3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.     

Towards the generation of dialogue acts in socio-affective ECAs: a corpus-based prosodic analysis

We present a corpus-based prosodic analysis with the aim of uncovering the relationship between dialogue acts, personality and prosody in view to providing guidelines for the ECA Greta’s text-to-speech system. The corpus used is the SEMAINE corpus, featuring four different personalities, further annotated for dialogue acts and prosodic features. In order to show the importance of the choice of dialogue act taxonomy, two different taxonomies were used, the first corresponding to Searle’s taxonomy of speech acts and the second, inspired by Bunt’s DIT++, including a division of directive acts into finer categories. Our results show that finer-grained distinctions are important when choosing a taxonomy. We also show with some preliminary results that the prosodic correlates of dialogue acts are not always as cited in the literature and prove more complex and variable. By studying the realisation of different directive acts, we also observe differences in the communicative strategies of the ECA depending on personality, in view to providing input to a speech system.     

Structural design using multi-objective metaheuristics. Comparative study and application to a real-world problem

Many structural design problems in the field of civil engineering are naturally multi-criteria, i.e., they have several conflicting objectives that have to be optimized simultaneously. An example is when we aim to reduce the weight of a structure while enhancing its robustness. There is no a single solution to these types of problems, but rather a set of designs representing trade-offs among the conflicting objectives. This paper focuses on the application of multi-objective metaheuristics to solve two variants of a real-world structural design problem. The goal is to compare a representative set of state-of-the-art multi-objective metaheuristic algorithms aiming to provide civil engineers with hints as to what optimization techniques to use when facing similar problems as those selected in the study presented in this paper. Accordingly, our study reveals that MOCell, a cellular genetic algorithm, provides the best overall performance, while NSGA-II, the de facto standard multi-objective metaheuristic technique, also demonstrates a competitive behavior.     

Massively parallel Lucas Kanade optical flow for real-time video processing applications

This paper deals with dense optical flow estimation from the perspective of the trade-off between quality of the estimated flow and computational cost which is required by real-world applications. We propose a fast and robust local method, denoted by eFOLKI, and describe its implementation on GPU. It leads to very high performance even on large image formats such as 4 K (3,840 × 2,160) resolution. In order to assess the interest of eFOLKI, we first present a comparative study with currently available GPU codes, including local and global methods, on a large set of data with ground truth. eFOLKI appears significantly faster while providing quite accurate and highly robust estimated flows. We then show, on four real-time video processing applications based on optical flow, that eFOLKI reaches the requirements both in terms of estimated flows quality and of processing rate.