Tabla de Contenidos: “…; Compact Shooter's Gear Bag; A Heavier Load; Part I: Compact Camera, Pro Results; People Pictures; Animals; Landscapes and City Scenes; Snow and Nature; Pro Studio; Tell a Story with Pictures; The Answers; Part II: What Your Compact Camera Can and Can't Do; Image Quality; Digital Noise Difference…”

“…De ello se deja amplia constancia en el texto y se describen de forma clara los paradigmas de la urbe desparramada y de la urbe compacta. En general, este panorama no era tratado por juristas y hombres y mujeres de leyes, que preferían dejarlo en manos de otros especialistas. …”
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“…Ainsi cet ouvrage nous offre un large panorama de la production théâtrale dans l’Irlande contemporaine. …”

Tabla de Contenidos: “…-- End-to-End Framework in Support of Virtual Design-EngineeringManufacturing-Construction Space Exploration -- Accuracy Aspects when Transforming a Boundary Representation of Solids into a Tetrahedral Space Partition -- The influence of topology optimisation's design space-from shell to volume-on the generation of structural systems -- Qualifying spatial information for underground volumes -- An algorithmic BIM approach to advance concrete printing -- A methodological approach to generate robot control algorithms from BIMModels -- Multi-Level LoD Parametric Design Approach in AEC for Robot-Oriented Construction -- Design and implementation of an optimal sensor system as part of a Digital Twin for a rotary bending machine -- The Effects of Fracture Energy on the Interfacial Strength of Self-Healing Concrete -- A Performance Metric for the Evaluation of Thermal Anomaly Identification with Ill-Defined Ground Truth -- Areas of Interest -- Semantic description of component locations for damage assessment -- Deep Neural Networks for visual bridge inspections and defect visualisation in Civil Engineering -- Automated decision making in structural health monitoring using explainable artificial intelligence -- VOX2BIM -- A Fast Method for Automated Point Cloud Segmentation -- Automated Generation of Railway Track Geometric Digital Twins (RailGDT) from Airborne LiDAR Data -- Deriving Digital Twin Models of Existing Bridges from Point Cloud Data Using Parametric Models and Metaheuristic Algorithms -- Building a balanced and well-rounded dataset for railway asset detection -- Panorama-to-digital twin registration using semantic features -- AI-based thermal bridge detection of building rooftops on district scale using aerial images -- Image Captioning in Chinese for Construction Activity Scenes Understanding Using a Pre-trained Cross-modal Language Model -- Process Pattern-based Hybrid Simulation for Emission Estimation of the Construction Processes -- Automatic image analysis of mineral construction and demolition waste (CDW) using machine learning methods and deep learning -- Fast Crack Detection Using Convolutional Neural Network -- An Integrated Computational GIS Platform for UAV-based Remote Building Façade Inspection -- A Computer Vision Approach for Building Facade Component Segmentation on 3D Point Cloud Models Reconstructed by Aerial Images -- Feasibility Study of Urban Flood Mapping Using Traffic Signs for Route Optimization -- Developing indicators for measuring the effectiveness of visualizations applied in construction safety management using eye-tracking -- Real-time LiDAR for Monitoring Construction Worker Presence Near Hazards and in Work Areas in a Virtual Reality Environment…”

Tabla de Contenidos: “…8.4.3.1 Example of embedded SoC for multimodal localization -- 8.4.3.2 Smart phones -- 8.4.3.3 Smart glasses -- 8.4.3.4 Autonomous mobile robots -- 8.4.3.5 Unmanned aerial vehicles -- 8.4.3.6 Autonomous driving vehicles -- 8.4.4 Discussion -- 8.5 Application Domains -- 8.5.1 Scene Mapping -- 8.5.1.1 Aircraft inspection -- 8.5.1.2 SenseFly eBee classic -- 8.5.2 Pedestrian Localization -- 8.5.2.1 Indoor localization in large-scale buildings -- 8.5.2.2 Precise localization of mobile devices in unknown environments -- 8.5.3 Automotive Navigation -- 8.5.3.1 Autonomous driving -- 8.5.3.2 Smart factory -- 8.5.4 Mixed Reality -- 8.5.4.1 Virtual cane system for visually impaired individuals -- 8.5.4.2 Engineering, construction and maintenance -- 8.6 Conclusion -- References -- 9 Self-Supervised Learning from Web Data for Multimodal Retrieval -- 9.1 Introduction -- 9.1.1 Annotating Data: A Bottleneck for Training Deep Neural Networks -- 9.1.2 Alternatives to Annotated Data -- 9.1.3 Exploiting Multimodal Web Data -- 9.2 Related Work -- 9.2.1 Contributions -- 9.3 Multimodal Text-Image Embedding -- 9.4 Text Embeddings -- 9.5 Benchmarks -- 9.5.1 InstaCities1M -- 9.5.2 WebVision -- 9.5.3 MIRFlickr -- 9.6 Retrieval on InstaCities1M and WebVision Datasets -- 9.6.1 Experiment Setup -- 9.6.2 Results and Conclusions -- 9.6.3 Error Analysis -- 9.6.3.1 Visual features confusion -- 9.6.3.2 Errors from the dataset statistics -- 9.6.3.3 Words with different meanings or uses -- 9.7 Retrieval in the MIRFlickr Dataset -- 9.7.1 Experiment Setup -- 9.7.2 Results and Conclusions -- 9.8 Comparing the Image and Text Embeddings -- 9.8.1 Experiment Setup -- 9.8.2 Results and Conclusions -- 9.9 Visualizing CNN Activation Maps -- 9.10 Visualizing the Learned Semantic Space with t-SNE -- 9.10.1 Dimensionality Reduction with t-SNE -- 9.10.2 Visualizing Both Image and Text Embeddings…”

“…It showcases possible ways of getting a grip on themes like: landscape openness, cluttering of the rural landscape, high-rise buildings in relation to cityscape, historic landscapes and motorway panoramas. It offers clues for visual landscape assessment of spaces in cities, parks and rural areas. …”