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  • 1.
    Li, Chengjie
    et al.
    KTH.
    Androulakaki, Theofronia
    KTH.
    Gao, Alex Yuan
    Yang, Fangkai
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (CST).
    Saikia, Himangshu
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (CST).
    Peters, Christopher
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (CST).
    Skantze, Gabriel
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Tal, musik och hörsel, TMH.
    Effects of Posture and Embodiment on Social Distance in Human-Agent Interaction in Mixed Reality2018Inngår i: Proceedings of the 18th International Conference on Intelligent Virtual Agents, ACM Digital Library, 2018, s. 191-196Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Mixed reality offers new potentials for social interaction experiences with virtual agents. In addition, it can be used to experiment with the design of physical robots. However, while previous studies have investigated comfortable social distances between humans and artificial agents in real and virtual environments, there is little data with regards to mixed reality environments. In this paper, we conducted an experiment in which participants were asked to walk up to an agent to ask a question, in order to investigate the social distances maintained, as well as the subject's experience of the interaction. We manipulated both the embodiment of the agent (robot vs. human and virtual vs. physical) as well as closed vs. open posture of the agent. The virtual agent was displayed using a mixed reality headset. Our experiment involved 35 participants in a within-subject design. We show that, in the context of social interactions, mixed reality fares well against physical environments, and robots fare well against humans, barring a few technical challenges.

  • 2.
    Saikia, Himangshu
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Comparison and Tracking Methods for Interactive Visualization of Topological Structures in Scalar Fields2017Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Scalar fields occur quite commonly in several application areas in both static and time-dependent forms. Hence a proper visualization of scalar fieldsneeds to be equipped with tools to extract and focus on important features of the data. Similarity detection and pattern search techniques in scalar fields present a useful way of visualizing important features in the data. This is done by isolating these features and visualizing them independently or show all similar patterns that arise from a given search pattern. Topological features are ideal for this purpose of isolating meaningful patterns in the data set and creating intuitive feature descriptors. The Merge Tree is one such topological feature which has characteristics ideally suited for this purpose. Subtrees of merge trees segment the data into hierarchical regions which are topologically defined. This kind of feature-based segmentation is more intelligent than pure data based segmentations involving windows or bounding volumes. In this thesis, we explore several different techniques using subtrees of merge trees as features in scalar field data. Firstly, we begin with a discussion on static scalar fields and devise techniques to compare features - topologically segmented regions given by the subtrees of the merge tree - against each other. Second, we delve into time-dependent scalar fields and extend the idea of feature comparison to spatio-temporal features. In this process, we also come up with a novel approach to track features in time-dependent data considering the entire global network of likely feature associations between consecutive time steps.The highlight of this thesis is the interactivity that is enabled using these feature-based techniques by the real-time computation speed of our algorithms. Our techniques are implemented in an open-source visualization framework Inviwo and are published in several peer-reviewed conferences and journals.

  • 3.
    Saikia, Himangshu
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST). Max Planck Institute for Informatics.
    Seidel, H. -P
    Weinkauf, Tino
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST). Max Planck Institute for Informatics.
    Extended Branch Decomposition Graphs: Structural Comparison of Scalar Data2014Inngår i: Computer Graphics Forum (Proc. EuroVis), ISSN 1467-8659, Vol. 33, nr 3, s. 41-50Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We present a method to find repeating topological structures in scalar data sets. More precisely, we compare all subtrees of two merge trees against each other - in an efficient manner exploiting redundancy. This provides pair-wise distances between the topological structures defined by sub/superlevel sets, which can be exploited in several applications such as finding similar structures in the same data set, assessing periodic behavior in time-dependent data, and comparing the topology of two different data sets. To do so, we introduce a novel data structure called the extended branch decomposition graph, which is composed of the branch decompositions of all subtrees of the merge tree. Based on dynamic programming, we provide two highly efficient algorithms for computing and comparing extended branch decomposition graphs. Several applications attest to the utility of our method and its robustness against noise.

  • 4.
    Saikia, Himangshu
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Seidel, Hans-Peter
    Weinkauf, Tino
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Fast Similarity Search in Scalar Fields using Merging Histograms2015Inngår i: Topological Methods in Data Analysis and Visualization IV: Theory, Algorithms, and Applications, Springer, 2015, s. 121-134Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Similarity estimation in scalar fields using level set topology has attracted a lot of attention in the recent past. Most existing techniques match parts of contour or merge trees against each other by estimating a best overlap between them. Due to their combinatorial nature, these methods can be computationally expensive or prone to instabilities. In this paper, we use an inexpensive feature descriptor to compare subtrees of merge trees against each other. It is the data histogram of the voxels encompassed by a subtree. A small modification of the merge tree computation algorithm allows for obtaining these histograms very efficiently. Furthermore, the descriptor is robust against instabilities in the merge tree. The method is useful in an interactive environment, where a user can search for all structures similar to an interactively selected one. Our method is conservative in the sense that it finds all similar structures, with the rare occurrence of some false positives. We show with several examples the effectiveness, efficiency and accuracy of our method.

  • 5.
    Saikia, Himangshu
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Weinkauf, Tino
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Fast Topology-based Feature Tracking using a Directed Acyclic GraphManuskript (preprint) (Annet vitenskapelig)
  • 6.
    Saikia, Himangshu
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Weinkauf, Tino
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Global Feature Tracking and Similarity Estimation in Time-Dependent Scalar Fields2017Inngår i: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 36, nr 3, s. 1-11Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We present an algorithm for tracking regions in time-dependent scalar fields that uses global knowledge from all time steps for determining the tracks. The regions are defined using merge trees, thereby representing a hierarchical segmentation of the data in each time step. The similarity of regions of two consecutive time steps is measured using their volumetric overlap and a histogram difference. The main ingredient of our method is a directed acyclic graph that records all relevant similarity information as follows: the regions of all time steps are the nodes of the graph, the edges represent possible short feature tracks between consecutive time steps, and the edge weights are given by the similarity of the connected regions. We compute a feature track as the global solution of a shortest path problem in the graph. We use these results to steer the - to the best of our knowledge - first algorithm for spatio-temporal feature similarity estimation. Our algorithm works for 2D and 3D time-dependent scalar fields. We compare our results to previous work, showcase its robustness to noise, and exemplify its utility using several real-world data sets.

  • 7.
    Yang, Fangkai
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (CST).
    Saikia, Himangshu
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (CST).
    Peters, Christopher E.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (CST).
    Who are my neighbors?: A perception model for selecting neighbors of pedestrians in crowds2018Inngår i: Proceedings of the 18th International Conference on Intelligent Virtual Agents, IVA 2018, Association for Computing Machinery (ACM), 2018, s. 269-274Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Pedestrian trajectory prediction is a challenging problem. One of the aspects that makes it so challenging is the fact that the future positions of an agent are not only determined by its previous positions, but also by the interaction of the agent with its neighbors. Previous methods, like Social Attention have considered the interactions with all agents as neighbors. However, this ends up assigning high attention weights to agents who are far away from the queried agent and/or moving in the opposite direction, even though, such agents might have little to no impact on the queried agent’s trajectory. Furthermore, trajectory prediction of a queried agent involving all agents in a large crowded scenario is not efficient. In this paper, we propose a novel approach for selecting neighbors of an agent by modeling its perception as a combination of a location and a locomotion model. We demonstrate the performance of our method by comparing it with the existing state-of-the-art method on publicly available data-sets. The results show that our neighbor selection model overall improves the accuracy of trajectory prediction and enables prediction in scenarios with large numbers of agents in which other methods do not scale well.

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