Realidad virtual y visualización de datos

The Virtual Reality area maintains four main research lines:

1.- VIRTUAL REALITY

The term Virtual Reality (VR) makes reference to any computer generated environment where it is possible to simulate physical presence of places in the real world and also imaginary worlds. The degree of presence inside the virtual environment depends of how immersed our senses are. To achieve this, VR makes use of real time stereoscopic graphics through different kind of display devices (HMD, StereoWall, CAVE, etc.), tracking systems to monitor one or multiple users and sensory haptic devices.

In the VR lab in CeDInt, our work has been focused mainly in the generation of realistic 3D content and applications to help the user get more involved with the content presented. We see VR as a key technology not only for the visualization and simulation of experiences but also as a great tool to help with real life problems. With this idea in mind, our VR projects have been aimed to detect and deal with these problems like the generation of a tool to reconstruct soft tissues or a monitor system for controlled environments to support decision taking.

Applications:

• Design, modeling and visualization of objects and places of interest such as buildings, environments, products, etc.
• Reconstruction and virtual visits to historical real places and heritage.
• Complex data visualization and simulation of processes.
• Formation and training of people in a collaborative way to accomplish complicated/dangerous procedures.
• Scientific research in medicine by virtually reproducing real life conditions and study users reactions in a practical and controlled way.

2.- AUGMENTED REALITY

The goal of augmented reality is the integration of computer generated content over a live video or the user's environment in real time. It is quite literally the practice of enhancing what’s already around us. Unlike virtual reality, augmented reality does not create a simulation of reality. Instead, it takes a real object or space as the foundation and adds contextual data to deepen a person’s understanding of the subject. The most often used example and one of the first commercial applications of AR technology was the yellow offside line in televised soccer games.

CeDInt works in augmented reality projects, pulling graphics out of your smartphone or computer display and trying to create experiences in which the user cannot tell the difference between the real world and the virtual augmentation of it. This technique provides a development framework for the study and research in a wide range of areas like entertainment, military training, engineering design, robotics, manufacturing and other industries.

Applications:

• Tracking users in unknown environment, using algorithms from robotic engineering, for instance SLAM and PTAM.
• Representation of contextual information superimposed according to the geometry of the environment.
• Possibility for users to rebuild ruins, buildings, or even landscapes as they formerly existed.
• Provide interactive content assisting the learning process. AR also permits learning via remote collaboration.
• Decision-making support to help industrial designers experience a product's design and operation before completion.
• Solve complex tasks such as assembly, maintenance, and surgery.
• Visualize building projects before the physical building is constructed. Architecture sight-seeing can be enhanced with AR applications allowing users viewing a building's exterior to virtually see through its walls viewing it's interior objects and layout.

 3.- COMPUTER VISION

Computer Vision, also Image Understanding, is a part of Artificial Intelligence that allows a machine to recognize the structure and properties of the environment represented in an image in the same way a human does. It applies methods like image processing, pattern recognition, statistics and graph theory.

In the Virtual Reality lab in CeDInt, Computer Vision techniques are used to support Virtual Reality and Augmented Reality areas.  These techniques process the images captured by cameras providing necessary data to the final applications, i. e. object detection or scene objects depth estimation for interaction.

Among the Computer Vision techniques employed are:

• Segmentation
• Feature extraction
• Stereo Matching
• Facial recognition
• Scenery Reconstruction

4.- INFORMATION VISUALIZATION AND VISUAL ANALYTICS

Problem solving for both academic and industrial researches is heavily related with a data analysis approach. In the data-boom era, this could sound really dramatic if researchers do not rely on proper tools and methodologies in order to facilitate their insight. For those reasons, Information Visualization and Visual Analytics have become a very active area of study in the recent years. The former aims at visually depicting abstract information into a more human-understandable way; the latter enforces human ability to reason about and understand complex and dynamic scenarios through perception mechanisms. The final goal of both of them is acquiring valuable knowledge from raw data by means of interactive visual metaphors.

Our group is currently interested in the following themes:

• Geo-spatial data representation;
• Timeline data analysis;
• Understanding complex patterns in urban environments;
• Supporting policy-making actors;
• Supporting data mining experts;
• Representation of data features into virtual / augmented reality environment;