Download or read book Scene Segmentation and Reasoning Under Uncertainty written by and published by . This book was released on 1991 with total page 4 pages. Available in PDF, EPUB and Kindle. Book excerpt: Segmentation of range images has long been considered in computer vision as an important but extremely difficult problem. A new paradigm for the segmentation of range images into piecewise continuous patches is presented. Data aggregation is performed via model recovery in terms of variable-order bi- variate polynomials using iterative regression. All the recovered models are potential candidates for the final description of the data. Selection of the models is achieved through a maximization of quadratic Boolean problem. The procedure can be adapted to prefer certain kinds of descriptions (one which describes more data points, or has smaller error, or has lower order model). They have developed a fast optimization procedure for model selection. The major novelty of the approach is in combining model extraction and model selection in a dynamic way. Partial recovery of the models is followed by the optimization (selection) procedure where only the best models are allowed to develop further. The results obtained in this way are comparable with the results obtained when using the selection module only after all the models are fully recovered, while the computational complexity is significantly reduced. The procedure was tested on several real range images.

Download or read book Uncertainty Reasoning for the Semantic Web I written by Paulo C. G. Costa and published by Springer Science & Business Media. This book was released on 2008-12-02 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed first three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2005, 2006, and 2007. The 22 papers presented are revised and strongly extended versions of selected workshops papers as well as invited contributions from leading experts in the field and closely related areas. The present volume represents the first comprehensive compilation of state-of-the-art research approaches to uncertainty reasoning in the context of the semantic Web, capturing different models of uncertainty and approaches to deductive as well as inductive reasoning with uncertain formal knowledge.

Download or read book Towards Scene Understanding written by Roozbeh Mottaghi and published by . This book was released on 2013 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scene understanding is one of the holy grails of computer vision. Despite decades of research on scene understanding, it is still considered an unsolved problem. The difficulty arises mainly because of the huge space of possible images. We require models to capture this variability of scenes and their constituents (e.g., objects) given the limited memory resources. Additionally, we require efficient learning and inference techniques for our models to find the optimal solution in the enormous space of possible solutions. In this thesis, we propose a set of novel techniques for object detection, segmentation, and contextual reasoning and take a further step towards the ultimate goal of holistic scene understanding. In particular, we propose a compositional method for representing objects and show inference can be performed for an exponential number of objects in linear time. Subsequently, we propose a series of discriminative learning methods for object detection and segmentation and show that our methods achieve the state-of-the-art performance on difficult benchmarks in the computer vision community. Finally, through a series of hybrid human-machine experiments, we try to identify bottlenecks in scene understanding to better guide future research efforts in this area.

Download or read book Spatial Reasoning and Multi Sensor Fusion written by Avinash C. Kak and published by Morgan Kaufmann. This book was released on 1987 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial Reasoning and Multi-Sensor Fusion

Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2021-08-16 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

Download or read book Epistemic Uncertainty in Artificial Intelligence written by Fabio Cuzzolin and published by Springer Nature. This book was released on with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Information Processing and Management of Uncertainty in Knowledge Based Systems written by Eyke Hüllermeier and published by Springer. This book was released on 2010-06-30 with total page 783 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 13th conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, held in Dortmund, Germany, in June 2010.

Download or read book Integrated Uncertainty in Knowledge Modelling and Decision Making written by Zengchang Qin and published by Springer. This book was released on 2013-06-20 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the International Symposium on Integrated Uncertainty in Knowledge Modeling and Decision Making, IUKM 2013, held in Beijing China, in July 2013. The 19 revised full papers were carefully reviewed and selected from 49 submissions and are presented together with keynote and invited talks. The papers provide a wealth of new ideas and report both theoretical and applied research on integrated uncertainty modeling and management.

Download or read book Frontiers in Computational Intelligence written by Sanaz Mostaghim and published by Springer. This book was released on 2017-10-05 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of several contributions which show the state of the art in specific areas of Computational Intelligence. This carefully edited book honors the 65th birthday of Rudolf Kruse. The main focus of these contributions lies on treating vague data as well as uncertain and imprecise information with automated procedures, which use techniques from statistics, control theory, clustering, neural networks etc. to extract useful and employable knowledge.

Download or read book Intelligent Robots and Computer Vision written by and published by . This book was released on 1992 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficient Multi level Scene Understanding in Videos written by Buyu Liu and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic video parsing is a key step towards human-level dynamic scene understanding, and a fundamental problem in computer vision. A core issue in video understanding is to infer multiple scene properties of a video in an efficient and consistent manner. This thesis addresses the problem of holistic scene understanding from monocular videos, which jointly reason about semantic and geometric scene properties from multiple levels, including pixelwise annotation of video frames, object instance segmentation in spatio-temporal domain, and/or scene-level description in terms of scene categories and layouts. We focus on four main issues in the holistic video understanding: 1) what is the representation for consistent semantic and geometric parsing of videos? 2) how do we integrate high-level reasoning (e.g., objects) with pixel-wise video parsing? 3) how can we do efficient inference for multi-level video understanding? and 4) what is the representation learning strategy for efficient/cost-aware scene parsing? We discuss three multi-level video scene segmentation scenarios based on different aspects of scene properties and efficiency requirements. The first case addresses the problem of consistent geometric and semantic video segmentation for outdoor scenes. We propose a geometric scene layout representation, or a stage scene model, to efficiently capture the dependency between the semantic and geometric labels. We build a unified conditional random field for joint modeling of the semantic class, geometric label and the stage representation, and design an alternating inference algorithm to minimize the resulting energy function. The second case focuses on the problem of simultaneous pixel-level and object-level segmentation in videos. We propose to incorporate foreground object information into pixel labeling by jointly reasoning semantic labels of supervoxels, object instance tracks and geometric relations between objects. In order to model objects, we take an exemplar approach based on a small set of object annotations to generate a set of object proposals. We then design a conditional random field framework that jointly models the supervoxel labels and object instance segments. To scale up our method, we develop an active inference strategy to improve the efficiency of multi-level video parsing, which adaptively selects an informative subset of object proposals and performs inference on the resulting compact model. The last case explores the problem of learning a flexible representation for efficient scene labeling. We propose a dynamic hierarchical model that allows us to achieve flexible trade-offs between efficiency and accuracy. Our approach incorporates the cost of feature computation and model inference, and optimizes the model performance for any given test-time budget. We evaluate all our methods on several publicly available video and image semantic segmentation datasets, and demonstrate superior performance in efficiency and accuracy.

Download or read book Fuzzy Logic and Applications written by Francesco Masulli and published by Springer. This book was released on 2013-11-08 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 10th International Workshop on Fuzzy Logic and Applications, WILF 2013, held in Genoa, Italy, in November 2013. After a rigorous peer-review selection process, ultimately 19 regular papers were selected for inclusion in this volume from 29 submissions. In addition the book contains 3 keynote talks and 2 tutorials. The papers are organized in topical sections named: fuzzy machine learning and interpretability; theory and applications.

Download or read book XxAI Beyond Explainable AI written by Andreas Holzinger and published by Springer Nature. This book was released on 2022 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.

Download or read book Sensor Management in ISR written by Kenneth J. Hintz and published by Artech House. This book was released on 2020-02-29 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: This innovative resource is the first book that partitions the intelligence, surveillance and reconnaissance (ISR) sensor management process into partitioned functions that can be studied and optimized independently of each other through defined conceptual interfaces. The book explains the difference between situation information and sensor information and how to compute both. The information-based sensor management (IBSM) approach to real-time orchestrated resource management (ORM) of intelligence, surveillance, and reconnaissance (ISR) assets in the physical, cyber, and social domains are detailed. The integrating concept of mission value through use of goal lattice (GL) methodology is explored. Approaches to implementing real-time sensor management (SM) systems by applying advanced information-based approaches that consider contextual situation and optimization of diverse sensor capabilities for information-based objectives are also covered. These methods have applications in physical intelligence, surveillance, and reconnaissance (ISR), as well as in cyber, and social domains. Based on 30 years of research in developing a mission-valued approach to maximizing the transfer of information from real, cyber, and social environments into a mission-valued, probabilistic representation of that environment on which decision makers can formulate actions, this is the only book that addresses real-time management of ISR from a first principles approach (information theory), and how information theory can be applied to the design and development of ISR systems.

Download or read book Neural and Stochastic Methods in Image and Signal Processing written by and published by . This book was released on 1992 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Geometric Reasoning written by Deepak Kapur and published by Bradford Book. This book was released on 1989 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geometry is at the core of understanding and reasoning about the form of physical objects and spatial relations which are now recognized to be crucial to many applications in artificial intelligence. The 20 contributions in this book discuss research in geometric reasoning and its applications to robot path planning, vision, and solid modeling. During the 1950s when the field of artificial intelligence was emerging, there were significant attempts to develop computer programs to mechanically perform geometric reasoning. This research activity soon stagnated because the classical AI approaches of rule based inference and heuristic search failed to produce impressive geometric, reasoning ability. The extensive research reported in this book, along with supplementary review articles, reflects a renaissance of interest in recent developments in algebraic approaches to geometric reasoning that can be used to automatically prove many difficult plane geometry theorems in a few seconds on a computer. Deepak Kapur is Professor in the Department of Computer Science at the State University of New York Albany. Joseph L. Mundy is a Coolidge Fellow at the Research and Development Center at General Electric. Geometric Reasoningis included in the series Special Issues from Artificial Intelligence: An International Journal. A Bradford Book

Download or read book Advances in Scalable and Intelligent Geospatial Analytics written by Surya S Durbha and published by CRC Press. This book was released on 2023-05-12 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geospatial data acquisition and analysis techniques have experienced tremendous growth in the last few years, providing an opportunity to solve previously unsolved environmental- and natural resource-related problems. However, a variety of challenges are encountered in processing the highly voluminous geospatial data in a scalable and efficient manner. Technological advancements in high-performance computing, computer vision, and big data analytics are enabling the processing of big geospatial data in an efficient and timely manner. Many geospatial communities have already adopted these techniques in multidisciplinary geospatial applications around the world. This book is a single source that offers a comprehensive overview of the state of the art and future developments in this domain. FEATURES Demonstrates the recent advances in geospatial analytics tools, technologies, and algorithms Provides insight and direction to the geospatial community regarding the future trends in scalable and intelligent geospatial analytics Exhibits recent geospatial applications and demonstrates innovative ways to use big geospatial data to address various domain-specific, real-world problems Recognizes the analytical and computational challenges posed and opportunities provided by the increased volume, velocity, and veracity of geospatial data This book is beneficial to graduate and postgraduate students, academicians, research scholars, working professionals, industry experts, and government research agencies working in the geospatial domain, where GIS and remote sensing are used for a variety of purposes. Readers will gain insights into the emerging trends on scalable geospatial data analytics.