Rockfall detection is a crucial procedure in the field of geology, which helps to reduce the associated risks. Currently, geologists identify rockfall events almost manually utilizing point cloud and imagery data obtained from different caption devices such as Terrestrial Laser Scanner (TLS) or digital cameras. Multitemporal comparison of the point clouds obtained with these techniques requires a tedious visual inspection to identify rockfall events which implies inaccuracies that depend on several factors such as human expertize and the sensibility of the sensors. This paper addresses this issue and provides an intelligent framework for rockfall event detection for any individual working in the intersection of the geology domain and decision support systems. The development of such an analysis framework presents major research challenges and justifies exhaustive experimental analysis. In particular, we propose an intelligent system that utilizes multiple machine learning algorithms to detect rockfall clusters of point cloud data. Due to the extremely imbalanced nature of the problem, a plethora of state-of-the-art resampling techniques accompanied by multiple models and feature selection procedures are being investigated. Various machine learning pipeline combinations have been examined and benchmarked applying well-known metrics to be incorporated into our system. Specifically, we developed machine learning techniques and applied them to analyze point cloud data extracted from TLS in two distinct case studies, involving different geological contexts: the basaltic cliff of Castellfollit de la Roca and the conglomerate Montserrat Massif, both located in Spain. Our experimental results indicate that some of the above-mentioned machine learning pipelines can be utilized to detect rockfall incidents on mountain walls, with experimentally validated accuracy.

Abstract In volume visualization, the definition of the regions of interest is inherently an iterative trial‐and‐error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi‐dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on‐demand multiple regions of interest. These methods can be split into a two‐level GPU‐based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error‐Correcting Output Codes (ECOC) framework. In a pre‐processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre‐labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi‐class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU‐OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy.

ABSTRACT In this paper, we present a generic interaction framework that controls intelligent objects' actions in different virtual world (VW) platforms. These actions are based on the state of external platform‐independent artificial intelligence‐based systems such as multi‐agent and rule‐based systems. We have evaluated the proposed framework by means of two intelligent objects, a door and a noticeboard, incorporating them in Second Life and OpenWonderland VWs. These objects allow to work along three advanced aspects of a serious virtual environment: it is a dynamic space where participants are synchronously informed about activities evolution inside the virtual space, it can integrate processes and information coming from the real world, and it needs norms to organize participants actions, to define actions consequences, and to prevent undesired participants behaviors. Copyright © 2011 John Wiley & Sons, Ltd.

Our research combines electronic institutions and 3D virtual worlds for the construction of virtual institutions which are virtual worlds with normative regulation of interactions. That is, a virtual world where participants actions have to comply with predefined institutional rules. In this context, the actions a participant may perform depend on the institutional rules and the current execution state. We propose to include iObjects, intelligent objects, as entities having both visualization properties and decision mechanisms in the virtual institution. They are a new key element to improve users participation in virtual institutions. We situate them in a middleware infrastructure in order to be independent of 3D virtual world platform and to provide a general solution in which participants could be connected from different immersive environment platforms.

Abstract Many different direct volume rendering methods have been developed to visualize 3D scalar fields on uniform rectilinear grids. However, little work has been done on rendering simultaneously various properties of the same 3D region measured with different registration devices or at different instants of time. The demand for this type of visualization is rapidly increasing in scientific applications such as medicine in which the visual integration of multiple modalities allows a better comprehension of the anatomy and a perception of its relationships with activity. This paper presents different strategies of direct multimodal volume rendering (DMVR). It is restricted to voxel models with a known 3D rigid alignment transformation. The paper evaluates at which steps of the rendering pipeline the data fusion must be realized in order to accomplish the desired visual integration and to provide fast re‐renders when some fusion parameters are modified. In addition, it analyses how existing monomodal visualization algorithms can be extended to multiple datasets and it compares their efficiency and their computational cost. Copyright © 2004 John Wiley & Sons, Ltd.

The 3D reconstruction of the cerebral vascular anatomy can greatly improve the diagnosis of vascular pathologies, such as embolisms and haemorrhages. This paper presents a new method for the automatic extraction of the blood vessels surface and the labelling of its features, specifically branching, aneurysms and stenoses. The method works with segmented magnetic resonance angiography images. It extracts a discrete medial axis transform from the stacked images and vectorizes it. During the vectorization process, the features are detected and labelled automatically. The final output is a labelled continuous medial axis transform representation of the vascular tree suitable for interactive visualizations and navigations, along with 3D geometrical and topological queries.

An interactive cerebral blood vessel exploration system is described. It has been designed on the basis of neurosurgeons' requirements in order to assist them in the diagnosis of vascular pathologies. The system is based on the construction of a symbolic model of the vascular tree, with automatic identification and labelling of vessel bifurcations, aneurysms and stenoses. It provides several types of visualization: individual MRA (magnetic resonance angiography) slices, MIP (maximum intensity projection), shaded rendering, symbolic schemes and surface reconstruction.