Abstract
Expeditious modelling of virtual urban environments consists of generating realistic 3d models from limited information. It has several practical applications but typically suffers from a lack of accuracy in the parameter values that feed the modeller. By gathering small amounts of information about certain key urban areas, it becomes possible to feed a system that automatically compares and adjusts the input parameter values to find optimal solutions of parameter combinations that resemble the real life model. These correctly parameterized rules can then be reapplied to generate virtual models of real areas with similar characteristics to the referenced area. Based on several nature inspired metaheuristic algorithms such as genetic algorithms, simulated annealing and harmony search, this paper presents a new hybrid metaheuristic algorithm capable of optimizing functions with both discrete and continuous parameters and offer competitive results in a highly neglected field of application.

Abstract

Abstract
In this paper we address the problem of automatically computing a set of views over a simulated three dimensional enviromnent. The viewing system aims at, for each moment, supplying the user with the most pertinent information in order to allow a good understanding of the evolving environment. Our approach relies on an innovative optimization architecture that enables intelligent optimization techniques based on simulated annealing and genetic algorithms. Reported experiments were performed in urban rescue scenarios from the RoboCup Rescue Domain. We outline the possible extension of the proposed architecture to other visualization problems and argue on how several problems within the fields of Visualization and Rendering can benefit from it.

Abstract
In this paper we address the problem of automatically computing a set of views over a simulated three dimensional environment. The viewing system aims at, for each moment, supplying the user with the most pertinent information in order to allow a good understanding of the evolving environment. Our approach relies on an innovative optimization architecture that enables intelligent optimization techniques based on simulated annealing and genetic algorithms. Reported experiments were performed in urban rescue scenarios from the RoboCup Rescue Domain. We outline the possible extension of the proposed architecture to other visualization problems and argue on how several problems within the fields of Visualization and Rendering can benefit from it.

Abstract
A Light Field is an imaged based rendering technique based on the eight dimensional Plenoptic Function, simplified to a four dimensional function. It describes the amount of light covering the space, from any point, in any arbitrary direction. It ignores variables like the time and wavelength and assumes that radiance is equal throughout a line in the free space and so it is more efficient than most of the other image based rendering techniques in the literature. Drawbacks appear in the quality of the visualization but may be reduced by the correct use of the technique regarding the desired application. This paper presents a didactic application of this type of imaged based rendering technique that uses multiple sights of a scene through different points of views. The application of light-fields to medical images is thought one of the main objectives of this work and so, a simple application of this image based rendering technique, to this type of images is presented in order to show the usefulness of the approach.

Abstract
There are several problems in the computer graphics and visualization domains which require optimization tasks to be performed in order to improve the quality of the overall process. In this context, we propose and describe an innovative optimization methodology and a supporting software framework: i-om. The design goals of the proposed framework were twofold. The first comprises the decoupling, as much as possible, of the optimization process from the application specific processing tasks. In order to attain this goal, we opted to make use of intelligent techniques (i.e. metaheuristics). The second goal is to allow remote operation, and consequently great portability and interoperability, between the optimization tools and the visualization application. To fulfill the latter requirement the proposed framework was designed with the ability to communicate with external application using a specifically developed high level message protocol. The optimization framework was implemented and the paper presents illustrative results demonstrating the usefulness and effectiveness of the proposed approach..