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Automated Low-Altitude Air Delivery
This book investigates Unmanned Aircraft Systems (UAS) with a payload capacity of one metric ton for transportation. The authors provide a large variety of perspectives–from economics to technical realization. With the focus on such heavy-lift cargo UAS, the authors consider recently established methods for approval and certification, which they expect to be disruptive for unmanned aviation. In particular, the Specific Operations Risk Assessment (SORA) and its impact on the presented technological solutions and operational concepts are studied. Starting with the assumption of an operation over sparsely populated areas and below common air traffic, diverse measures to further reduce operational risks are proposed. Operational concepts derived from logistics use-cases set the context for an in-depth analysis including aircraft and system design, safe autonomy as well as airspace integration and datalinks. Results from simulations and technology demonstrations are presented as a proof of concept for solutions proposed in this book.
Image Analysis and Recognition
This book constitutes the thoroughly refereed proceedings of the 7th International Conference, ICIAR 2010, held in Póvoa de Varzin, Portugal in June 2010. The 88 revised full papers were selected from 164 submissions. The papers are organized in topical sections on Image Morphology, Enhancement and Restoration, Image Segmentation, Featue Extraction and Pattern Recognition, Computer Vision, Shape, Texture and Motion Analysis, Coding, Indexing, and Retrieval, Face Detection and Recognition, Biomedical Image Analysis, Biometrics and Applications.
Manifold Learning Theory and Applications
Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-dimensional observations. Manifold learning, a groundbreaking technique designed to tackle these issues of dimensionality reduction, finds widespread
Image Analysis and Recognition
This book constitutes the thoroughly refereed proceedings of the 7th International Conference, ICIAR 2010, held in Póvoa de Varzin, Portugal in June 2010. The 88 revised full papers were selected from 164 submissions. The papers are organized in topical sections on Image Morphology, Enhancement and Restoration, Image Segmentation, Featue Extraction and Pattern Recognition, Computer Vision, Shape, Texture and Motion Analysis, Coding, Indexing, and Retrieval, Face Detection and Recognition, Biomedical Image Analysis, Biometrics and Applications.
ECAI 2020
This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of ...
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
This title is part of a two-volume set that constitute the refereed proceedings of the 10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007. Coverage in this first volume includes diffusion tensor imaging and computing, cardiac imaging and robotics, image segmentation and classification, image guided intervention and robotics, innovative clinical and biological applications, brain atlas computing, and simulation of therapy.
Sensor Data Understanding
The rapid development in the area of sensor technology has been responsible for a number of societal phenomena like UGC (User Generated Content) or QS (Quantified Self). Machine learning algorithms benefit a lot from the availability of such huge volumes of digital data. For example, new technical solutions for challenges caused by the demographic change (ageing society) can be proposed in this way, especially in the context of healthcare systems in industrialised countries. The goal of this book is to present selected algorithms for Visual Scene Analysis (VSA, processing UGC) as well as for Human Data Interpretation (HDI, using data produced within the QS movement) and to expose a joint methodological basis between these two scientific directions. While VSA approaches have reached impressive robustness towards human-like interpretation of visual sensor data, HDI methods are still of limited semantic abstraction power. Using selected state-of-the-art examples, this book shows the maturity of approaches towards closing the semantic gap in both areas, VSA and HDI.
