Notions of optimal transport theory and how to implement them on a computer

Publication date: Available online 8 February 2018
Source:Computers & Graphics
Author(s): Bruno Lévy, Erica L. Schwindt
This article gives an introduction to optimal transport, a mathematical theory that makes it possible to measure distances between functions (or distances between more general objects), to interpolate between objects or to enforce mass/volume conservation in certain computational physics simulations. Optimal transport is a rich scientific domain, with active research communities, both on its theoretical aspects and on more applicative considerations, such as geometry processing and machine learning. This article aims at explaining the main principles behind the theory of optimal transport, introduce the different involved notions, and more importantly, how they relate, to let the reader grasp an intuition of the elegant theory that structures them. Then we will consider a specific setting, called semi-discrete, where a continuous function is transported to a discrete sum of Dirac masses. Studying this specific setting naturally leads to an efficient computational algorithm, that uses classical notions of computational geometry, such as a generalization of Voronoi diagrams called Laguerre diagrams.

Full article: Notions of optimal transport theory and how to implement them on a computer

Using Real Life Incidents for Realistic Virtual Crowds with Data-Driven Emotion Contagion

Publication date: Available online 15 February 2018
Source:Computers & Graphics
Author(s): Ahmet Eren Başak, Uğur Güdükbay, Funda Durupınar
We propose a data-driven approach for tuning, validating and optimizing crowd simulations by learning parameters from real-life videos. We discuss the common traits of incidents and their video footages suitable for the learning step. We then demonstrate the learning process in three real-life incidents: a bombing attack, a panic situation on the subway and a Black Friday rush. We reanimate the incidents using an existing emotion contagion and crowd simulation framework and optimize the parameters that characterize agent behavior with respect to the data extracted from the video footages of the incidents.

Full article: Using Real Life Incidents for Realistic Virtual Crowds with Data-Driven Emotion Contagion

A comparative review of plausible hole filling strategies in the context of scene depth image completion

Publication date: Available online 15 February 2018
Source:Computers & Graphics
Author(s): Amir Atapour-Abarghouei, Toby P. Breckon
Despite significant research focus on 3D scene capture systems, numerous unresolved challenges remain in relation to achieving full coverage scene depth estimation which is the key part of any modern 3D sensing system. This has created an area of research where the goal is to complete the missing 3D information post capture via a secondary depth filling process. In many downstream applications, an incomplete depth scene is of limited value, requiring many special cases for subsequent utilization, and thus techniques are required to “fill the holes” that exist in terms of both missing depth and color scene information. An analogous problem exists within the scope of scene filling post object removal in the same context. Although considerable research has resulted in notable progress in the synthetic expansion or reconstruction of missing color scene information in both statistical (texture synthesis) and structural (image completion) forms, work on the plausible completion of missing scene depth is contrastingly limited. This survey aims to provide a state of the art overview within this growing field of depth synthesis work whilst noting related solutions in the space of traditional texture synthesis and color image completion for hole filling. To these ends, we concentrate on the plausible completion of both underlying depth structure and relief texture to provide both greater understanding and future development in the area. Our analyses are in part supported by illustrative experimental examples of the comparative use of a subset of representative approaches over common depth completion examples.

Full article: A comparative review of plausible hole filling strategies in the context of scene depth image completion

Exploring visual attention and saliency modeling for task-based visual analysis

Publication date: Available online 7 February 2018
Source:Computers & Graphics
Author(s): Patrik Polatsek, Manuela Waldner, Ivan Viola, Peter Kapec, Wanda Benesova
Memory, visual attention and perception play a critical role in the design of visualizations. The way users observe a visualization is affected by salient stimuli in a scene as well as by domain knowledge, interest, and the task. While recent saliency models manage to predict the users’ visual attention in visualizations during exploratory analysis, there is little evidence how much influence bottom-up saliency has on task-based visual analysis. Therefore, we performed an eye-tracking study with 47 users to determine the users’ path of attention when solving three low-level analytical tasks using 30 different charts from the MASSVIS database [1]. We also compared our task-based eye tracking data to the data from the original memorability experiment by Borkin et al. [2]. We found that solving a task leads to more consistent viewing patterns compared to exploratory visual analysis. However, bottom-up saliency of a visualization has negligible influence on users’ fixations and task efficiency when performing a low-level analytical task. Also, the efficiency of visual search for an extreme target data point is barely influenced by the target’s bottom-up saliency. Therefore, we conclude that bottom-up saliency models tailored towards information visualization are not suitable for predicting visual attention when performing task-based visual analysis. We discuss potential reasons and suggest extensions to visual attention models to better account for task-based visual analysis.

Full article: Exploring visual attention and saliency modeling for task-based visual analysis

KaraKter: An Autonomously Interacting Karate Kumite Character for VR-based Training and Research

Publication date: Available online 10 February 2018
Source:Computers & Graphics
Author(s): Liang Zhang, Guido Brunnett, Katharina Petri, Marco Danneberg, Stefan Masik, Nicole Bandow, Kerstin Witte
We report on the creation of an autonomous Karate kumite character (KaraKter) that can be used for VR based training and research in Karate kumite. For the real time interaction with KaraKter, a human athlete is tracked in a virtual environment. KaraKter moves in Karate specific ways, approaches the athlete and realizes adequate attacks depending on the behavior of the human. KaraKter passed tests on functionality and performance and has been evaluated by high ranking Karate experts. The evaluation showed that the athletes accept KaraKter as an actual opponent. All experts rated the system to be useful in the training of Karate kumite.

Full article: KaraKter: An Autonomously Interacting Karate Kumite Character for VR-based Training and Research

Exploration of Blood Flow Patterns in Cerebral Aneurysms during the Cardiac Cycle

Publication date: Available online 10 February 2018
Source:Computers & Graphics
Author(s): Monique Meuschke, Samuel Voß, Bernhard Preim, Kai Lawonn
This paper presents a method for clustering time-dependent blood flow data, represented by path lines, in cerebral aneurysms using a reliable similarity measure combined with a clustering technique. Such aneurysms bear the risk of rupture, whereas their treatment also carries considerable risks for the patient. Medical researchers emphasize the importance of investigating aberrant blood flow patterns for the patient-specific rupture risk assessment and treatment analysis. Therefore, occurring flow patterns are manually extracted and classified according to predefined criteria. The manual extraction is time-consuming for larger studies and affected by visual clutter, which complicates the subsequent classification of flow patterns. In contrast, our method allows an automatic and reliable clustering of intra-aneurysmal flow patterns that facilitates their classification. We introduce a similarity measure that groups spatio-temporally adjacent flow patterns. We combine our similarity measure with a commonly used clustering technique and applied it to five representative datasets. The clustering results are presented by 2D and 3D visualizations and were qualitatively compared and evaluated by four domain experts. Moreover, we qualitatively evaluated our similarity measure.

Full article: Exploration of Blood Flow Patterns in Cerebral Aneurysms during the Cardiac Cycle

A survey of virtual human anatomy education systems

Publication date: Available online 31 January 2018
Source:Computers & Graphics
Author(s): Bernhard Preim, Patrick Saalfeld
This survey provides an overview of visualization and interaction techniques developed for anatomy education. Besides individual techniques, the integration into virtual anatomy systems is considered. Web-based systems play a crucial role to enable learning independently at any time and space. We consider the educational background, the underlying data, the model generation as well as the incorporation of textual components, such as labels and explanations. Finally, stereoscopic devices and first immersive VR solutions are discussed. The survey comprises also evaluation studies that analyze the learning effectiveness.

Full article: A survey of virtual human anatomy education systems

Fitting Scattered Data Points with Ball B-Spline Curves using Particle Swarm Optimization

Publication date: Available online 2 February 2018
Source:Computers & Graphics
Author(s): Zhongke Wu, Xingce Wang, Yan Fu, Junchen Shen, Qianqian Jiang, Yuanshuai Zhu, Mingquan Zhou
Scattered data fitting has always been a challenging problem in the fields of geometric modeling and computer-aided design. As the skeleton-based three-dimensional solid model representation, the ball B-Spline curve is suitable to fit scattered data points on the surface of a tubular shape. We study the problem of fitting scattered data points with ball B-spline curves (BBSCs) and propose a corresponding fitting algorithm based on the particle swarm optimization (PSO) algorithm. In this process, we encounter three critical and difficult sub-problems: (1) parameterizing data points, (2) determining the knot vector, and (3) calculating the control radii. All of these problems are multidimensional and nonlinear. The parallelism of the PSO algorithm provides high optimization, which is suitable for solving nonlinear, non-differentiable, and multi-modal optimization problems. Therefore, we use it to solve the scattered data fitting problem. The PSO is applied in three steps to solve this problem. First, we determine the parametric values of the data points using PSO. Then, we compute the knot vector based on the parametric values of the data points. Finally, we obtain the radius function. The experiments on the shell surface, crescent surface, and real vessel models verify the accuracy and flexibility of the method. The research can be widely used in computer-aided design, animation, and model analysis.

Full article: Fitting Scattered Data Points with Ball B-Spline Curves using Particle Swarm Optimization