Information visualization: perception for design by Colin Ware

By Veit Jahns. 2000

2,398 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

A survey of data fusion in smart city applications (2019, background): “ique used for the presentation of output to the end users via some platform. The end result often requires human intervention.Examples of such techniques can be referred to the following papers [78]– [80]. C. Data Input and Output Types (D) Dasarathy’s classiﬁcation [39] is based on input and output”
A Multi-Level Typology of Abstract Visualization Tasks (2013, background): “verify, analyze [42, 50]*[40], support, reevaluate (hypotheses) [51]*, monitoring [76], confirm (hypotheses), expose (uncertainty),”
The Science of Visual Data Communication: What Works (2021, background): “Red and green will combine in an integral fashion when both are at their highest value, and the viewer will see a single integral percept of yellow (Ware, 2019).”
A Survey of Visualization Systems for Network Security (2012, neutral): “Ç”
Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities (2016, methodology): “Whereas recommendation algorithms find interesting items in large data sets automatically, information visualization makes use of the principles in Gestalt Theory that explain the human visual capacity, such as proximity, similarity, continuity, symmetry, closure and relative size (Ware, 2000).”
Visualizing Big Data with augmented and virtual reality: challenges and research agenda (2015, background): “Information Visualization [108–111] emerged as a branch of the Human-Computer Interaction field in the end of 1980s.”
Big data, bigger dilemmas: A critical review (2015, methodology): “The actual layout of such visualizations, as an artifact of both the algorithm and physical limitations such as plotter paper, are in fact more frequently arbitrary than not (Ware, 2013).”
Temporal Event Sequence Simplification (2013, background): “Filtering keeps control in the users’ hands, allowing them to remove events and records based on preexisting attributes.”
To go where no man has gone before: Virtual reality in architecture, landscape architecture and environmental planning (2015, methodology): “representation of data in graphs and tables (Hansen & Machin, 2013; Valiela, 2009; Ware, 2013).”
An Interactive Node-Link Visualization of Convolutional Neural Networks (2015, methodology): “The colormap is consistent with colour-encoding theory, which encourages a mapping across value for ordered elements [25], and discourages the use of a full rainbow pallette [26].”