Download or read book Graph Vision written by Theodora Vardouli and published by MIT Press. This book was released on 2024-11-12 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: How a protean mathematical object, the graph, ushered in new images, tools, and infrastructures for design and catalyzed a digital future for architecture. In Graph Vision, Theodora Vardouli offers a fresh history of architecture’s early entanglements with modern mathematics and digital computing by focusing on a hidden protagonist: the graph. Fueled by iconoclastic sentiments and skepticism of geometric depiction, architects, she explains, turned to the skeletal underpinnings of their work, and with it the graph, as a site of representation, operation, and political possibility. Taking the reader on an enthralling journey through a polyvalent mathematical entity, Vardouli combines close readings of graphs’ architectural manifestations as images, tools, and infrastructures for design with original archival work on research centers that spearheaded mathematical and computational approaches to architecture. Structured thematically, Graph Vision weaves together archival findings on influential research groups such as the Land Use Built Form Studies Center at the University of Cambridge, the Center for Environmental Structure at Berkeley, the Architecture Machine Group at the Massachusetts Institute of Technology, among others, as well as important figures who led, or worked in proximity to, these groups, including Lionel March, Christopher Alexander, and Yona Friedman. Together, this material chronicles the emergence of both a new way of seeing and a new prospect for the discipline that prefigured its digital future—of a “graph vision.” Vardouli argues that this vision was one of vacillation toward visual appearance. Digital approaches to architecture, she ultimately reveals, were founded on a profound ambivalence toward the visual realm endemic to mid-twentieth century architectural and mathematical modernisms.
Download or read book Graph Based Methods in Computer Vision Developments and Applications written by Bai, Xiao and published by IGI Global. This book was released on 2012-07-31 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision, the science and technology of machines that see, has been a rapidly developing research area since the mid-1970s. It focuses on the understanding of digital input images in many forms, including video and 3-D range data. Graph-Based Methods in Computer Vision: Developments and Applications presents a sampling of the research issues related to applying graph-based methods in computer vision. These methods have been under-utilized in the past, but use must now be increased because of their ability to naturally and effectively represent image models and data. This publication explores current activity and future applications of this fascinating and ground-breaking topic.
Download or read book Applied Graph Theory in Computer Vision and Pattern Recognition written by Abraham Kandel and published by Springer Science & Business Media. This book was released on 2007-03-12 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents novel graph-theoretic methods for complex computer vision and pattern recognition tasks. It presents the application of graph theory to low-level processing of digital images, presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, and provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks.
Download or read book Graph Neural Networks Foundations Frontiers and Applications written by Lingfei Wu and published by Springer Nature. This book was released on 2022-01-03 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
Download or read book Handbook of Mathematical Models in Computer Vision written by Nikos Paragios and published by Springer Science & Business Media. This book was released on 2006-01-16 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract Biological vision is a rather fascinating domain of research. Scientists of various origins like biology, medicine, neurophysiology, engineering, math ematics, etc. aim to understand the processes leading to visual perception process and at reproducing such systems. Understanding the environment is most of the time done through visual perception which appears to be one of the most fundamental sensory abilities in humans and therefore a significant amount of research effort has been dedicated towards modelling and repro ducing human visual abilities. Mathematical methods play a central role in this endeavour. Introduction David Marr's theory v^as a pioneering step tov^ards understanding visual percep tion. In his view human vision was based on a complete surface reconstruction of the environment that was then used to address visual subtasks. This approach was proven to be insufficient by neuro-biologists and complementary ideas from statistical pattern recognition and artificial intelligence were introduced to bet ter address the visual perception problem. In this framework visual perception is represented by a set of actions and rules connecting these actions. The emerg ing concept of active vision consists of a selective visual perception paradigm that is basically equivalent to recovering from the environment the minimal piece information required to address a particular task of interest.
Download or read book Computer Vision ECCV 2010 written by Kostas Daniilidis and published by Springer. This book was released on 2010-09-08 with total page 828 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 2010 edition of the European Conference on Computer Vision was held in Heraklion, Crete. The call for papers attracted an absolute record of 1,174 submissions. We describe here the selection of the accepted papers: Thirty-eight area chairs were selected coming from Europe (18), USA and Canada (16), and Asia (4). Their selection was based on the following criteria: (1) Researchers who had served at least two times as Area Chairs within the past two years at major vision conferences were excluded; (2) Researchers who served as Area Chairs at the 2010 Computer Vision and Pattern Recognition were also excluded (exception: ECCV 2012 Program Chairs); (3) Minimization of overlap introduced by Area Chairs being former student and advisors; (4) 20% of the Area Chairs had never served before in a major conference; (5) The Area Chair selection process made all possible efforts to achieve a reasonable geographic distribution between countries, thematic areas and trends in computer vision. Each Area Chair was assigned by the Program Chairs between 28–32 papers. Based on paper content, the Area Chair recommended up to seven potential reviewers per paper. Such assignment was made using all reviewers in the database including the conflicting ones. The Program Chairs manually entered the missing conflict domains of approximately 300 reviewers. Based on the recommendation of the Area Chairs, three reviewers were selected per paper (with at least one being of the top three suggestions), with 99.
Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Download or read book Deep Learning on Graphs written by Yao Ma and published by Cambridge University Press. This book was released on 2021-09-23 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.
Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2024 written by Marius George Linguraru and published by Springer Nature. This book was released on with total page 815 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Graph Embedding for Pattern Analysis written by Yun Fu and published by Springer Science & Business Media. This book was released on 2012-11-19 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.
Download or read book A Horizontal vertical Illusion of Brightness in Foveal Vision Apparent in Astronomical Observations of the Relative Luminosity of Twin Stars written by Joseph Wanton Hayes and published by . This book was released on 1916 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Courage and Vision to Experiment written by and published by . This book was released on 1991 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Image Processing and Analysis with Graphs written by Olivier Lezoray and published by CRC Press. This book was released on 2017-07-12 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering the theoretical aspects of image processing and analysis through the use of graphs in the representation and analysis of objects, Image Processing and Analysis with Graphs: Theory and Practice also demonstrates how these concepts are indispensible for the design of cutting-edge solutions for real-world applications. Explores new applications in computational photography, image and video processing, computer graphics, recognition, medical and biomedical imaging With the explosive growth in image production, in everything from digital photographs to medical scans, there has been a drastic increase in the number of applications based on digital images. This book explores how graphs—which are suitable to represent any discrete data by modeling neighborhood relationships—have emerged as the perfect unified tool to represent, process, and analyze images. It also explains why graphs are ideal for defining graph-theoretical algorithms that enable the processing of functions, making it possible to draw on the rich literature of combinatorial optimization to produce highly efficient solutions. Some key subjects covered in the book include: Definition of graph-theoretical algorithms that enable denoising and image enhancement Energy minimization and modeling of pixel-labeling problems with graph cuts and Markov Random Fields Image processing with graphs: targeted segmentation, partial differential equations, mathematical morphology, and wavelets Analysis of the similarity between objects with graph matching Adaptation and use of graph-theoretical algorithms for specific imaging applications in computational photography, computer vision, and medical and biomedical imaging Use of graphs has become very influential in computer science and has led to many applications in denoising, enhancement, restoration, and object extraction. Accounting for the wide variety of problems being solved with graphs in image processing and computer vision, this book is a contributed volume of chapters written by renowned experts who address specific techniques or applications. This state-of-the-art overview provides application examples that illustrate practical application of theoretical algorithms. Useful as a support for graduate courses in image processing and computer vision, it is also perfect as a reference for practicing engineers working on development and implementation of image processing and analysis algorithms.
Download or read book Computer Vision Advances and applications written by Rangachar Kasturi and published by Institute of Electrical & Electronics Engineers(IEEE). This book was released on 1991 with total page 728 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Repertory to the More Characteristic Symptoms of the Materia Medica written by Constantine Lippe and published by . This book was released on 1879 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Repertory to the More Characteristic Symptoms of the New Materia Medica written by Constantine Lippe and published by . This book was released on 1879 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Fifth IEEE Workshop on Applications of Computer Vision written by and published by IEEE Computer Society Press. This book was released on 2000 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains 36 contributed papers (24 oral presentations, 15 poster presentations, and several invited talks, keynote presentations, and a panel discussion) presenting the work of academic, industrial, and government researchers and practitioners discussing the development of practical and novel application-specific methods and applications of computer vision. Topics include image enhancement, document and image processing and analysis, classification and recognition, novel application domains, medical image analysis, roads and traffic, road and range analysis, 3D modeling, motion and tracking, video applications, and motion and stereo. Somewhat fuzzy, bandw photographs that appear to be simply photocopied. Lacks a subject index. Annotation copyrighted by Book News, Inc., Portland, OR.
