There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications, and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book’s practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.

贝叶斯滤波与平滑 (Bayesian filtering and smoothing)

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https://li-chongyi.github.io/proj_benchmark.html .

An Underwater Image Enhancement Benchmark Dataset and Beyond

We introduce an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption. Our method is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. It builds on the blending of two images that are directly derived from a color-compensated and white-balanced version of the original degraded image. The two images to fusion, as well as their associated weight maps, are defined to promote the transfer of edges and color contrast to the output image. To avoid that the sharp weight map transitions create artifacts in the low frequency components of the reconstructed image, we also adapt a multiscale fusion strategy. Our extensive qualitative and quantitative evaluation reveals that our enhanced images and videos are characterized by better exposedness of the dark regions, improved global contrast, and edges sharpness. Our validation also proves that our algorithm is reasonably independent of the camera settings, and improves the accuracy of several image processing applications, such as image segmentation and keypoint matching.

/pdf/color-balance-and-fusion-for-underwater-image-enhancement-4cjuexzr3u.pdf

Color Balance and Fusion for Underwater Image Enhancement

This paper proposes a methodology to estimate the transmission in underwater environments which consists on an adaptation of the Dark Channel Prior (DCP), a statistical prior based on properties of images obtained in outdoor natural scenes. Our methodology, called Underwater DCP (UDCP), basically considers that the blue and green color channels are the underwater visual information source, which enables a significant improvement over existing methods based in DCP. This is shown through a comparative study with state of the art techniques, we present a detailed analysis of our technique which shows its applicability and limitations in images acquired from real and simulated scenes.

/pdf/transmission-estimation-in-underwater-single-images-1y9pv2qp2e.pdf

Transmission Estimation in Underwater Single Images

In underwater environments, the scattering and absorption phenomena affect the propagation of light, degrading the quality of captured images. In this work, the authors present a method based on a physical model of light propagation that takes into account the most significant effects to image degradation: absorption, scattering, and backscattering. The proposed method uses statistical priors to restore the visual quality of the images acquired in typical underwater scenarios.

Underwater Depth Estimation and Image Restoration Based on Single Images

The complete modeling and simulation of an unmanned vehicle with combined aerial and underwater ca- pabilities, called Hybrid Unmanned Aerial Underwater Vehicle (HUAUV), is presented in this paper. The best architecture for this kind of vehicle was evaluated based on the adaptation of typical platforms for aerial and underwater vehicles, to allow the navigation in both environments. The model selected was based on a quadrotor-like aerial platform, adapted to dive and move underwater. Kinematic and dynamic models are presented here, and the parameters for a small dimension prototype was estimated and simulated. Finally, controllers were used and validated in realistic simulation, including air and water navigation, and the environment transition problem. To the best of our knowledge, it is the first vehicle that is able to navigate in both environment without mechanical adaptation during the medium transitions. I. INTRODUCTION Nowadays, unmanned autonomous vehicles have been the focus of many development efforts, with a large range of applications. The amount of resources applied has improved their capabilities, especially in the military field. Remotely operated or autonomous Unmanned Aerial Vehicles (UAVs), for example, were used in recent military operations around the world (1). But they were also used in non-military activities, like agriculture (2) and surveillance (3). Another important robotic platform are the Unmanned Underwater Vehicles (UUVs), whose the most known are the Remotely Operated Vehicles (ROVs). This kind of vehicles can also be applied in several commercial field operations (4), e.g. oil and gas extraction in ultra deep waters (5). Both kind of vehicles are well adapted to work in their own environment (air and water, respectively), but some situations may require a single vehicle capable of working in both environment. Such requirement commonly appears when is necessary to perform maintenance on partially or fully submersing structures, as ship hull or risers. A typical approach includes using auxiliary vessels to transport ROVs that will make the inspection of offshore target regions. This problem is harder in partially submersed structures. In such situations, where the usage of auxiliary ships is difficult and expensive, underwater robots equipped with wheels or tracks are recommended.

Hybrid Unmanned Aerial Underwater Vehicle: Modeling and simulation

The use of 3D laser range nder (LRF) and cameras is increasingly common in the navigation application for mobile robots. This paper proposes a new method to perform the extrinsic calibration between a pinhole camera and a 3D-LRF with the aid of an Inertial Measurement Unit (IMU). While state of the art calibration procedures require a large number of points for robust calibration, the proposed approach is innovate in terms of higher exibility and wider range of application. The proposed method is based on the presentation to the sensor platform of a stationary planar board containing a checkerboard pattern. The homography which de nes the rotation matrix between the LRF and camera is achieved moving the robot and observing the resulting motion of the sensors. This step attempts to solve the well-known homogeneous transform equation of the form AX = BX. Next, translation is calculated using a commonly least-squares estimation algorithm according to the corners of the pattern, previously detected by both, camera and LRF. The developed algorithm was tested on a mobile robot which is equipped with sensor platform composed of pinhole camera, LRF pan-tilt unit and a IMU, and proved to be very accurate. Furthermore, the applicability of the method is greatly expanded, requiring attachment of a simple inertial sensor to the sensor platform. Index Terms 3D Laser Sensors, Laser-Camera Calibration

https://ap.isr.uc.pt/archive/ECMR09%20-%20PMNT.pdf

Data Fusion Calibration for a 3D Laser Range Finder and a Camera using Inertial Data.

The use of Autonomous Underwater Vehicles (AUVs) for underwater tasks is a promising robotic field. These robots can carry visual inspection cameras. Besides serving the activities of inspection and mapping, the captured images can also be used to aid navigation and localization of the robots. Visual odometry is the process of determining the position and orientation of a robot by analyzing the associated camera images. It has been used in a wide variety of non-standard locomotion robotic methods. In this context, this paper proposes an approach to visual odometry and mapping of underwater vehicles. Supposing the use of inspection cameras, this proposal is composed of two stages: i) the use of computer vision for visual odometry, extracting landmarks in underwater image sequences and ii) the development of topological maps for localization and navigation. The integration of such systems will allow visual odometry, localization and mapping of the environment. A set of tests with real robots was accomplished, regarding online and performance issues. The results reveals an accuracy and robust approach to several underwater conditions, as illumination and noise, leading to a promissory and original visual odometry and mapping technique.

/pdf/visual-odometry-and-mapping-for-underwater-autonomous-2is5hs4bh6.pdf

Paulo Drews

Papers

Transmission Estimation in Underwater Single Images

Underwater Depth Estimation and Image Restoration Based on Single Images

Hybrid Unmanned Aerial Underwater Vehicle: Modeling and simulation

Data Fusion Calibration for a 3D Laser Range Finder and a Camera using Inertial Data.

Visual odometry and mapping for Underwater Autonomous Vehicles