Large-scale Geometric Data Decomposition, Processing and Structured Mesh Generation

Mesh generation is a fundamental and critical problem in geometric data modeling and processing. In most scientific and engineering tasks that involve numerical computations and simulations on 2D/3D regions or on curved geometric objects, discretizing or approximating the geometric data using a polygonal or polyhedral meshes is always the first step of the procedure. The quality of this tessellation often dictates the subsequent computation accuracy, efficiency, and numerical stability. When compared with unstructured meshes, the structured meshes are favored in many scientific/engineering tasks due to their good properties. However, generating high-quality structured mesh remains challenging, especially for complex or large-scale geometric data. In industrial Computer-aided Design/Engineering (CAD/CAE) pipelines, the geometry processing to create a desirable structural mesh of the complex model is the most costly step. This step is semi-manual, and often takes up to several weeks to finish. Several technical challenges remains unsolved in existing structured mesh generation techniques. This dissertation studies the effective generation of structural mesh on large and complex geometric data. We study a general geometric computation paradigm to solve this problem via model partitioning and divide-and-conquer. To apply effective divide-and-conquer, we study two key technical components: the shape decomposition in the divide stage, and the structured meshing in the conquer stage. We test our algorithm on vairous data set, the results demonstrate the efficiency and effectiveness of our framework. The comparisons also show our algorithm outperforms existing partitioning methods in final meshing quality. We also show our pipeline scales up efficiently on HPC environment

WAVELET BASED DATA HIDING OF DEM IN THE CONTEXT OF REALTIME 3D VISUALIZATION (Visualisation 3D Temps-Réel à Distance de MNT par Insertion de Données Cachées Basée Ondelettes)

The use of aerial photographs, satellite images, scanned maps and digital elevation models necessitates the setting up of strategies for the storage and visualization of these data. In order to obtain a three dimensional visualization it is necessary to drape the images, called textures, onto the terrain geometry, called Digital Elevation Model (DEM). Practically, all these information are stored in three different files: DEM, texture and position/projection of the data in a geo-referential system. In this paper we propose to stock all these information in a single file for the purpose of synchronization. For this we have developed a wavelet-based embedding method for hiding the data in a colored image. The texture images containing hidden DEM data can then be sent from the server to a client in order to effect 3D visualization of terrains. The embedding method is integrable with the JPEG2000 coder to accommodate compression and multi-resolution visualization. Résumé L'utilisation de photographies aériennes, d'images satellites, de cartes scannées et de modèles numériques de terrains amène à mettre en place des stratégies de stockage et de visualisation de ces données. Afin d'obtenir une visualisation en trois dimensions, il est nécessaire de lier ces images appelées textures avec la géométrie du terrain nommée Modèle Numérique de Terrain (MNT). Ces informations sont en pratiques stockées dans trois fichiers différents : MNT, texture, position et projection des données dans un système géo-référencé. Dans cet article, nous proposons de stocker toutes ces informations dans un seul fichier afin de les synchroniser. Nous avons développé pour cela une méthode d'insertion de données cachées basée ondelettes dans une image couleur. Les images de texture contenant les données MNT cachées peuvent ensuite être envoyées du serveur au client afin d'effectuer une visualisation 3D de terrains. Afin de combiner une visualisation en multirésolution et une compression, l'insertion des données cachées est intégrable dans le codeur JPEG 2000

Working Group “Red”. Sharing Responsibility for the Protection of the Danube Delta (SHARED – Society, Heritage, Awareness, River, Environment, Danube)

The project focuses on involving stakeholders such as communities, authorities, industry, and researchers from the Danube River Basin (DRB) countries in sharing responsibility for the protection of natural heritage in the Danube Delta (DD). The expected results include increased shared responsibility for the protection of the Danube Delta natural heritage, informed and engaged communities in the DRB, and better networking between the stakeholders. These results will be achieved by using an upstream-downstream cooperation approach through round-trip visits by a Boat interactive centre, application of ICT, and a plethora of events. The project results will promote institutional, economic, and behavioral changes that ensure sustainability and preservation of natural heritage

On realistic target coverage by autonomous drones

Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent sensing systems. To achieve the full potential of such drones, it is necessary to develop new enhanced formulations of both common and emerging sensing scenarios. Namely, several fundamental challenges in visual sensing are yet to be solved including (1) fitting sizable targets in camera frames; (2) positioning cameras at effective viewpoints matching target poses; and (3) accounting for occlusion by elements in the environment, including other targets. In this article, we introduce Argus, an autonomous system that utilizes drones to collect target information incrementally through a two-tier architecture. To tackle the stated challenges, Argus employs a novel geometric model that captures both target shapes and coverage constraints. Recognizing drones as the scarcest resource, Argus aims to minimize the number of drones required to cover a set of targets. We prove this problem is NP-hard, and even hard to approximate, before deriving a best-possible approximation algorithm along with a competitive sampling heuristic which runs up to 100× faster according to large-scale simulations. To test Argus in action, we demonstrate and analyze its performance on a prototype implementation. Finally, we present a number of extensions to accommodate more application requirements and highlight some open problems

Privacy Intelligence: A Survey on Image Sharing on Online Social Networks

Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion. The recent image leaks from popular OSN services and the abuse of personal photos using advanced algorithms (e.g. DeepFake) have prompted the public to rethink individual privacy needs when sharing images on OSNs. However, OSN image sharing itself is relatively complicated, and systems currently in place to manage privacy in practice are labor-intensive yet fail to provide personalized, accurate and flexible privacy protection. As a result, an more intelligent environment for privacy-friendly OSN image sharing is in demand. To fill the gap, we contribute a systematic survey of 'privacy intelligence' solutions that target modern privacy issues related to OSN image sharing. Specifically, we present a high-level analysis framework based on the entire lifecycle of OSN image sharing to address the various privacy issues and solutions facing this interdisciplinary field. The framework is divided into three main stages: local management, online management and social experience. At each stage, we identify typical sharing-related user behaviors, the privacy issues generated by those behaviors, and review representative intelligent solutions. The resulting analysis describes an intelligent privacy-enhancing chain for closed-loop privacy management. We also discuss the challenges and future directions existing at each stage, as well as in publicly available datasets.Comment: 32 pages, 9 figures. Under revie

Understanding intentions in animacy displays derived from human motion

As humans we live in a world where we are constantly interacting with those around us. To achieve this we must be able to successfully anticipate the intentions of others by correctly interpreting their movements. In studying how humans interpret intention from motion, we make use of simplified scenarios known as animacy displays where it has been shown that observers will attribute human-like qualities to the motion of geometric shapes (Heider and Simmel, 1944). This thesis advances the research into the attribution of social intentions by re-addressing the methods for the creation of animacy displays, leading to previously unexplored avenues of research. Where animacy displays are normally made via clever animations or mathematical algorithms, we introduce a method for creating these displays directly from video recordings of human motion, there by producing the first examples of animacy displays that are truly representative of human motion. Initially, explorative steps were taken to establish this technique as successful in creating displays that will be perceived as animate, using video recordings of simple and complex human interactions as a basis. Using a combination of tasks, including free response tasks and 10 point Likert scales, the use of this technique for stimulus production was validated. Furthermore, results showed that the viewpoint from which animacy displays are to be perceived from, comparing a side view and an overhead view, has effects on the ability to judge intentions in the displays, with a clear preference to the elevated viewpoint. Following this, the intentions of Chasing, Fighting, Flirting, Following, Guarding and Playing, thought to be generic to animacy displays, were used to create displays via this new method of stimulus production. Using a six Alternative Forced Choice (AFC) task it was shown that participants are successful at recognising these intentions, however, that the addition of ordinal depth cues, as well as cues to identity and boundaries, has little impact on increasing the ability to perceive intentions in animacy displays. Next, an experiment on the ability to judge intentions in animacy displays of brief durations was performed. Using the same 6 intentions as before, displays were created lasting 1, 5, and 10 seconds. Results of a 6 AFC task showed that observers are accurate at all durations, and furthermore, results indicate that participants are as accurate at recognising the intention in a display after 5 seconds, as after viewing longer durations of approximately 30 seconds. We then perform a comprehensive analysis of the animacy displays used, looking at the motion patterns and the kinematic properties such as speed, acceleration and distance of the agents. This analysis shows clear differences in the displays across viewpoints, and across intentions, that are indicative of the cues that participants may use to differentiate between intentions. We also perform a stepwise regression analysis to find the motion and positional predictors that best explain the variance in the behavioural data of previous experiments in this thesis. It is found that speed and acceleration cues are important for the classification of intentions in animacy displays. Finally, a study is presented that attempts to advance research into the perception of social intentions by people with Autistic Spectrum Disorders (ASDs), using video recordings of human motions and the resultant animacy displays. The intentions of Chasing, Fighting, Flirting, Following, Guarding and Playing, were again used in conjunction with a 6 AFC task. Comparing people with ASDs to an age-matched control population, results indicate that people with ASDs are poorer at judging intentions in animacy displays. In addition, results reveal an unknown deficit, not seen in the control population, in judging intentions from an elevated position in video displays. This work may be considered of interest to various groups of people with a wide range of research interests, including the perception and cognition of human motion, the attribution of social intent and “Theory of Mind”, and the surveillance of people via video techniques