1. Project overview

   The goal of DENSITY is to develop a new methodology for 3D reconstruction suitable to inexperienced end-users. The basic idea relies on the observation that it is difficult to control the user environment and camera settings, and that certain knowledge is needed in order to take “appropriate” pictures. Our easy-to-use, cost-effective scanning solution which is based on such a sensor could make 3D scanning technology more accessible to everyday users. Instead of guiding the user in trying to reduce the number of necessary pictures to a minimum, which leads in practice to very unreliable results, we propose to make use of depth images (RGB-D images) available from using low-cost depth cameras (Kinect or Time-of-Flight sensor). The depth images of those low cost depth cameras are currently of low resolution and noisy, but provide relatively stable results and a good coverage of the perceived area. However, those partial and coarse 3D views still need to be registered and refined in order to be useful in our scanning task.

2. KinectAvatar: Fully Automatic Body Capture Using a Sigle Kinect

   We present a novel scanning system for capturing a full 3D human body model using just a single depth camera and no auxiliary equipment. We claim that data captured from a single Kinect is sufficient to produce a good quality full 3D human model. In this setting, the challenges we face are the sensor’s low resolution with random noise and the subject’s non-rigid movement when capturing the data. To overcome these challenges, we develop an improved super-resolution algorithm that takes color constraints into account. We then align the super-resolved scans using a combination of automatic rigid and nonrigid registration. As the system is of low price and obtains impressive results in several minutes, full 3D human body scanning technology can now become more accessible to everyday users at home. First, we give an overview of our scanning system, easily built as shown in Fig. 1a. The user stands before a Kinect in a range of about 2 meters, such that the full body falls in the Kinect’s range of view. Then, the user simply turns around 360 degrees for about 20 to 30 seconds while maintaining an approximate “T” pose. Fig. 2 shows our scanning results on five users. Our result reproduces the whole human structure well (especially the arms and legs), and can reconstruct detailed geometry such as the face structure and the wrinkles of the clothes. To evaluate the accuracy of the reconstruction, we compare the biometric measurements of the reconstructed human models with data from the actual people in Tab. 1. The values are the average absolute distance among eight people.

We also show average runtime statistics in Tab. 1. The whole processing time for each model is about 14 minutes on average, using an Intel(R) Xeon 2.67GHz CPU with 12GB of memory. Note that 90% of the time in our method is used for computing closest points. Previous work on human body reconstruction can only capture nearly naked human bodies and spends nearly one hour of computation time, and prior work on articulated registration computes the registration frame by frame inK minimization steps, taking nearly two hours to compute.

1. Body Capture Using Multiple Kinects

   In this application, we present a scanning system with muliple Kinects. The Kinects are fixed on the pillars from different views, as Fig. 3a shows. We delete the interference each other of the Kinects by shaking the kinects. And the first scanning result is shown in Fig. 3b. Because the there is only three views, not enough to cover all the human shape, there are some holes on the final mesh. We fill the holes based on a human template . Deform and fit the template human shape to the scanning human, then merge and smooth both mesh together to get a final result.

2. Virtual Clothes Try-on

   There are many applications in virtual reality task with 3d full body model. Online shopping has grown exponentially during the past decade. More andmore customers turn to purchase dresses online. However, the customer can not try out the garment before purchasing, do not know the size of the clothes is suitable or not. With our easy-to-use and low cost Kinect scanning system, the untrained customer can get his own 3D model at home. Then virtual try on clothes with human model, even can interactively edit 2D pattern designs.

Die 3D-Koordinatenmesstechnik dient in der Automobilindustrie zur Beurteilung der Merkmale eines Bauteils oder einer Baugruppe und ist damit eines der wichtigsten Hilfsmittel der Qualitätssicherung. Die effiziente Planung der Messabläufe und deren Integration in den betrieblichen Produktionsablauf sind dabei wichtige Aspekte. In diesem Zusammenhang bietet eine durchgängige Prozesskette, ausgehend von der Messplanung, über die Erzeugung und Simulation des Messprogramms bis hin zur Analyse der Messergebnisse in der Produktion erhebliches Potential, um den Messablauf zu optimieren.

Das Ziel des Projektes iMP – Intelligente Messplanung ist die weitgehende Automatisierung der Messplanung im Rahmen der Qualitätsprüfung von Bauteilen bzw. Baugruppen in der Automobilindustrie. Zu diesem Zweck sollen systemunabhängige Softwaremodule entwickelt werden, die die Erstellung des Messplans weitestgehend automatisieren, so dass der Messingenieur den erzeugten Messplan lediglich überprüfen und ggf. interaktiv überarbeiten muss. Alle Softwaremodule werden in eine einheitliche Benutzeroberfläche integriert, um damit die Voraussetzung für den Einsatz in der produktionsbegleitenden Qualitätsprüfung zu schaffen.

Insgesamt wird durch die automatisierte Messplanung eine Verkürzung der Planungszeiten sowie das Senken von Entwicklungs- und Herstellungskosten erreicht. Anwender der Software sind in erste Linie die Automobilindustrie und deren Zulieferer. Eine Übertragung der Ergebnisse auf andere Industriezweige, wie beispielsweise die Flugzeugindustrie, wird angestrebt.

Improper strain on the musculoskeletal system, repetitive movements or an ergonomically uncomfortable posture lead to complaints from many employees. In particular, older employees often suffer from disorders of the musculoskeletal system or other age-related limitations due to their long-term activities. In the BIONIC project funded by the European Union (EU), the DFKI works as coordinator together with ten international partners on intelligent solutions to reduce such health problems.

Body Sensor Network (BSN) analyzes loads and corrects malpositions in real time

A network of different sensors worn on the body is used to develop a system that records the state of health of workers during the course of the day. The analysis takes place on an intelligent chip on the body; pre-processing the raw data directly at the “source” enables local data analysis in real time. Novel methods of risk analysis allow direct feedback on stresses and malpositions. Playful applications and a training app motivate the user to counteract one-sided stress and provide personalized and medical support for training at home.

Further development of applications from the predecessor project “EASY-IMP”

A large number of the partners have already successfully worked on the development of a BSN in the EU project EASY-IMP, which used small IMU sensors (acceleration or rotation rate sensors) attached to clothing or skin for the analysis of body movements. The lightweight and modular design of the system will be further developed in BIONIC to facilitate integration.

Biomechanical Models and Deep Learning for Ergonomic Risk Assessment

By using biomechanical models for age-related and chronic impairments, we design algorithms for ergonomic risk assessment of physical stress. The input parameters include posture, forces and ?moments?, as well as physiological parameters such as heart rate or body temperature.

Procedures based on objective and subjective data (expert criteria) are used as a basis and supplemented by personalised algorithms for which deep learning methods are used. The data generated is stored in accordance with the EU Data Protection Directive.

“BIONIC – Personalized Body Sensor Networks with Built-In Intelligence for Real-Time Risk Assessment and Coaching of Ageing workers, in all types of working and living environments” is an interdisciplinary research project with eleven partners from medicine, biotechnology, electronics, information technology and artificial intelligence to construction and factory workers who will validate the results in pilot trials.

Partners

• Deutsches Forschungszentrum für Künstliche Intelligenz GmbH DFKI (Projektkoordination)
• Technische Universität Kaiserslautern – wearHEALTH Group
• Instituto de Biomechanica de Valencia, Spanien
• Roessingh Research and Development, University of Twente, Niederlande
• University of Piraeus – Systems Security Lab, Griechenland
• Interactive Wear GmbH, München
• Hypercliq IKE, Griechenland
• ACCIONA Construcción S.A. – Spanien
• Rolls-Royce Power Systems AG – Friedrichshafen
• Bundesanstalt für Arbeitsschutz und Arbeitsmedizin (BAuA), Dortmund
• Fundación Laboral de la Construcción – Spanien

The information gap between virtual product and manufacturing engineering and the physical start of production is a fundamental problem for European manufacturers. Knowledge about products and processes, which is currently distributed over heterogeneous systems, is rich of information, but a platform for presenting this knowledge according to the different user roles (e.g., production planners or shop floor people) is missing. Enterprise data must be captured, updated, enriched and transferred into an interoperable platform, which enables cross-disciplinary knowledge sharing throughout the product life-cycle.

Reuse of product and process data is a promising approach to leverage virtual simulation of manual manufacturing processes. Up to now, the complexity and incompatibility of digital data are main reasons why planning and training of manual manufacturing processes, e.g. in automotive and aerospace, are still carried out in physical stages or during the ramp-up. The simulation and training of complex manufacturing processes in physical stages are expensive and often ineffective. In order to reduce the need for physical prototypes and to reduce time-to-market, virtual training must overcome the problems of former approaches, e.g., inadequate authoring times, cost-prohibitive hardware and insufficient user integration. VISTRA aims at the development of a comprehensive platform for simulation, documentation and training of manual assembly processes based on advanced ICT-technologies and concepts, such as auto-generation, realistic physical behaviour, game-based learning, advanced user-interaction, low-cost hardware and cross-disciplinary information sharing.

The overall aim of the VISTRA project is to minimize the number of physical prototypes in the field of industrial production by more efficient and realistic simulation techniques on the basis of realistic and user-centred simulation methods, and to enable that gained knowledge will be (re-)used for efficient knowledge sharing between different stakeholders. The overall aim is divided into the following strategic objectives:

1. Data Mining and Harmonization from Product and Manufacturing Enterprise Data
2. Acquisation and Representation of Cross-disciplinary Knowledge
3. Interactive Environments with High User Acceptance
4. Physical behaviour and Optimized Real-time Simulation of Deformables in Virtual Training
5. Reuse of Knowledge and Cross-disciplinary Knowledge Sharing
6. Applicability in Real Industrial Environments
7. Interoperability and Standardization

Partners

• DFKI (Germany)
• Fraunhofer IGD (Germany)
• Fraunhofer-Chalmers Centre (Sweden)
• University of Nottingham (United Kingdom)
• Serious Games Interactive (Denmark)
• VOLVO Technology Corporation
• Adam Opel AG

The objective of EPOS is to reduce the efforts for personal knowledge management and to evolve personal to organizational knowledge spaces. EPOS addresses the following topics:

• workspace modelling, techniques for user observation and context identification
• identification of information needs, query modification for collaborative information retrieval
• communication structures in interacting knowledge workspaces
• leveraging individual native information structures to models and ontologies, evidence calculation for ontology creation
• communication and negotiation protocols on the meta-level
• ontology-based visualization components, configuration rules for task-specific information-visualization.

EPOS Motivation

Knowledge workers put a lot of effort during every day’s work in structuring their own information. This is done with the help of email tools, file directories, or bookmarks and the way this is done depends highly on the individual manner of working. Contrary to that are the knowledge management goals of a company: global collection, structuring and distribution of knowledge. Thus, there is a discrepancy between the global benefit for the organization and the personal benefit for the individual knowledge worker.

While the organization asks for universally applicable and standardized persistent structures, processes, and work organizations to achieve and maintain universally accessible information archives, the individual knowledge worker requests individualized structures and flexibility in processes and work organization in order to reach optimal support for the individual activities.

Therefore, EPOS investigates a bottom-up evolutionary approach to resolve this discrepancy. The individual knowledge workspace, realized as a set of agents in the knowledge workers’ personal computer, will provide adequate and task-specific supporting information to the human. In parallel, the system will observe the work and the users’ ways of information access/ handling and automatically learn about intentions, structures, ontologies, and work processes. Towards the user, the knowledge workspace thus acts as an adaptive information assistant. In order to present this information that has been gathered behind the scenes advanced Information Visualization techniques are necessary.

The individual face of the knowledge workspace is complemented by globally oriented sharing and exchange facilities. Interacting agents from different workspaces synchronize information needs, balance structures, ontologies, and process models, and exchange context-specific relevant information. A society of agents, represented by the collection of individual knowledge workspaces, will thus reach a common and shared understanding of the structured information and knowledge used in their realm, and finally, contribute to the organizational memory.

EPOS Vision (slides in english, pdf) (Kurzversion in deutsch, pdf)

EPOS and gnowsis in the DFKI Newsletter

• in 15/2005 (page 10: English , German/deutsch)
• in 17/2006 (page 5: English , German/deutsch)

EPOS succeeds the FRODO (Framework for Distributed Organization Memories) project. In FRODO several assumptions, methods, and techniques have already been tackled.

The works towards the Semantic Desktop lead to the EU IP-Project Nepomuk

Successor projects

EPOS finished in 2005, but research as continued in these projects:

• EU Integrated Project Nepomuk
• MyMory
• ADIB – Adaptive Information Delivery
• lots of components are published as open source at OpenDFKI
• a DFKI spin-off the gnowsis.com
• We were part of the EU IP ForgetIT contributing with our new Semantic Desktop to Managed Forgetting, Contextualized Remembering and Synergetic Preservation (Find videos and more detailed explanations of our newest Semantic Desktop at the Personal Preservation Pilot webpage)
• supSpaces the Semantic Desktop Server is the back-end of supSpaces
• (2019) Our work culminated in our DFKI CoMem at comem.ai

In the following we present more details of the EPOS project.

EPOS Research lines

Personal Information Models & Ontologies

Short description of the destination of this part of the EPOS project:

• With e-mail and file folders, bookmark hierarchies etc., knowledge workers already use and continuously maintain a lot of structures that reflect parts their knowledge (e.g., about people and projects), their domain of interest, or world view.
• These structures can be seen as valuable input for organizational knowledge management, but also as a means to personalize information delivery to the user (e.g., by mapping search results onto the specific user’s structure).
• However, the flexibility that the current mode of interaction with these structures offers, entails also serious drawbacks for the exploitation for automated information services: Parts of the structures are redundant or contradictory, the elements don’t have a clear semantics, etc.
• Goal of this project line is to integrate the various native structures of one knowledge workspace into one Personal Information Model (PIM) that on the one hand reflects the personal structures, but on the other hand has a clear formal semantics. These PIM can than be used for knowledge exchange between workspaces and for communication with an Organizational Memory.

  Research Topics:

• Specify the Personal Information Model
  • Analysis of exploitable “native structures” and their intended semantics
  • Definition of a representation framework that is sufficient to reflect native structures
  • Techniques for leveraging native structures to the PIM
  • Implementation of the PIM and a communication interface to query and manipulate it
• Framework for Inter-PIM communication
  • Mappings between several Personal Information Models
  • Re-use of fragments from external models
• Integration of Personal Information Models with (organizational) ontologies

  The work started in the EPOS project resulted and influenced the work done in the Nepomuk project and there the PIMO (Personal Information Model Ontology) and finally also our work in CoMem and SensAI

Context Elicitation by User Observation in EPOS

The goal is to estimate a user’s context automatically and, hence, without disturbing and distracting him. When we talk about context we are focusing on contextual information relevant for (personal) knowledge management aspects. This means, we are not sensoring the room temperature or the user’s heart beat frequency.

We are mainly observing the user’s interactions with his PC. (Personal) Knowledge Management work is focussing on text work. Therefore, we are observing text-oriented actions in particular. For example, writing or replying to emails, or browsing the web, etc.

The user observation is done by a plugins an the user observation framework. The observed user actions are taken as evidences.

Short description of the destination of this part of the EPOS project:

• Human uses the computer to help him do his work.
• Usage of computer is done in a nowadays typical way, i.e., a graphical user interface providing access to applications running on the user’s machine.
• Applications, tools, operating system components, etc. allow for interactions with objects on the machine and in the world (documents, e-mails, etc.).
• Such objects can be created, stored, viewed, edited, etc.
• We observe the user doing his work in order to get a clue about his intentions and goals.
• The user’s intentions, goals, application usage are part of the context he is in.
• This context will be used to support the human’s work with context-sensitive, pro-active knowledge assistance which will make his work easier. Think of an intelligent assistant helping you with
  • minor working steps
  • remembering you of important appointments
  • showing you related information
  • example of our assistant bar (screenshot )

    Research Topics:

• Specify a generic Workspace Model (objects, operations / operators on these objects)
• Instantiate and observe the / all / some applications and operating systems components (web browser, e-mail client, Open Office, text editor, file explorer, notes application, proFiler/brainfiler (multi-criterial document archive and classification))
• Specify different levels of user activity (4 levels of user activity (image))
• Mapping observed workspace events to user actions (user intentions, i.e., short-term goals) -> plan recognition
• Mapping user actions to task concepts (user goals, i.e., mid-term goals): goal elicitation, task concept ontology (hierarchical task ontology)
• Mapping task concepts to workflow working steps (user is embedded in / assigned to workflows -> long-term goals); process / task identification; semantic description of workflows / tasks
• Create a context elicitation framework (context elicitation architecture (image))

  The results are part of Sven Schwarz’ PhD “Schwarz S (2010), “Context-Awareness and Context-Sensitive Interfaces for Knowledge Work, 2010. Disertation, Kaiserslautern University, Verlag Dr. Hut.

InfoVis

The information available within one user’s personal knowledge space consists of highly complex structures. These may be personal information models underlying the identified processes the user is performing or simply large amounts of pieces of information as a result of previously expressed information needs. This complexity even increases when we consider the set of the users’ personal knowledge spaces that together form the organizational memory. Intelligent visualization techniques help the users to cope with that complexity.

When we have a closer look at these knowledge spaces, we identify four domains:

• the document spaces,
• the personal and organizational information models,
• the processes,
• the users.

  Each domain is usually populated by a set of entities that are implicitly or explicitly linked to other entities within the same or within other domains: Each of the users within the organization has got one or more document spaces to store his information items. For structuring his knowledge he relies on his personal information model. Additionally, on the level of the organizational memory, there is one global model, the organization’s ontology. Besides that many process models describe different tasks of the user’s daily work.

  In this domain model we find three dimensions to consider for visualization:

• each single entity in a domain (which can be a multivariate space itself),
• a set of entities in one domain (e.g., all processes a user is involved in),
• the inter-domain views (e.g., the connection of process steps with knowledge from the organizational memory).

  While there exist many research results that address problems from the first and second dimension, there is still a demand for research in dimension three. Especially, here we get domain-spanning information of very different kinds: This may on one hand be arbitrary structural information linking objects from two or more domains or on the other hand be a large set of query results from one domain.

  During the EPOS project we want to address both of these problem-classes by generalizing existing techniques within the Information Visualization domain and automatically selecting the appropriate metaphors from a given set and applying them to the given problem.

Peer-to-Peer Information Retrieval

Precise satisfaction of information needs in interacting knowledge workspaces

• Improve user satisfaction by collaborative information retrieval
  • exploit query experience resulting from previous queries together with user feedback on the query results
  • associate query experience with the appropriate context and model elements
  • re-use query experience to improve new user queries in a way that the results will better fit to the current context and the assumed user intentions
• Tap personal knowledge workspaces as an organizational memory resource
  • communicate information needs across workspaces
  • integrate answers from different workspaces to satisfy the query

  Reuse query experience in order to improve search in collaborative workspaces

• Relevance feedback allows to save positive examples of query results as reusable experience
  • Learn about successful query-result-concepts based on term occurrence
• Retrieved information objects are associated with the personal information models and the retrieval context. This association is transferred to the corresponding query experiences
• Current information needs are better satisfied by using stored query experiences for query reformulation
• The exchange of stored query experiences between individual workspaces helps to identify and build thematic communities
  • other users in similar situations profit from previous experience
  • particular queries are routed to appropriate partner workspaces to retrieve information across workspace boundaries

  Research Topics:

• Peer-To-Peer IR (P2P-IR)
  • How to identify the peers to inform/query ?
  • How to merge search results from several peers?
• Quality estimation
  • How relevant are the results from other peers?
  • What makes a peer a specialist/expert/layman/amateur?
• Conceptional Level Query Manager, Query Recommender
• Detail Level (detailed information is presented here and here and here )

Related Projects and Groups

Specter

Our partner DFKI-project Specter dealing with context- and affect-aware personal assistance in instrumented environments, DFKI Saarbrücken

EDAMOK

Enabling distributed and autonomous management of knowledge; ITC irst in Trento, Italy

SWAP

EU-project in the IST-programme considered with using Semantic Web and P2P technologies for knowledge management. Bibster is a system that builds on SWAP technology and assists researchers in managing, searching, and sharing bibliographic data in a peer-to-peer network.

AWAKE

Knowledge Sharing in Heterogeneous Expert Communities. The goal is to develop an experimental, agent-based platform, making it possible to explore and to compare different methods for knowledge sharing and information search in heterogeneous expert communities. Awake focuses on capturing and visualizing implicit knowledge. Michael Wurst visited the EPOS team.

Gnowsis

The goal is to develop a semantic desktop based on Semantic Web technology. Leo Sauermann joined our project team in July 04. – and after some years now made a spin-off gnowsis.com in 2009

Haystack

Haystack also focuses on the individual information handling perspective with Semantic Web technology.

NEPOMUK

NEPOMUK brings together researchers, industrial software developers, and representative industrial users, to develop a comprehensive solution for extending the personal desktop into a collaboration environment which supports both the personal information management and the sharing and exchange across social and organizational relations. The work done in EPOS heavily influenced the project, e.g., the PIMO

Members of the EPOS project

(here’s a team picture from a long time ago)

• Heiko Maus (project lead)
• Ludger van Elst
• Sven Schwarz
• Leo Sauermann
• Jan-Thies Heinrich (formerly Bähr)
• Andreas Lauer

  Associated Researchers

• Ansgar Bernardi
• Peter Dannenmann
• Rolf-Hendrik van Lengen
• Michael Sintek
• Malte Kiesel
• Barbara Spillmann (then Uni Bern)
• Armin Hust

  Students

  (long long time ago…thanks to you all!)

• Björn Endres
• Stefan Weisenberger
• Malte Kiesel (then staff 🙂
• Markus Reinhardt
• Robinson Aschoff, University of Heidelberg
• Frank Osterfeld
• Panitini Madhu Satyanarayana
• Pascal Arweiler, FH Birkenfeld
• Michael Breidel, FH Birkenfeld
• Emmanuel Gasne, ENST Bretagne, France
• Björn Endres
• Jordi Hernandez
• Nagaseshagiri Poola
• Christian Schütz

Tools

Tools of the EPOS project. (notice: not all tools are available anymore):

RDF tools

see FRODO Tools section or RDF2Java

FRODO TaskMan

Workflow management assistant tool for weakly structured processes with the concept of weakly structured workflows or agile knowledge workflows (our new term for this).

Semantic Desktop gnowsis

gnowsis is a personal semantic web desktop server – Semantic Desktop for short. Like a local webserver, that can be seen only by you and that contains your own files, emails, friends and photos. This semantic desktop will interact with the Personal Information Model (PIM)

Nepomuk Semantic Desktop

The follow-up of the gnowsis Semantic Desktop

User Context Framework

Hosts the platform for researchers and practitioners about user context. In our view, user context is the things that influence knowledge work. Through user observation a context-sensitive application can detect the user’s current work context and support the user with suggestions and other services…

Mozilla Plugins for UserObservation

The enhanced Mozilla extensions are available as DragonTalk, an open-source project.

Nabu

Nabu is a plugin for Jive Messenger, a server implementation of the Jabber Instant Messaging protocol. It provides server-side logging of chat conversations and related events. The logged data is stored in a semantic graph, using the RDF W3C standard.

Jatke

JATKE is a Protégé plug-in that provides a unified platform for ontology learning. It is capable of employing and combining arbitrary modules, and thus provides a custom setup for every scenario. JATKE features three different module abstraction levels (Information/Source, Evidence, Proposal), hence facilitating easy reuse and combination of existing modules.

Dragontalk

The Dragontalk project provides a set of extensions for the Mozilla products Thunderbird (email client) and Firefox (web browser). One of the goals of the project is to get information about the user’s operations. For instance, if the user sends or replies to an email, Dragontalk sends a SendEmail or a ReplyToEmail event to some port where an XMLRPC listener can catch it. This is used to elicit the user’s context automatically.

rdfhomepage

RDFHomepage creates personal homepages based on your RDF data. Never more duplicate the information in your bibtex, FOAF file, etc. by manually coding ugly and complicated HTML by hand! Let rdfhomepage create it for you! (last one ctive is the one of Heiko Maus

Aperture

grew out of the Gnowsis Semantic Desktop initiative: Aperture is a Java framework for extracting and querying full-text content and metadata from various information systems (e.g. file systems, web sites, mail boxes) and the file formats (e.g. documents, images) occurring in these systems. (also used in the Nepomuk project)

OpenDFKI.de

Visit also our department’s Open Source platform for more tools such as a Semantic Wiki

Während einer Flugreise müssen Piloten komplizierte Situationen meistern – gleichzeitig sehen sie sich aufgrund der Menge und Art der verfügbaren Informationen mit einer zunehmenden Systemkomplexität konfrontiert. Co2Team (Cognitive Collaboration for Teaming) verfolgt die Idee, dass ein auf künstlicher Intelligenz basierendes System den Piloten durch den Einsatz von Cognitive Computing effizient unterstützen kann.

Hauptziel des Projekts ist es, einen technologischen und methodischen Übergang zu einem eigenständigeren Flugverkehr vorzuschlagen. Co2Team wird eine Roadmap für Cognitive Computing entwickeln, um den Piloten für den zukünftigen Luftverkehr zu unterstützen. Dieser Übergang basiert auf einem innovativen bidirektionalen Kommunikationsparadigma und einer optimierten gemeinsamen Kompetenz (Mensch-Maschine) unter Nutzung des Potenzials des kognitiven Rechnens (Pilotmonitoring, Umwelt- und Situationsverständnis, erweiterte Unterstützung, adaptive Automatisierung).

Projektpartner sind die Abteilung Augmented Vision des DFKI, die Deutsche Lufthansa AG und das Institut Polytechnique de Bordeaux (INP Bordeaux).

Partners

• Deutsche Lufthansa AG
• Institut Polytechnique de Bordeaux (INP Bordeaux)
• DFKI GmbH

Das Projekt verfolgt die Entwicklung eines Systems zur physikalisch korrekten Visualisierung virtueller Objekte in einer beliebigen realen und dynamischen Umgebung. Dabei kommt eine sphärische Kamera zum Einsatz, um die Umgebung sowie Lichteinwirkung in höchster Qualität aufzunehmen und in Echtzeit auf das virtuelle Objekt anzuwenden (z.B. Spiegelungen, Lichteinwirkung). Die Einbettung des virtuellen Objekts in die reale Umgebung erfolgt dabei durch ein digitales Fernglas mit Alphakanal Unterstützung, welches eine realistische Abbildung ermöglicht. Um das virtuellen Objekts korrekt in die reale Umgebung einzubetten werden die Position und die Blickrichtung des Nutzers bzw. des digitalen Fernglases durch geeignete Trackingverfahren bestimmt, wodurch man sich frei um dieses Objekt bewegen und es von allen Seiten, genau wie einen real existierenden Gegenstand, untersuchen kann.

Eine Besonderheit des Alphakanal-Fernglases ist die Möglichkeit, die Transparenz einzelner Bereiche des realen Bildes beliebig anzupassen. Dies ermöglicht eine realistische Einbettung des virtuellen Objekts in die reale Umgebung ohne störende Überstrahlungen durch hinter dem virtuellen Objekt liegende reale Objekte.

Der Einsatz eines solchen Displays ist einmalig und eröffnet völlig neue und noch nicht erforschte Möglichkeiten der Darstellung für das Gebiet des realistischen Renderings. Ziel ist es, dass der Benutzer die Umgebung natürlich wahrnimmt und nicht unterscheiden kann, welche Objekte in der Umgebung real oder virtuell sind. Dieses ultimative Ziel vom Augmented Reality ist bis dato durch den Einsatz von Live-Video-Hintergrund oder transparenten Standard-Displays ohne Alphakanal nicht zu erreichen. Die Realisierung der geplanten Arbeiten wird zu einer neuen Qualität von Augmented Reality führen und dadurch neue Anwendungen ermöglichen.

Bei der Betriebsführung siedlungswasserwirtschaftlicher Anlagen sind erhebliche Defizite und große Optimierungspotenziale vorhanden. Der zunehmende Wettbewerb zwischen den Entsorgern wird die Betriebe in den nächsten Jahren zwingen, diese Potenziale zu erschließen. Es muss somit nach Mitteln und Wegen gesucht werden, wie die Prozesse in der Siedlungswasserwirtschaft transparenter gemacht und ökonomisch und ökologisch optimiert werden können. Ein (innovativer) Ansatz, dieses Ziel zu erreichen, besteht darin, dem Betriebspersonal (mobile) Werkzeuge an die Hand zu geben, welche Informationen geeignet aufbereitet objekt- und/oder verrichtungsorientiert visualisieren und die Entscheidungsfindung unterstützen.

Ziel des Projektes “SIMILAR – Skalierbare Informationsvisualisierung auf mobilen Endgeräten zur effizienten Informationsvermittlung und betrieblichen Leistungssteigerung im Bereich von Abwasseranlagen” ist die Konzeption und Entwicklung eines Frameworks sowie eines Technologiedemonstrators für ein flexibles, mobiles Informationsvisualisierungssystem im Bereich von Abwasseranlagen. Anhand des Demonstrators sollen beispielhaft mögliche Anwendungen im Bereich der Betriebsführung umgesetzt werden. Dabei sollen modernste mobile Geräte, sowie aktuelle Übertragungstechnologien wie Bluetooth, GPRS oder auch UMTS zum Einsatz kommen.

ARinfuse is an European project funded under Erasmus+, the EU’s programme to support education, training, youth and sport in Europe.

The objective of ARinfuse is to support individuals in acquiring and developing basic skills and key competences within the field of geoinformatics and utility infrastructure, in order to foster employability. This objective is addressed through the development of new learning modules where Augmented Reality technologies are merged with geoinformatics and applied within the utility infrastructure sector. The developed digital learning content and tools will be implemented in university programs as well as in vocational training programs, and will be made available as Open Educational Resources, open textbooks and Open Source Educational Software.

Partners

• GeoImaging Ltd (Cyprus)
• Novogit AB (Sweden)
• Cyprus University of Technology (Cyprus)
• GISIG Association (Italy)
• Sewerage Board of Nicosia (Cyprus)
• Flanders Environment Agency (VMM, Belgium)
• DFKI GmbH

Today, companies are confronted with rapid changes within their environment. Thus, open market places are developed in the Internet to satisfy more and more individual customer requirements. New innovations in information and communication technology (ICT) are generated at high speed and completely new value chains and business models evolve across corporate boundaries. Now in the face of those changes, companies will only be able to preserve and strengthen their economic productivity if they move towards so-called digital companies, in which ICT is the key driver for product and process innovations. The goal of EMERGENT is to lay the foundations for this change. For this purpose, basic methods, techniques, algorithms and data structures for emergent software are explored and developed.

The term emergent software represents a new category of innovative information systems that arise from the cross-company interaction of individual components and services on the Internet of things and services. The use of emergent software allows a dynamic adaptation to the requirements of the market and supports the handling of complex and dynamic business processes thereby enabling innovative services in the future Internet. This constitutes on the one hand a significant competitive advantage for manufacturers of enterprise software as opposed to conventionally organized, static businesses software solutions. On the other hand, it also increases the value-creation significantly for users of emergent software, especially for small and medium enterprises.