Graph Vision
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Graph Vision
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.
Graph Vision
"This book details how a diverse set of architects began adopting graph theory to transform architectural theory and design, prompting a shift from geometry, which long held pride of place in architecture, to topology, which instead emphasized relations"--
Graph Convolutional Neural Networks for Computer Vision
Author: Malini Alagarsamy
language: en
Publisher: John Wiley & Sons
Release Date: 2026-01-09
Revolutionize your machine learning practice with this essential book that provides expert insights into leveraging Graph Convolutional Networks (GCNNs) to overcome the limitations of traditional CNNs. In the last decade, computer vision has become a major focus for addressing the world’s growing processing needs. Many existing deep learning architectures for computer vision challenges are based on convolutional neural networks (CNNs). Despite their great achievements, CNNs struggle to encode the intrinsic graph patterns in specific learning tasks. In contrast, graph convolutional networks have been used to address several computer vision issues with equivalent or superior results. The use of GCNNs has shown significant achievement in image classifications, video understanding, point clouds, meshes, and other applications in natural language processing. This book focuses on the applications of graph convolutional networks in computer vision. Through expert insights, it explores how researchers are finding ways to perform convolution algorithms on graphs to improve the way we use machine learning.
