Название: External Labeling: Fundamental Concepts and Algorithmic Techniques
Автор: Michael A. Bekos, Benjamin Niedermann, Martin Nöllenburg
Издательство: Morgan & Claypool
Серия: Synthesis Lectures on Visualization
Год: 2021
Страниц: 132
Язык: английский
Формат: pdf (true)
Размер: 12.3 MB

This book focuses on techniques for automating the procedure of creating external labelings, also known as callout labelings. In this labeling type, the features within an illustration are connected by thin leader lines (called leaders) with their labels, which are placed in the empty space surrounding the image.

In general, textual labels describing graphical features in maps, technical illustrations (such as assembly instructions or cutaway illustrations), or anatomy drawings are an important aspect of visualization that convey information on the objects of the visualization and help the reader understand what is being displayed.

Most labeling techniques can be classified into two main categories depending on the "distance" of the labels to their associated features. Internal labels are placed inside or in the direct neighborhood of features, while external labels, which form the topic of this book, are placed in the margins outside the illustration, where they do not occlude the illustration itself. Both approaches form well-studied topics in diverse areas of computer science with several important milestones.

As a team of three researchers with a background on formal, algorithmic methods in graph drawing, computational geometry, and information visualization, we have worked ourselves on many external labeling problems, both from theoretical and practical perspectives. After years of research experience it turned out that there is a small set of algorithm design techniques, which can be used to solve a large number of external labeling problems. Thus, one goal of this book is to summarize and explain these techniques to readers with different backgrounds in computer science and related disciplines.

Moreover, we observed that there is a multitude of labeling models with various important parameters, but no commonly used taxonomy guiding experts and novices alike through the existing state of the art. This is due not least because external labeling is studied in many different fields such as algorithm design, information visualization, computer graphics, or virtual/augmented reality, all with their own approaches to the respective problems—from the mathematical curiosity of basic research to the practical needs of creating readable visualizations. A second goal of the book is thus to unify the diverse labeling models and provide a common taxonomy, which facilitates classifying new research results.

A third part of the book covers the existing state of the art in a well-structured way, both in a compact tabular form—where each method is described according to a set of important parameters—as well as in a more detailed description of the respective results. Finally, we provide a collection of ten research challenges in external labeling to be seen as opportunities for interdisciplinary research collaborations in the coming years.