A Perceptual-Statistics Shading Model

Šoltészová, Veronika; Turkay, Çağatay; Price, M. C.; Viola, Ivan

doi:10.1109/tvcg.2012.188

Cited by 8 publications

(10 citation statements)

References 37 publications

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“…While some of the previous evaluations of the depth perception of volume rendered images have been conducted with a special domain focus [Ropinski et al 2006;Kersten-Oertel et al 2014], our study keeps the domain open, and thus does not make implications about the nature of the used data sets. Apart from these depth perception evaluations, several authors have presented studies in which they have investigated shape perception, e. g., [Baer et al 2011;Solteszova et al 2012]. Interrante et al have for instance focused on the perception of surface shape, by determining how texturing affects the perception of nested semitransparent surfaces [1997].…”

Section: Related Workmentioning

confidence: 99%

Evaluating the perception of semi-transparent structures in direct volume rendering techniques

Englund

Ropinski²

2016

SIGGRAPH ASIA 2016 Symposium on Visualization

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Direct volume rendering (DVR) provides the possibility to visualize volumetric data sets as they occur in many scientific disciplines. A key benefit of DVR is that semi-transparency can be facilitated in order to convey the complexity of the visualized data. Unfortunately, semi-transparency introduces new challenges in spatial comprehension of the visualized data, as the ambiguities inherent to semi-transparent representations affect spatial comprehension. Accordingly, many visualization techniques have been introduced to enhance the spatial comprehension of DVR images. In this paper, we conduct a user evaluation in which we compare standard DVR with five visualization techniques which have been proposed to enhance the spatial comprehension of DVR images. In our study, we investigate the perceptual performance of these techniques and compare them against each other to find out which technique is most suitable for different types of data and purposes. In order to do this, a large-scale user study was conducted with 300 participants who completed a number of micro-tasks designed such that the aggregated feedback gives us insight on how well these techniques aid the end user to perceive depth and shape of objects. Within this paper we discuss the tested techniques, present the conducted study and analyze the retrieved results.

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Section: Related Workmentioning

confidence: 99%

Evaluating the perception of semi-transparent structures in direct volume rendering techniques

Englund

Ropinski²

2016

SIGGRAPH ASIA 2016 Symposium on Visualization

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“…To identify, which schemes an evaluation system should support, we have reviewed quantitative user evaluations earlier reported in the visualization literature. Based on this analysis we have decided to confine ourselves to evaluations which include static images only, as they have been described in Kosara's overview [27], and as they are facilitated in many recent publications about evaluations, for example, [31,45,18,17,3]. While not all publications describe the used questionnaires in all necessary detail, in most cases we were able to reconstruct the underlying scheme based on the result analysis.…”

Section: Trial Generationmentioning

confidence: 99%

“…In many cases, this correctness might be encoded in a pixel's color, either in the stimuli itself but mostly in one of the annotation layers. We found this especially useful for evaluations exploiting markers, were the distance to the camera [18], surface normals [45], or object size [31] have to be judged. In some occasions the correctness cannot be specified a priori or annotation layers are not available, for example when reusing stimuli from old user evaluations, then the correct value can be stored in the stimulus as a label.…”

Section: Response Data Processingmentioning

confidence: 99%

A crowdsourcing system for integrated and reproducible evaluation in scientific visualization

Englund

Kottravel

Ropinski

2016

2016 IEEE Pacific Visualization Symposium (PacificVis)

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User evaluations have gained increasing importance in visualization research over the past years, as in many cases these evaluations are the only way to support the claims made by visualization researchers. Unfortunately, recent literature reviews show that in comparison to algorithmic performance evaluations, the number of user evaluations is still very low. Reasons for this are the required amount of time to conduct such studies together with the difficulties involved in participant recruitment and result reporting. While it could be shown that the quality of evaluation results and the simplified participant recruitment of crowdsourcing platforms makes this technology a viable alternative to lab experiments when evaluating visualizations, the time for conducting and reporting such evaluations is still very high. In this paper, we propose a software system, which integrates the conduction, the analysis and the reporting of crowdsourced user evaluations directly into the scientific visualization development process. With the proposed system, researchers can conduct and analyze quantitative evaluations on a large scale through an evaluation-centric user interface with only a few mouse clicks. Thus, it becomes possible to perform iterative evaluations during algorithm design, which potentially leads to better results, as compared to the time consuming user evaluations traditionally conducted at the end of the design process. Furthermore, the system is built around a centralized database, which supports an easy reuse of old evaluation designs and the reproduction of old evaluations with new or additional stimuli, which are both driving challenges in scientific visualization research. We will describe the system's design and the considerations made during the design process, and demonstrate the system by conducting three user evaluations, all of which have been published before in the visualization literature.

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“…The goal is to bridge the gap between the findings made with respect to realism in VREs, and the impact of volume illumination models on task performance as reported in the visualization literature [21,39,40].…”

Section: Virtual Reality Evaluationmentioning

confidence: 99%

An experimental study on the effects of shading in 3D perception of volumetric models

Díaz¹,

Ropinski

Navazo

et al. 2015

Vis Comput

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Throughout the years, many shading techniques have been developed to improve the conveying of information in volume visualization. Some of these methods, usually referred to as realistic, are supposed to provide better cues for the understanding of volume data sets. While shading approaches are heavily exploited in traditional monoscopic setups, no previous study has analyzed the effect of these techniques in virtual reality. To further explore the influence of shading on the understanding of volume data in such environments, we carried out a user study in a desktop-based stereoscopic setup. The goals of the study were to investigate the impact of well-known shading approaches and the influence of real illumination on depth perception. Participants had to perform three different perceptual tasks when exposed to static visual stimuli. 45 participants took part in the study, giving us 1152 trials for each task. Results show that advanced shading techniques improve depth perception in stereoscopic volume visualization. As well, external lighting does not affect depth perception when these shading methods are applied. As a result, we derive some guidelines that may help the researchers when selecting illumination models for stereoscopic rendering.Peer ReviewedPostprint (published version

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A Perceptual-Statistics Shading Model

Cited by 8 publications

References 37 publications

Evaluating the perception of semi-transparent structures in direct volume rendering techniques

Evaluating the perception of semi-transparent structures in direct volume rendering techniques

A crowdsourcing system for integrated and reproducible evaluation in scientific visualization

An experimental study on the effects of shading in 3D perception of volumetric models

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