental comparison is made with a Hough transform-based method for detecting lines

Visual SLAM for Measurement and Augmented Reality in Laparoscopic Surgery

In spite of the great advances in laparoscopic surgery, this type of surgery still shows some difficulties during its realization, mainly caused by its complex maneuvers and, above all, by the loss of the depth perception. Unlike classical open surgery --laparotomy-- where surgeons have direct contact with organs and a complete 3D perception, laparoscopy is carried out by means of specialized instruments, and a monocular camera (laparoscope) in which the 3D scene is projected into a 2D plane --image. The main goal of this thesis is to face with this loss of depth perception by making use of Simultaneous Localization and Mapping (SLAM) algorithms developed in the fields of robotics and computer vision during the last years. These algorithms allow to localize, in real time (25 $\thicksim$ 30 frames per second), a camera that moves freely inside an unknown rigid environment while, at the same time, they build a map of this environment by exploiting images gathered by that camera. These algorithms have been extensively validated both in man-made environments (buildings, rooms, ...) and in outdoor environments, showing robustness to occlusions, sudden camera motions, or clutter. This thesis tries to extend the use of these algorithms to laparoscopic surgery. Due to the intrinsic nature of internal body images (they suffer from deformations, specularities, variable illumination conditions, limited movements, ...), applying this type of algorithms to laparoscopy supposes a real challenge. Knowing the camera (laparoscope) location with respect to the scene (abdominal cavity) and the 3D map of that scene opens new interesting possibilities inside the surgical field. This knowledge enables to do augmented reality annotations directly on the laparoscopic images (e.g. alignment of preoperative 3D CT models); intracavity 3D distance measurements; or photorealistic 3D reconstructions of the abdominal cavity recovering synthetically the lost depth. These new facilities provide security and rapidity to surgical procedures without disturbing the classical procedure workflow. Hence, these tools are available inside the surgeon's armory, being the surgeon who decides to use them or not. Additionally, knowledge of the camera location with respect to the patient's abdominal cavity is fundamental for future development of robots that can operate automatically since, knowing this location, the robot will be able to localize other tools controlled by itself with respect to the patient. In detail, the contributions of this thesis are: - To demonstrate the feasibility of applying SLAM algorithms to laparoscopy showing experimentally that using robust data association is a must. - To robustify one of these algorithms, in particular the monocular EKF-SLAM algorithm, by adapting a relocalization system and improving data association with a robust matching algorithm. - To develop of a robust matching method (1-Point RANSAC algorithm). - To develop a new surgical procedure to ease the use of visual SLAM in laparoscopy. - To make an extensive validation of the robust EKF-SLAM (EKF + relocalization + 1-Point RANSAC) obtaining millimetric errors and working in real time both on simulation and real human surgeries. The selected surgery has been the ventral hernia repair. - To demonstrate the potential of these algorithms in laparoscopy: they recover synthetically the depth of the operative field which is lost by using monocular laparoscopes, enable the insertion of augmented reality annotations, and allow to perform distance measurements using only a laparoscopic tool (to define the real scale) and laparoscopic images. - To make a clinical validation showing that these algorithms allow to shorten surgical times of operations and provide more security to the surgical procedures

Geometric uncertainty models for correspondence problems in digital image processing

Many recent advances in technology rely heavily on the correct interpretation of an enormous amount of visual information. All available sources of visual data (e.g. cameras in surveillance networks, smartphones, game consoles) must be adequately processed to retrieve the most interesting user information. Therefore, computer vision and image processing techniques gain significant interest at the moment, and will do so in the near future. Most commonly applied image processing algorithms require a reliable solution for correspondence problems. The solution involves, first, the localization of corresponding points -visualizing the same 3D point in the observed scene- in the different images of distinct sources, and second, the computation of consistent geometric transformations relating correspondences on scene objects. This PhD presents a theoretical framework for solving correspondence problems with geometric features (such as points and straight lines) representing rigid objects in image sequences of complex scenes with static and dynamic cameras. The research focuses on localization uncertainty due to errors in feature detection and measurement, and its effect on each step in the solution of a correspondence problem. Whereas most other recent methods apply statistical-based models for spatial localization uncertainty, this work considers a novel geometric approach. Localization uncertainty is modeled as a convex polygonal region in the image space. This model can be efficiently propagated throughout the correspondence finding procedure. It allows for an easy extension toward transformation uncertainty models, and to infer confidence measures to verify the reliability of the outcome in the correspondence framework. Our procedure aims at finding reliable consistent transformations in sets of few and ill-localized features, possibly containing a large fraction of false candidate correspondences. The evaluation of the proposed procedure in practical correspondence problems shows that correct consistent correspondence sets are returned in over 95% of the experiments for small sets of 10-40 features contaminated with up to 400% of false positives and 40% of false negatives. The presented techniques prove to be beneficial in typical image processing applications, such as image registration and rigid object tracking

Development of a Visual Odometry System as a Location Aid for Self-Driving Cars

Conocer la posición exacta que ocupa un robot y la trayectoria que describe es esencial en el ámbito de la automoción. Durante años se han desarrollado distintos sensores y técnicas para este cometido que se estudian a lo largo del trabajo. En este proyecto se utilizan dos cámaras a bordo del vehículo como sensores de percepción del entorno. Se propone un algoritmo basado únicamente en odometría visual, es decir, analizando la secuencia de imágenes captadas por las cámaras, sin conocimiento previo del entorno y sin el uso de otros sensores, se pretende obtener una estimación real de la posición y orientación del vehículo. Dicha propuesta se ha validado en el dataset de KITTI y se ha comparado con otras técnicas de odometría visual existentes en el estado del arteKnowing the exact position occupied by a robot and the trajectory it describes is essential in the automotive field. Some techniques and sensors have been developed over the years for this purpose which are studied in this work. In this project, two cameras on board the vehicle are used as sensors for the environment perception. The proposed algorithm is based only on visual odometry, it means, using the sequence of images captured by the cameras, without prior knowledge of the environment and without the use of other sensors. The aim is to obtain a real estimation of the position and orientation of the vehicle. This proposal has been validated on the KITTI benchmark and compared with other Visual Odometry techniques existing in the state of the art.Grado en Ingeniería en Electrónica y Automática Industria