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Master's Thesis Topics available at DIS

Publication date: 14-10-2018

The Distributed & Interactive Systems group at CWI has new open positions for motivated students who would like to work on their Master’s thesis as an internship in the group. Topics include smart textiles, activity recognition, physiological sensing, virtual reality, point clouds, Internet of things and web technologies. Keep reading for more information about research topics, requirements and contact information.

TextileEmotionSensing: Exploring smart textiles for emotion recognition in mobile interactions

Contact: Abdallah El Ali (abdallah.el.ali@cwi.nl)

Can the clothes we wear or chairs we sit on know how we’re feeling? In this project, you will explore a range of smart textiles (e.g., capacitative pressure sensors) for emotion recognition in mobile interactions. Should the textile be embedded in a couch? Should it be attached to the users? Can we robustly detect affective states such as arousal, valence, joy, anger, etc.? This project will require knowledge and know-how of embedded systems, and use of fabrication techniques for embedding sensors in such fabrics. It will involve running controlled user studies to collect (and later analyze) such biometric data.

Skills:

Required:

  • Embedded systems, signal processing, interest in human computer interaction and fabrication

Recommended:

  • Applied machine learning

Literature:

ThermalEmotions: Exploring thermal cameras for pose-invariant emotion recognition while mobile

Contact: Abdallah El Ali (abdallah.el.ali@cwi.nl)

Thermal cameras have the unique advantage of being able to capture thermal signatures (heat radiation) from energy-emitting entities. Previous work has shown the potential of such cameras for cognitive load estimation, even under high pose variance. In this project, you will explore (using a standard computer vision approach) the potential for mobile (FLIR) (or possibly higher resolution) thermal cameras for pose-invariant emotion recognition while users are mobile. The idea is that the emotional signature on a user’s face allows such recognition, when coupled with standard facial expression features even under computer vision challenging conditions. You will use/collect a mobile dataset thermal face dataset, and explore different types of SOTA deep neural network architectures to perform such supervised emotion classification.

Skills:

Required:

  • Computer vision, machine learning

Recommended:

  • Human computer interaction

Literature:

EmotionalFingerprints: Exploring individual patterns (user profiling) of emotional behavior through physiological sensing

Contact: Abdallah El Ali (abdallah.el.ali@cwi.nl)

Do we all physiologically react to events and experiences the same way? Are there commonalities across age groups or gender differences? In this project the goal is to investigate emotional fingerprints, and tease out to what extent our physiological data (EKG, GSR, EEG, pupil dilation) can be used to bucket users. This data may be combined with behavioral markers such as gait and posture. This will involve running controlled in-lab user studies and user modeling. This can provide the first steps towards an affect-aware recommender system.

Skills:

Required:

  • Embedded systems, applied machine learning, interest in human computer interaction

Literature:

PhysiologicalPrivacy: Quantifying privacy and monetary costs of personal physiologically sensed data

Contact: Abdallah El Ali (abdallah.el.ali@cwi.nl)

The goal of this project is to understand the privacy and monetary costs of physiologically sensed data (e.g. heart rate, GSR, EEG). You will explore different auction-based protocols to model these economic and privacy factors. You will have to run controlled user studies to understand the perceived price users place on such personal data. The outcome is to provide an empirically valid monetary and privacy model of physiological data.

Skills:

Required:

  • Controlled user studies, quantitative economics, statistical methods (linear/logit regression, etc.)

Recommended:

  • Physiological sensors

Literature:

AffectMaps: quantifying the mood of urban city locations with wearable physiological sensing

Contact: Abdallah El Ali (abdallah.el.ali@cwi.nl)

Is there a correlation between user defined tags and physiological markers? What about between the photos they take and the semantics of those photos? In this project, you will investigate how we can physiologically crowdsource urban feelings. This will involve either finding a physiological dataset that is geotagged, or deploying an Android crowdsourcing app to collect such data. This project will be co-supervised by Telefonica Research in Spain, where we may use call detail record (CDR) datasets for Barcelona and/or London.

Skills:

Required:

  • Android programming, knowledge of smartphone physiological sensors, natural language processing (NLP), geographic information systems (GIS)

Recommended:

  • Applied machine learning, human computer interaction

Literature:

EmotionalFashion: Visualizing (emotional) biometric data for smart fashion

Contact: Abdallah El Ali (abdallah.el.ali@cwi.nl)

Wearable biotech fashion is becoming a recent trend, however we still know very little on what the best means of visualizing such biometric data. How and when should a necklace visualize a user’s heartbeat? Should a person’s clothe indicate their body heat emission? How should such on-body wearable sensors look like, and should they actuate in the same or a different place? This is a project to explore the intersection between fashion, aesthetics, and wearable biotech sensors. The project should result in a series of smart wearable fashion prototypes, and evaluated in the field.

Skills:

Required:

  • Embedded systems, human computer interaction, fabrication, multimodal output.

Literature:

Objective metrics for point cloud quality assessment

Contact: Pablo Cesar (p.s.cesar@cwi.nl)

Volumetric data captured by state of the art capture devices, in its most primitive form, consists of a collection of points called a point cloud (PC). A point cloud consists of a set of individual 3D points. Each point, in addition to having a 3D (x, y, z) position, i.e., spatial attribute, may also contain a number of other attributes such as color, reflectance, surface normal, etc. There are no spatial connections or ordering relations specified among the individual points. When a PC signal is processed, for example undergoing lossy compression to reduce its size, it is critical to be able to quantify how well the processed signal is approximating the original one, as in the perception of the end user, which is the human being who will visualise the signal. The goal of this project is to develop a new algorithm (i.e. objective full-reference quality metric)  to evaluate the perceptual fidelity of a processed PC with respect to its original version. A framework implementing the objective metrics currently available in literature to assess PC visual quality, and comparing the performance to the proposed method will also be developed. Subjective feedbacks on the visual quality of the signals will be collected from users to serve as ground-truth.

Skills:

Required:

  • Good (Matlab, Python, or C++) programming skills

Literature:

  • E. Torlig; E. Alexiou; T. Fonseca; R. de Queiroz; T. Ebrahimi, A novel methodology for quality assessment of voxelized point clouds”, 2018. SPIE Optical Engineering + Applications
  • E. Alexiou; T. Ebrahimi, “Benchmarking of objective quality metrics for colorless point clouds”, 2018 Picture Coding Symposium

User navigation in 6DoF Virtual Reality

Contact: Pablo Cesar (p.s.cesar@cwi.nl)

Nowadays, Virtual Reality (VR) applications are typically designed to provide an immersive experience with three Degrees of Freedom (3DoF): a user who watches a 360-degree video on a Head-Mounted Display (HMD) can choose the portion of the spherical content to view, by rotating the head to a specific direction. Nevertheless, the feeling of immersion in VR results not only from the possibility to turn the head and change the viewing direction but also from changing the viewpoint, moving within the virtual scene. VR applications allowing translations inside the virtual scene are referred to as six Degrees of Freedom (6DoF) applications.

The goal of this project is the development of a platform to capture user’s navigation patterns in 6DoF VR. First, an interface to capture user’s position in the virtual space will be implemented in Unity3D for a HMD equipped with controllers and eventually special sensors for positional tracking. Second, a user study to collect the navigation patterns of actual users in a virtual environment, such a 3D scene model, will be designed and performed. Third, the data will be analysed to explore correlation between different user’s navigation behaviour.

Skills:

  • Unity programming

Literature:

  • X Corbillon, F De Simone, G Simon, P. Frossard, “Dynamic Adaptive Streaming for Multi-Viewpoint Omnidirectional Videos”, Proceedings of the 9th ACM on Multimedia Systems Conference ACM MMsys 2018
  • A. L. Simeone et al., “Altering User Movement Behaviour in Virtual Environments”, IEEE Transactions on Visualization and Computer Graphics 2017
  • http://antilatency.com  

Studying the impact of audio cues on Focus of Attention in 3DoF VR

Contact: Pablo Cesar (p.s.cesar@cwi.nl)

Omnidirectional, i.e., 360-degree videos, are spherical signals used in Virtual Reality (VR) applications: a user who watches a 360-degree video on a Head-Mounted Display (HMD) can choose which portion of spherical content to display by moving the head to a specific direction. This is referred to as three Degrees of Freedom (3DoF) navigation. The portion of spherical surface attended by the user is projected to a segment of plane, called viewport. Recently, many studies have appeared proposing datasets of user’s head movements during 360-degree video consumption to analyse how users explore immersive virtual environments.

Understanding VR content navigation is crucial for many applications, such as designing VR content, developing new compression algorithms, or learning computational models of saliency or visual attention. Nevertheless, most of the existing dataset are considering video content without an audio channel. The goal of this project is the creation of a dataset of head movements of users watching 360-degree videos, with or without an audio channel, on a Head-Mounted Display (HMD). First, a user experiment to collect such data will be designed and performed. Second, the collected data will be analysed by means of statistical tools to quantify the impact of audio cues, which might drive the user’s visual Focus of Attention.

Skills:

  • Unity programming

Literature:

  • X Corbillon, F De Simone, G Simon, “360-degree video head movement dataset”, Proceedings of the 8th ACM on Multimedia Systems Conference ACM MMsys 2017
  • V. Sitzmann et al., “Saliency in VR: How do people explore virtual environments?”, in IEEE Transactions on Visualization and Computer Graphics 2018
  • Alia Sheikh, Andy Brown, Zillah Watson, and Michael Evans, “Directing attention in 360-degree video” IBC 2016
  • http://dash.ipv6.enstb.fr/headMovements/

Comparing the performance of mesh versus point cloud-based compression

Contact: Pablo Cesar (p.s.cesar@cwi.nl)

Recent advances in 3D capturing technologies enable the generation of dynamic and static volumetric visual signals from real-world scenes and objects, opening the way to a huge number of applications using this data, from robotics to immersive communications. Volumetric signals are typically represented as polygon meshes or point clouds and can be visualized from any viewpoint, providing six Degrees of Freedom (6DoF) viewing capabilities. They represent a key enabling technology for Augmented and Virtual Reality (AR and VR) applications, which are receiving a lot of attention from main technological innovation players, both in academic and industrial communities. Volumetric signals are extremely high rate, thus require efficient compression algorithms able to remove the visual redundancy in the data while preserving the perceptual quality of the processed visual signal. Existing compression technologies for mesh-based signals include open source libraries such a Draco (https://github.com/google/draco). Compression of point clouds signals is currently under standardisation. The goal of this project is the development of a platform to compare the performance of mesh versus point cloud based compression algorithms in terms of visual quality of the resulting compressed volumetric object. Starting from a set of high quality point cloud (or mesh) volumetric objects, the corresponding mesh (or point cloud) representations of the same objects are extracted. Each representation is then compressed using a point cloud/mesh-based codec, and the resulting compressed signals are evaluated in terms of objective and subjective quality.

Skills:

  • Good programming skills
  • Computer graphic basics

Literature:

  • Kyriaki Christaki, et al, “Subjective Visual Quality Assessment of Immersive 3D Media Compressed by Open-Source Static 3D Mesh Codecs”, preprint, 2018
  • Gauthier Lafruit et al., “MPEG-I coding performance in immersive VR/AR applications”, IBC 2018
  • M. Berger et al., “A Survey of Surface Reconstruction from Point Clouds”, Computer Graphics Forum 2016
  • https://github.com/google/draco

Human perception of volumes

Contact: Pablo Cesar (p.s.cesar@cwi.nl)

Recent advances in 3D capturing technologies enable the generation of dynamic and static volumetric visual signals from real-world scenes and objects, opening the way to a huge number of applications using this data, from robotics to immersive communications. Volumetric signals are typically represented as polygon meshes or point clouds and can be visualized from any viewpoint, providing six Degrees of Freedom (6DoF) viewing capabilities. They represent a key enabling technology for Augmented and Virtual Reality (AR and VR) applications, which are receiving a lot of attention from main technological innovation players, both in academic and industrial communities. The goal of this project is to design and perform a set of psychovisual experiments, using VR technology and visualisation via a Head Mounted Display (HMD), in which the impact on human perception of different properties of the volumetric signal representation via point clouds or meshes, such as the convexity and concavity of a surface, the resolution, the illumination and color, are analysed. First, a review of the state of the art on the perception of volumetric objects will be performed, second a set of open research questions will be chosen and a set of experiments will be designed and performed, and the collected data will be analysed in order to answer the research question.

Skills:

  • Unity programming
  • Interest in human perception analysis

Literature:

  • C. J. Wilson and A. Soranzo, “The Use of Virtual Reality in Psychology: A Case Study in Visual Perception”, Computational and Mathematical Methods in Medicine, 2015
  • J. Zhang et al., “A subjective quality evaluation for 3d point cloud models”, in Proc. of the 2014 International Conference on Audio, Language and Image Processing
  • J. Thorn et al., “Assessing 3d scan quality through paired-comparisons psychophysics test”, in Proc. of the 2016 ACM Conference on Multimedia

Reconstructing high frame rate point clouds of human bodies

Contact: Pablo Cesar (p.s.cesar@cwi.nl)

Volumetric sensing, based on range-sensing technology, allows to capture the depth of on object or an entire scene, in addition to its color information. A format that has recently become widespread to represent volumetric signals captured by  such sensors, such as the Intel Real Sense camera, is the point cloud. A point cloud is a set of individual points in the 3D space, each associated with attributes, such as a color triplet. With respect to other volumetric representations, such as polygon meshes, the point cloud content generation implies much less computational processing, thus it is suitable for live capture and transmission. The goal of this project is the development of a machine learning-based approach to interpolate the point clouds representing a human body captured at different instants in time, in order to increase the frame rate of dynamic point cloud capturing a user’s body. The core of the project will be on the design of the network. The second main goal will be the collection of training data produced by using a capture set-up made of multiple Intel Real Sense sensors, in order to train the neural network aiming at learning how the point cloud signal representing body movement evolves in time.

Skills:

  • Machine learning
  • Good programming skills

Literature:

Impartial Data

Contact: Steven Pemberton (steven.pemberton@cwi.nl)

Pilot implementation of a generic data platform.

To create a pilot implementation of a generic platform that reads data in any (parsable) form for generic processing. Program will be used as a showcase for the technique, and will be possibly embedded in other platforms. May eventually lead to a W3C standard.

Skills:

  • Programming in Python and/or C
  • Some knowledge of grammars would be advantageous.

CSS Skinning

Contact: Steven Pemberton (steven.pemberton@cwi.nl)

XForms is a W3C standard [1], originally designed for forms on the web, but in later versions generalised to support more general applications [e.g. 2, 3]. This project will create a CSS suite to allow the ‘skinning’ of XForms: parameterised for colour and other elements, in the manner of Bootstrap [4] and Bulma [5] mobile aware, so that the same form would be usable equally well on a desktop as a phone.

Skills:

  • Understanding of markup and CSS

Literature:

XForms Test Suite

Contact: Steven Pemberton (steven.pemberton@cwi.nl)

XForms is a W3C standard [1], originally designed for forms on the web, but in later versions generalised to support more general applications [e.g. 2, 3]. This project will create a test infrastructure to test implementations of the latest version of XForms. Parameterised using XForms for easy updating Generating an overview of test results.

Skills:

  • HTML
  • XML would be a plus.

Literature:

Internet of Things (IoT) programming notation

Contact: Steven Pemberton (steven.pemberton@cwi.nl)

Development of a programming notation for a new interactive IoT platform. A declarative interface technique is used to access and control the devices without having to worry about the variety of device-interfaces. This project will analyse programming notations for other similar projects, such as IFTTT, and design a declarative programming notation based on state changes, rather than using events, or traditional programming.

Igor is an IoT platform using new techniques for managing IoT devices by placing a thin layer around a non-homogeneous collection of Internet of Things devices, hiding the data-format and data-access differences, and auto-updating the devices as needed.

Skills:

  • Knowledge of Python would be advantageous.

Literature: