Reconstruction of tree branching structures from UAV-LiDAR data

The reconstruction of tree branching structures is a longstanding problem in Computer Graphics which has been studied over several data sources, from photogrammetry point clouds to Terrestrial and Aerial Laser Imaging Detection and Ranging technology. However, most data sources present acquisition errors that make the reconstruction more challenging. Among them, the main challenge is the partial or complete occlusion of branch segments, thus leading to disconnected components whether the reconstruction is resolved using graph-based approaches. In this work, we propose a hybrid method based on radius-based search and Minimum Spanning Tree for the tree branching reconstruction by handling occlusion and disconnected branches. Furthermore, we simplify previous work evaluating the similarity between ground-truth and reconstructed skeletons. Using this approach, our method is proved to be more effective than the baseline methods, regarding reconstruction results and response time. Our method yields better results on the complete explored radii interval, though the improvement is especially significant on the Ground Sampling Distance In terms of latency, an outstanding performance is achieved in comparison with the baseline method.Junta de Andalucia 1381202-GEU PYC20-RE-005-UJAEuropean Commission Spanish Government PID2021-126339OB-I00 FPU17/01902 FPU19/0010

CleanPage: Fast and Clean Document and Whiteboard Capture

The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for the high-quality digitization of content from pages and whiteboards to sharable online material. However, capturing this information is not always easy nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop, and clean an image of a page or whiteboard. Unlike equivalent systems, no user intervention is required during processing, and the result is a high-contrast, low-noise image with a clean homogenous background. Results are presented for a selection of scenarios showing the versatility of the design. CleanPage is compared with two market leader scanning apps using two testing approaches: real paper scans and ground-truth comparisons. These comparisons are achieved by a new testing methodology that allows scans to be compared to unscanned counterparts by using synthesized images. Real paper scans are tested using image quality measures. An evaluation of standard image quality assessments is included in this work, and a novel quality measure for scanned images is proposed and validated. The user experience for each scanning app is assessed, showing CleanPage to be fast and easier to use

Going batty: the challenges and opportunities of using drones to monitor the behaviour and habitat use of rays

The way an animal behaves in its habitat provides insight into its ecological role. As such, collecting robust, accurate datasets in a time-efficient manner is an ever-present pressure for the field of behavioural ecology. Faced with the shortcomings and physical limitations of traditional ground-based data collection techniques, particularly in marine studies, drones offer a low-cost and efficient approach for collecting data in a range of coastal environments. Despite drones being widely used to monitor a range of marine animals, they currently remain underutilised in ray research. The innovative application of drones in environmental and ecological studies has presented novel opportunities in animal observation and habitat assessment, although this emerging field faces substantial challenges. As we consider the possibility to monitor rays using drones, we face challenges related to local aviation regulations, the weather and environment, as well as sensor and platform limitations. Promising solutions continue to be developed, however, growing the potential for drone-based monitoring of behaviour and habitat use of rays. While the barriers to enter this field may appear daunting for researchers with little experience with drones, the technology is becoming increasingly accessible, helping ray researchers obtain a wide range of highly useful data

CleanPage: Fast and Clean Document and Whiteboard Capture

The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for the high-quality digitization of content from pages and whiteboards to sharable online material. However, capturing this information is not always easy nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop, and clean an image of a page or whiteboard. Unlike equivalent systems, no user intervention is required during processing, and the result is a high-contrast, low-noise image with a clean homogenous background. Results are presented for a selection of scenarios showing the versatility of the design. CleanPage is compared with two market leader scanning apps using two testing approaches: real paper scans and ground-truth comparisons. These comparisons are achieved by a new testing methodology that allows scans to be compared to unscanned counterparts by using synthesized images. Real paper scans are tested using image quality measures. An evaluation of standard image quality assessments is included in this work, and a novel quality measure for scanned images is proposed and validated. The user experience for each scanning app is assessed, showing CleanPage to be fast and easier to use

Hyperspectral Image Analysis of Food for Nutritional Intake

The primary object of this dissertation is to investigate the application of hyperspectral technology to accommodate for the growing demand in the automatic dietary assessment applications. Food intake is one of the main factors that contribute to human health. In other words, it is necessary to get information about the amount of nutrition and vitamins that a human body requires through a daily diet. Manual dietary assessments are time-consuming and are also not precise enough, especially when the information is used for the care and treatment of hospitalized patients. Moreover, the data must be analyzed by nutritional experts. Therefore, researchers have developed various semiautomatic or automatic dietary assessment systems; most of them are based on the conventional color images such as RGB. The main disadvantage of such systems is their inability to differentiate foods of similar color or same ingredients in various colors, or different forms such as cooked or mixed forms. Although adding features such as shape, size and texture improve the overall performance, they are sensitive to changes in the illumination, rotation, scale, etc. A balance between quality and quantity of features representation, and system efficiency must also be considered. Hyperspectral technology combines conventional imaging technology with spectroscopy in a three-dimensional data-cube to obtain both the spatial and spectral information of the objects. However, the high dimensionality of hyperspectral data in addition to the redundancy between spectral bands limits performance, especially in online or onboard data processing applications. Thus, various features selection/extraction are also used to select the optimal feature subsets. The results are promising and verify the feasibility of using hyperspectral technology in dietary assessment applications

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs