External Labeling Techniques: A Taxonomy and Survey

Bekos, Michael A.; Niedermann, Benjamin; Nöllenburg, Martin

doi:10.1111/cgf.13729

Cited by 28 publications

(21 citation statements)

References 88 publications

(298 reference statements)

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“…We found a similar performance of SITUATED and BOUNDARY across all tasks. In 2D visualizations, boundary labeling is a standard way to reduce visual clutter in many scenarios, and was found to be beneficial [1]. However, we could not confirm the advantage of boundary labels in our AR study.…”

Section: Ar Label Properties and Considerationscontrasting

confidence: 58%

“…To make search results pop out in a visualization, it is common to show icons or labels overlaid on top of the original visualization (e.g., restaurants on a map). To avoid occluding local visualization features, external labeling techniques [1] place labels outside the visualization, and use leader lines to connect the visual elements with their associated labels. Other techniques allow users to zoom out to an overview to locate the searched for elements, and then zoom in to check the details.…”

Section: Introductionmentioning

confidence: 99%

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Labeling Out-of-View Objects in Immersive Analytics to Support Situated Visual Searching

Lin,

Yang,

Beyer

et al. 2021

Preprint

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Augmented Reality (AR) embeds digital information into objects of the physical world. Data can be shown in-situ, thereby enabling real-time visual comparisons and object search in real-life user tasks, such as comparing products and looking up scores in a sports game. While there have been studies on designing AR interfaces for situated information retrieval, there has only been limited research on AR object labeling for visual search tasks in the spatial environment. In this paper, we identify and categorize different design aspects in AR label design and report on a formal user study on labels for out-of-view objects to support visual search tasks in AR. We design three visualization techniques for out-of-view object labeling in AR, which respectively encode the relative physical position (height-encoded), the rotational direction (angle-encoded), and the label values (value-encoded) of the objects. We further implement two traditional in-view object labeling techniques, where labels are placed either next to the respective objects (situated) or at the edge of the AR FoV (boundary). We evaluate these five different label conditions in three visual search tasks for static objects.Our study shows that out-of-view object labels are beneficial when searching for objects outside the FoV, spatial orientation, and when comparing multiple spatially sparse objects. Angle-encoded labels with directional cues of the surrounding objects have the overall best performance with the highest user satisfaction. We discuss the implications of our findings for future immersive AR interface design.

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Section: Ar Label Properties and Considerationscontrasting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Labeling Out-of-View Objects in Immersive Analytics to Support Situated Visual Searching

Lin,

Yang,

Beyer

et al. 2021

Preprint

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“…Labeling is an automatic approach to position text or image labels in order to efficiently communicate additional information about POIs. It improves clarity and understandability of the underlying information [2]. Internal labels are overlaid onto their reference objects.…”

Section: Labeling In Various Spaces (2d 3d Vr and Ar)mentioning

confidence: 99%

“…We employ ray tracing to detect occlusions, which is different from existing approaches [2]. As the labels have been sorted by the distance to the user, the occlusions are detected and solved iteratively from label l 1 to label l n of the sorted list S. For each label l i , the origins of four rays are set to the location of the user's device in AR.…”

Section: Occlusion Detectionmentioning

confidence: 99%

Context-Responsive Labeling in Augmented Reality

Köppel,

Gröller,

2021

Preprint

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“…We visually associate the labels with their point features 1 by connecting them via a thin curve, also called leader. Placing the labels alongside the boundary of a rectangular map or figure is known in the literature as boundary labeling, which is a special case of external labeling [5]. For small-screen devices the number k of labels that can be placed at the bottom of the map is typically smallin our experiments we set k = 5.…”

Section: Introductionmentioning

confidence: 99%

Zoomless Maps: External Labeling Methods for the Interactive Exploration of Dense Point Sets at a Fixed Map Scale

Gedicke¹,

Bonerath²,

Niedermann³

et al. 2020

Preprint

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Visualizing spatial data on small-screen devices such as smartphones and smartwatches poses new challenges in computational cartography. The current interfaces for map exploration require their users to zoom in and out frequently. Indeed, zooming and panning are tools suitable for choosing the map extent corresponding to an area of interest. They are not as suitable, however, for resolving the graphical clutter caused by a high feature density since zooming in to a large map scale leads to a loss of context. Therefore, in this paper, we present new external labeling methods that allow a user to navigate through dense sets of points of interest while keeping the current map extent fixed. We provide a unified model, in which labels are placed at the boundary of the map and visually associated with the corresponding features via connecting lines, which are called leaders. Since the screen space is limited, labeling all features at the same time is impractical. Therefore, at any time, we label a subset of the features. We offer interaction techniques to change the current selection of features systematically and, thus, give the user access to all features. We distinguish three methods, which allow the user either to slide the labels along the bottom side of the map or to browse the labels based on pages or stacks. We present a generic algorithmic framework that provides us with the possibility of expressing the different variants of interaction techniques as optimization problems in a unified way. We propose both exact algorithms and fast and simple heuristics that solve the optimization problems taking into account different criteria such as the ranking of the labels, the total leader length as well as the distance between leaders. In experiments on real-world data we evaluate these algorithms and discuss the three variants with respect to their strengths and weaknesses proving the flexibility of the presented algorithmic framework.

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External Labeling Techniques: A Taxonomy and Survey

Cited by 28 publications

References 88 publications

Labeling Out-of-View Objects in Immersive Analytics to Support Situated Visual Searching

Labeling Out-of-View Objects in Immersive Analytics to Support Situated Visual Searching

Context-Responsive Labeling in Augmented Reality

Zoomless Maps: External Labeling Methods for the Interactive Exploration of Dense Point Sets at a Fixed Map Scale

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