Optimal Parameterization of Point Distribution Models

We address the problem of determining the \emph{optimal model complexity} for shape modeling. This complexity is a compromise between model specificity and generality. We show that the error of a model can be split into two components, the model error and the fitting error, of which the first one can be used to optimize the model complexity based on the specific application. This strategy improves over traditional approaches, where the model complexity is only determined by vague heuristics or trial-and-error. A method for the determination of optimal active shape models is proposed and its efficiency is validated in several experiments. Furthermore, this method gives an indication on the range of valid shape parameters and on whether or not an increased number of training data will reduce the number of shape parameters further

Task Driven Perceptual Organization for Extraction of Rooftop Polygons

A new method for extracting planar polygonal rooftops in monocular aerial imagery is proposed. Structural features are extracted and hierarchically related using perceptual grouping techniques. Top-down feature verification is used so that features, and links between the features, are verified with local information in the image and weighed in a graph. Cycles in the graph correspond to possible building rooftop hypotheses. Virtual features are hypothesized for the perceptual completion of partially occluded rooftops. Extraction of the "best" grouping of features into a building rooftop hypothesis is posed as a graph search problem. The maximally weighted, independent set of cycles in the graph is extracted as the final set of roof boundaries. 1 Introduction Extraction of polygonal structures from an aerial image is an important step in building detection and model construction. We would like to determine the shape and location of buildings within an aerial image robustly and accuratel..

Modelagem tridimensional de edificações usando dados do sistema laser scanner e imagem orbital de alta resolução /

Orientador : Prof. Dr. Jorge A. Silva CentenoCo-orientador : Prof. Dr. Edson Aparecido MitishitaTese (doutorado) - Universidade Federal do Paraná, Setor de Ciencias da Terra, Programa de Pós-Graduação em Ciencias Geodésicas. Defesa: Curitiba, 2007Inclui bibliografi

Large-scale 3D environmental modelling and visualisation for flood hazard warning.

3D environment reconstruction has received great interest in recent years in areas such as city planning, virtual tourism and flood hazard warning. With the rapid development of computer technologies, it has become possible and necessary to develop new methodologies and techniques for real time simulation for virtual environments applications. This thesis proposes a novel dynamic simulation scheme for flood hazard warning. The work consists of three main parts: digital terrain modelling; 3D environmental reconstruction and system development; flood simulation models. The digital terrain model is constructed using real world measurement data of GIS, in terms of digital elevation data and satellite image data. An NTSP algorithm is proposed for very large data assessing, terrain modelling and visualisation. A pyramidal data arrangement structure is used for dealing with the requirements of terrain details with different resolutions. The 3D environmental reconstruction system is made up of environmental image segmentation for object identification, a new shape match method and an intelligent reconstruction system. The active contours-based multi-resolution vector-valued framework and the multi-seed region growing method are both used for extracting necessary objects from images. The shape match method is used with a template in the spatial domain for a 3D detailed small scale urban environment reconstruction. The intelligent reconstruction system is designed to recreate the whole model based on specific features of objects for large scale environment reconstruction. This study then proposes a new flood simulation scheme which is an important application of the 3D environmental reconstruction system. Two new flooding models have been developed. The first one is flood spreading model which is useful for large scale flood simulation. It consists of flooding image spatial segmentation, a water level calculation process, a standard gradient descent method for energy minimization, a flood region search and a merge process. The finite volume hydrodynamic model is built from shallow water equations which is useful for urban area flood simulation. The proposed 3D urban environment reconstruction system was tested on our simulation platform. The experiment results indicate that this method is capable of dealing with complicated and high resolution region reconstruction which is useful for many applications. When testing the 3D flood simulation system, the simulation results are very close to the real flood situation, and this method has faster speed and greater accuracy of simulating the inundation area in comparison to the conventional flood simulation model