Managing Visual Clutter: A Generalized Technique for Label Segregation using Stereoscopic Disparity

Peterson, Stephen D.; Axholt, Magnus; Ellis, Stephen R.

doi:10.1109/vr.2008.4480769

Cited by 24 publications

(10 citation statements)

References 14 publications

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“…The current results add to the previous findings [21] on the label layering technique; we now know that i) the label order should be correlated with the distance order of the background objects, ii) the height in the visual field of the labels does not need to be correlated with that of the background objects, iii) the interlayer disparity should be in the range 5-10 arcmin, iv) the label layering technique works not only in a laboratory environment but also in a far-field AR setting with optical properties similar to those in an ATC tower. This empirical baseline data is important in the future development of an automated label layering algorithm using stereoscopic disparity, which effectively expands the design space and could alleviate the need for compromises required by traditional decluttering methods, such as filtering, dimming, aggregation, or label size reduction.…”

Section: Discussionsupporting

confidence: 89%

“…We have conducted a previous investigation of the benefits for visual search of label layering in laboratory conditions [21]. The experiment reported in this paper extends that work to a setting with optical conditions closer to those in an ATC tower, a far-field environment with small natural accommodation and vergence demands due to the large distance.…”

Section: Introductionmentioning

confidence: 64%

“…Previous work on label layering has shown that distributing multiple overlapping labels in depth significantly improves user performance in a visual search task [21]. It was determined that the depth order of the labels must be correlated with that of the overlaid objects; a random depth distribution of labels conversely significantly degraded user performance, likely due to a mismatch in depth cues between labels and objects hampering label-object integration.…”

Section: Related Workmentioning

confidence: 99%

“…The depth order of the labels was correlated with the depth order of the corresponding markers, to satisfy the criterion of ordered disparity in [21]. In each of these conditions the closest label layer was located at 2.2 m, while the farthest label layer was located at 11 m in the large interlayer disparity condition.…”

Section: Independent Variablesmentioning

confidence: 99%

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Label segregation by remapping stereoscopic depth in far-field augmented reality

Peterson

Axholt

Ellis

2008

2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality

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This paper describes a novel technique for segregating overlapping labels in stereoscopic see-through displays. The present study investigates the labeling of far-field objects, with distances ranging 100-120 m. At these distances the stereoscopic disparity difference between objects is below 1 arcmin, so labels rendered at the same distance as their corresponding objects appear as if on a flat layer in the display. This flattening is due to limitations of both display and human visual resolution. By remapping labels to predetermined depth layers on the optical path between the observer and the labeled object, an interlayer disparity ranging from 5 to 20 arcmin can be achieved for 5 overlapping labels. The present study evaluates the impact of such depth separation of superimposed layers, and found that a 5 arcmin interlayer disparity yields a significantly lower response time, over 20% on average, in a visual search task compared to correctly registering labels and objects in depth. Notably the performance does not improve when doubling the interlayer disparity to 10 arcmin and, surprisingly, the performance degrades significantly when again doubling the interlayer disparity to 20 arcmin, approximating the performance in situations with no interlayer disparity. These results confirm that our technique can be used to segregate overlapping labels in the far visual field, without the cost associated with traditional label placement algorithms.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 64%

Section: Related Workmentioning

confidence: 99%

Section: Independent Variablesmentioning

confidence: 99%

See 2 more Smart Citations

Label segregation by remapping stereoscopic depth in far-field augmented reality

Peterson

Axholt

Ellis

2008

2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality

Self Cite

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“…In military applications this is referred to as "camouflage breaking." Work by Peterson et al [23][24] uses stereoscopic displays to segregate overlapping labels. They concentrate on the issue of visual clutter associated with label overlap and propose utilizing stereo "label-layering" to address the problem.…”

Section: Related Workmentioning

confidence: 99%

Stereoscopic Highlighting: 2D Graph Visualization on Stereo Displays

Alper

Höllerer

Kuchera-Morin

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. The stereoscopic highlighting technique illustrated with separate images for the left and right eye. The highlighted yellow node and all of its immediate neighbors are rendered at a depth closer to the viewer. The two images above represent a stereo pair. By crossing eyes, the reader can perceive the 3rd dimension of the stereoscopic highlighting.Abstract-In this paper we present a new technique and prototype graph visualization system, stereoscopic highlighting, to help answer accessibility and adjacency queries when interacting with a node-link diagram. Our technique utilizes stereoscopic depth to highlight regions of interest in a 2D graph by projecting these parts onto a plane closer to the viewpoint of the user. This technique aims to isolate and magnify specific portions of the graph that need to be explored in detail without resorting to other highlighting techniques like color or motion, which can then be reserved to encode other data attributes. This mechanism of stereoscopic highlighting also enables focus+context views by juxtaposing a detailed image of a region of interest with the overall graph, which is visualized at a further depth with correspondingly less detail. In order to validate our technique, we ran a controlled experiment with 16 subjects comparing static visual highlighting to stereoscopic highlighting on 2D and 3D graph layouts for a range of tasks. Our results show that while for most tasks the difference in performance between stereoscopic highlighting alone and static visual highlighting is not statistically significant, users performed better when both highlighting methods were used concurrently. In more complicated tasks, 3D layout with static visual highlighting outperformed 2D layouts with a single highlighting method. However, it did not outperform the 2D layout utilizing both highlighting techniques simultaneously. Based on these results, we conclude that stereoscopic highlighting is a promising technique that can significantly enhance graph visualizations for certain use cases.

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