On visual complexity of 3D shapes

Saleem, Waqar; Belyaev, Alexander; Wang, Danyi; Seidel, Hans‐Peter

doi:10.1016/j.cag.2011.03.006

Cited by 23 publications

(22 citation statements)

References 9 publications

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“…Depicting eye-tracking fixations, saccades, and scan paths is also a visualization issue. The output visualization can produce many fixations that overlay each other, as a user may observe a virtual 3D object of interest from many different angles and distances (we can argue that not only do 3D visual stimuli offer more information than their 2D counterparts [86], they also require more cognitive processing [87], i.e., dwell time). It is also relevant that the user moves about in the 3D environment.…”

Section: Data Visualization and Related Algorithmsmentioning

confidence: 99%

Eye-Tracking in Interactive Virtual Environments: Implementation and Evaluation

et al. 2022

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Not all eye-tracking methodology and data processing are equal. While the use of eye-tracking is intricate because of its grounding in visual physiology, traditional 2D eye-tracking methods are supported by software, tools, and reference studies. This is not so true for eye-tracking methods applied in virtual reality (imaginary 3D environments). Previous research regarded the domain of eye-tracking in 3D virtual reality as an untamed realm with unaddressed issues. The present paper explores these issues, discusses possible solutions at a theoretical level, and offers example implementations. The paper also proposes a workflow and software architecture that encompasses an entire experimental scenario, including virtual scene preparation and operationalization of visual stimuli, experimental data collection and considerations for ambiguous visual stimuli, post-hoc data correction, data aggregation, and visualization. The paper is accompanied by examples of eye-tracking data collection and evaluation based on ongoing research of indoor evacuation behavior.

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Section: Data Visualization and Related Algorithmsmentioning

confidence: 99%

Eye-Tracking in Interactive Virtual Environments: Implementation and Evaluation

et al. 2022

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“…Backes et al proposed a “complexity” index by employing pixel intensity values of grayscale images and fractal dimension, and proposed a method for image analysis and retrieval [ 8 ]. Wang et al defined the “complexity” of 3D shapes as the difference between outlines of shapes viewed from various angles and proposed an index based on this definition [ 9 , 10 ]. In addition, since Farin argued that curvatures on shapes enables detection of slight changes in shape [ 11 ], researches about “beauty” of curves and automatic generation of beautiful curves employed curvature [ 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Gaussian Curvature Entropy for Curved Surface Shape Generation

Okano

Matsumoto

Katô

2020

Entropy

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The overall shape features that emerge from combinations of shape elements, such as "complexity" and "order", are important in designing shapes of industrial products. However, controlling the features of shapes is difficult and depends on the experience and intuition of designers. Among these features, "complexity" is said to have an influence on the "beauty" and "preference" of shapes. This research proposed a Gaussian curvature entropy as a "complexity" index of a curved surface shape. The proposed index is calculated based on Gaussian curvature, which is obtained by the sampling and quantization of a curved surface shape and validated by the sensory evaluation experiment while using two types of sample shapes. The result indicates the correspondence of the index to perceived "complexity" (the determination coefficient is greater than 0.8). Additionally, this research constructed a shape generation method that was based on the index as a car design supporting apparatus, in which the designers can refer many shapes generated by controlling "complexity". The applicability of the proposed method was confirmed by the experiment while using the generated shapes.

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“…Su [19] used graphic boundaries, global structures, and symmetry to measure shape complexity. Saleem [20] used a similarity matrix of 2D graphics to describe the complexity of 3D graphics. Matsumoto [21] used the absolute curvature to quantify the surface shape complexity of the object.…”

Section: B Shape Complexitymentioning

confidence: 99%

A Data Allocation Strategy for Geocomputation Based on Shape Complexity in A Cloud Environment Using Parallel Overlay Analysis of Polygons as an Example

et al. 2020

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Given the explosive growth of geospatial data, parallel computing technologies have become widely used in the spatial analysis of these massive types of data. The data used in geographic computing often exhibit a complex graphic structure, which is an important cause of data skew in parallel computing. The shape complexity is crucial to the task allocation strategy of parallel computing. The effect of polygon shape features on the performance of spatial analysis was investigated in this study. A quantitative polygonshaped complexity evaluation model was established through regression analysis. The Hilbert data partition strategy weighted by shape complexity was used as a spatial data allocation method for parallel spatial analysis. This study established a shape complexity evaluation model for overlay analysis and used the Spark parallel computing paradigm to carry out a comparative experiment of a massive, complex polygon. Experimental results showed that the spatial data allocation strategy based on the complexity of polygon shape computing effectively solved the problem of data skew in the parallel spatial analysis of massive complex polygons.

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On visual complexity of 3D shapes

Cited by 23 publications

References 9 publications

Eye-Tracking in Interactive Virtual Environments: Implementation and Evaluation

Eye-Tracking in Interactive Virtual Environments: Implementation and Evaluation

Gaussian Curvature Entropy for Curved Surface Shape Generation

A Data Allocation Strategy for Geocomputation Based on Shape Complexity in A Cloud Environment Using Parallel Overlay Analysis of Polygons as an Example

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