A new theory and methodology for the application of computer vision methods to the computational analysis of collected, digitized visual materials, called "distant viewing." Distant Viewing: Computational Exploration of Digital Images presents a new theory and methodology for the computational analysis of digital images, offering a lively, constructive critique of computer vision that you can actually use. What does it mean to say that computer vision "understands" visual inputs? Annotations never capture a whole image. The way digital images convey information requires what researchers Taylor Arnold and Lauren Tilton call "distant viewing"--a play off the well-known term "distant reading" from computational literary analysis. Recognizing computer vision's limitations, Arnold and Tilton's spirited examination makes the technical exciting by applying distant viewing to the sitcoms Bewitched and I Dream of Jeannie, movie posters and other popular forms of advertising, and Dorothea Lange's photography. In the tradition of visual culture studies and computer vision, Distant Viewing's interdisciplinary perspective encompasses film and media studies, visual semiotics, and the sciences to create a playful, accessible guide for an international audience working in digital humanities, data science, media studies, and visual culture studies.

Distant Viewing: Computational Exploration of Digital Images

Arnold, Taylor

Tilton, Lauren

Reference

The MIT Press

This book teaches readers to integrate data analysis techniques into humanities research practices using the R programming language. Methods for general-purpose visualization and analysis are introduced first, followed by domain-specific techniques for working with networks, text, geospatial data, temporal data, and images. The book is designed to be a bridge between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. The second edition of the text is a significant revision, with almost every aspect of the text rewritten in some way. The most notable difference is the incorporation of new R packages such as ggplot2 and dplyr that center broad data-science concepts. This 2nd edition of Humanities Data with R&nbsp;does not presuppose background&nbsp;programming experience. Early chapters take readers from R set-up to exploratory data analysis, with one chapter dedicated to each stage of the data-science pipeline (data collection, visualization, manipulation, and relational joins). Following this, text analysis, networks, temporal data, geospatial data, and image analysis each have a dedicated chapter. These are grounded in examples to move readers beyond the intimidation of adding new tools to their research.&nbsp;The final section of the book extends the core material with additional computer science techniques for processing large datasets. Everything is hands-on: image analysis is explained using digitized photographs from the 1930s, and networks are applied to page links on Wikipedia. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix. The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries.

Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text (Quantitative Methods in the Humanities and Social Sciences)

Computers & Technology

Software

Springer

This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. This book is also designed to be a bridge: between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility). Following this, networks, geospatial data, image data, natural language processing and text analysis each have a dedicated chapter. Each chapter is grounded in examples to move readers beyond the intimidation of adding new tools to their research. Everything is hands-on: networks are explained using U.S. Supreme Court opinions, and low-level NLP methods are applied to short stories by Sir Arthur Conan Doyle. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix. This book uses an expanded conception of the forms data may take and the information it represents. The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries.

Computer Science

This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. This book is also designed to be a bridge: between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility). Following this, networks, geospatial data, image data, natural language processing and text analysis each have a dedicated chapter. Each chapter is grounded in examples to move readers beyond the intimidation of adding new tools to their research. Everything is hands-on: networks are explained using U.S. Supreme Court opinions, and low-level NLP methods are applied to short stories by Sir Arthur Conan Doyle. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix. This book uses an expanded conception of the forms data may take and the information it represents. The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries.    

See the best price to sell, buy, or rent books by Tilton, Lauren. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Tilton, Lauren

See the best price to sell, buy, or rent books by Tilton, Lauren. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Distant Viewing: Computational Exploration of Digital Images

Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text (Quantitative Methods in the Humanities and Social Sciences)

Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text (Quantitative Methods in the Humanities and Social Sciences)

Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text (Quantitative Methods in the Humanities and Social Sciences)