Automated detection in benthic images for megafauna classification and marine resource exploration: supervised and unsupervised methods for classification and regression tasks in benthic images with efficient integration of expert knowledge

Schoening T. Automated detection in benthic images for megafauna classification and marine resource exploration: supervised and unsupervised methods for classification and regression tasks in benthic images with efficient integration of expert knowledge. Bielefeld: Universitätsbibliothek Bielefeld; 2015.Image acquisition of deep sea floors allows to cast a glance on an extraordinary environment. Exploring the rarely known geology and biology of the deep sea regularly questions the scientific understanding of occurring conditions, processes and changes. Increasing sampling efforts, by both more frequent image acquisition as well as widespread monitoring of large areas, currently refine the scientific models about this environment. Accompanied by the sampling efforts, novel challenges emerge for the image based marine research. These include growing data volume, growing data variety and increased velocity at which data is acquired. Apart from the included technical challenges, the fundamental problem is to add semantics to the acquired data to extract further meaning and gain derived knowledge. Manual analysis of the data in terms of manually annotating images (e.g. annotating occurring species to gain species interaction knowledge) is an intricate task and has become infeasible due to the huge data volumes. The combination of data and interpretation challenges calls for automated approaches based on pattern recognition and especially computer vision methods. These methods have been applied in other fields to add meaning to visual data but have rarely been applied to the peculiar case of marine imaging. First of all, the physical factors of the environment constitute a unique computer vision challenge and require special attention in adapting the methods. Second, the impossibility to create a reliable reference gold standard from multiple field expert annotations challenges the development and evaluation of automated, pattern recognition based approaches. In this thesis, novel automated methods to add semantics to benthic images are presented that are based on common pattern recognition techniques. Three major benthic computer vision scenarios are addressed: the detection of laser points for scale quantification, the detection and classification of benthic megafauna for habitat composition assessments and the detection and quantity estimation of benthic mineral resources for deep sea mining. All approaches to address these scenarios are fitted to the peculiarities of the marine environment. The primary paradigm, that guided the development of all methods, was to design systems that can be operated by field experts without knowledge about the applied pattern recognition methods. Therefore, the systems have to be generally applicable to arbitrary image based detection scenarios. This in turn makes them applicable in other computer vision fields outside the marine environment as well. By tuning system parameters automatically from field expert annotations and applying methods that cope with errors in those annotations, the limitations of inaccurate gold standards can be bypassed. This allows to use the developed systems to further refine the scientific models based on automated image analysis

Design Issues for Hexapod Walking Robots

Hexapod walking robots have attracted considerable attention for several decades. Many studies have been carried out in research centers, universities and industries. However, only in the recent past have efficient walking machines been conceived, designed and built with performances that can be suitable for practical applications. This paper gives an overview of the state of the art on hexapod walking robots by referring both to the early design solutions and the most recent achievements. Careful attention is given to the main design issues and constraints that influence the technical feasibility and operation performance. A design procedure is outlined in order to systematically design a hexapod walking robot. In particular, the proposed design procedure takes into account the main features, such as mechanical structure and leg configuration, actuating and driving systems, payload, motion conditions, and walking gait. A case study is described in order to show the effectiveness and feasibility of the proposed design procedure

Waves and Coasts in the Pacific - Cost Analysis of Wave Energy in the Pacific

Ocean waves are often cited as an appealing source of renewable energy in the Pacific but the cost effectiveness of wave energy converters (WECs) is deemed unproven and the technology is rarely considered as a reliable renewable energy resource in Pacific Island countries. However, single/stand-alone WECs could be a competitive option against fossil fuel generators because of the high cost of imported fuel. This study analyses the wave energy resource in the Pacific and calculates the potential cost and power generation of a benchmark WEC in Pacific Island countries. The type of WEC chosen depends largely on the environmental and geophysical characteristics of the wave energy site where it is to be deployed. The aim of this study was not to report on the best device for each site but rather to give advice about the islands that could benefit most from wave energy. Therefore, the cost analysis is based on a single WEC – the Pelamis device. The Pelamis device cost presented here serves as a benchmark for comparison with other WECs in different locations. Due to uncertainties and variations in potential costs across the region, the study evaluated the range of costs applicable to the whole region. The costs of the WEC, transport, installation, operation and management, refit and decommissioning are included. Site-specific potential power generation was calculated, based on a realistic power output dependent on the wave conditions. The study found that Pacific islands south of latitude 20oS receive a substantial amount of wave energy with a mean available wave resource above 20 kilowatts per metre (kW/m) and that many other islands also have potential for wave energy extraction with a mean wave resource above 7 kW/m. This study found that a Pelamis device in the Pacific could cost between USD 6,318,000 and USD 14,104,000 to install and can operate for 25 years. The energy produced by such a device could be up to 1200 megawatt hours (MWh) per year for sites exposed to large swells. Using these values, the range of the total lifetime cost of power generation was calculated to be between USD 200/MWh for exposed sites and USD 1800/MWh for more sheltered sites. The corresponding operation and maintenance generation cost are between USD 40/MWh and USD 900/MWh. These costs are on a par with the cost of generation of other renewable energies, such as wind and solar, and, for exposed sites, on a par with the cost of diesel generation. These findings suggest that wave energy is a genuine contender for the development of renewable energy in the Pacific and should no longer be ignored when planning such development; a concerted effort from all stakeholders should be made in order to benefit from this technology. Further deployment in wave technology will reduce the cost of single wave energy devices, and most small Pacific Islands would not need to deploy large-scale wave farms of ten or more devices, as power production would greatly exceed the demand. With expected rises in fuel prices in the next decades, it would be wise to investigate further the potential of wave energy technology. The deployment of WECs in the Pacific could provide an opportunity for the technology to prove itself in the region and attract the attention of investors, policy makers and decision makers to invest in wave energy development in the Pacific . Page | 2 Waves and Coasts in the Pacific Other recommendations are listed below. 1. French Polynesia, the Austral Islands in particular, should investigate potential wave energy sites. On these islands, wave energy generation could become a major renewable energy resource with a relatively low cost that could even compete with fossil fuel. 2. Tonga, Cook Islands and New Caledonia should also investigate wave energy sites and suitable wave energy devices. Wave energy has a great potential for helping these countries reach their renewable energy targets and supply energy more cheaply than other renewable energy resources. 3. Countries with a mean wave energy flux above 7 kW/m should also investigate wave energy hotspots and wave energy device options, especially in exposed locations. There, wave energy may be able to supply a significant amount of renewable energy and help these countries meet their renewable energy targets. However, wave energy in these locations may be more expensive than other types of renewable energy. 4. Countries with a mean wave energy flux of less than 7 kW/m, such as Papua New Guinea and Solomon islands, are unlikely to benefit from wave energy unless a major technological breakthrough makes wave energy devices much more efficient. These countries should therefore not consider wave energy as a significant renewable energy resource. The WACOP project has provided calculations similar to those presented in this study for more than 200 Pacific locations in wave climate reports that should be consulted as an initial assessment of the wave energy resource available.1 The WACOP project also provides a detailed wave climate analysis for Samoa, Rarotonga in Cook Islands, Tongatapu and 'Eua in Tonga, southern Viti Levu in Fiji, Efate in Vanuatu, and Funafuti in Tuvalu. These analyses include wave energy and cost calculations based on the calculations presented in this report

3D Classification of Power Line Scene Using Airborne Lidar Data

Failure to adequately maintain vegetation within a power line corridor has been identified as a main cause of the August 14, 2003 electric power blackout. Such that, timely and accurate corridor mapping and monitoring are indispensible to mitigate such disaster. Moreover, airborne LiDAR (Light Detection And Ranging) has been recently introduced and widely utilized in industries and academies thanks to its potential to automate the data processing for scene analysis including power line corridor mapping. However, today’s corridor mapping practice using LiDAR in industries still remains an expensive manual process that is not suitable for the large-scale, rapid commercial compilation of corridor maps. Additionally, in academies only few studies have developed algorithms capable of recognizing corridor objects in the power line scene, which are mostly based on 2-dimensional classification. Thus, the objective of this dissertation is to develop a 3-dimensional classification system which is able to automatically identify key objects in the power line corridor from large-scale LiDAR data. This dissertation introduces new features for power structures, especially for the electric pylon, and existing features which are derived through diverse piecewise (i.e., point, line and plane) feature extraction, and then constructs a classification model pool by building individual models according to the piecewise feature sets and diverse voltage training samples using Random Forests. Finally, this dissertation proposes a Multiple Classifier System (MCS) which provides an optimal committee of models from the model pool for classification of new incoming power line scene. The proposed MCS has been tested on a power line corridor where medium voltage transmission lines (115 kV and 230 kV) pass. The classification results based on the MCS applied by optimally selecting the pre-built classification models according to the voltage type of the test corridor demonstrate a good accuracy (89.07%) and computationally effective time cost (approximately 4 hours/km) without additional training fees

Compact-morphology-based poly-metallic nodule delineation

Poly-metallic nodules are a marine resource considered for deep sea mining. Assessing nodule abundance is of interest for mining companies and to monitor potential environmental impact. Optical seafloor imaging allows quantifying poly-metallic nodule abundance at spatial scales from centimetres to square kilometres. Towed cameras and diving robots acquire high-resolution imagery that allow detecting individual nodules and measure their sizes. Spatial abundance statistics can be computed from these size measurements, providing e.g. seafloor coverage in percent and the nodule size distribution. Detecting nodules requires segmentation of nodule pixels from pixels showing sediment background. Semi-supervised pattern recognition has been proposed to automate this task. Existing nodule segmentation algorithms employ machine learning that trains a classifier to segment the nodules in a high-dimensional feature space. Here, a rapid nodule segmentation algorithm is presented. It omits computation-intense feature-based classification and employs image processing only. It exploits a nodule compactness heuristic to delineate individual nodules. Complex machine learning methods are avoided to keep the algorithm simple and fast. The algorithm has successfully been applied to different image datasets. These data sets were acquired by different cameras, camera platforms and in varying illumination conditions. Their successful analysis shows the broad applicability of the proposed method

SIMULATING HIERARCHICAL STRUCTURE OF HUMAN VISUAL CORTEX FOR IMAGE CLASSIFICATION

Ph.DDOCTOR OF PHILOSOPH