Publications by Nicolas Aspert

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During the last 20 years, the joint expansion of computing power, computer graphics, networking capabilities and multiresolution analysis have stimulated several research domains, and developed the need for new types of data such as 3D models, i.e. discrete surfaces. In the intersection between multiresolution analysis and computer graphics, subdivision methods, i.e. iterative refinement procedures of curves or surfaces, have a non-negligible place, since they are a basic component needed to adapt existing

In this paper, we present an original non-linear subdivision scheme suitable for univariate data, plane curves and discrete triangulated surfaces, while keeping the complexity acceptable. The proposed technique is compared to linear subdivision methods having an identical support. Numerical criteria are proposed to verify basic properties, such as convergence of the scheme and the regularity of the limit function.

This paper proposes an efficient method to estimate the distance between discrete 3D surfaces represented by triangular 3D meshes. The metric used is based on an approximation of the Hausdorff distance, which has been appropriately implemented in order to reduce unnecessary computations and memory usage. Results show that when compared to similar tools, a significant gain in both memory and speed can be achieved

We investigate the design of a convolutional layer where kernels are parameterized functions. This layer aims at being the input layer of convolutional neural networks for audio applications or applications involving time-series. The kernels are defined as ...

This paper proposes a method to embed information into a 3D model represented by a polygonal mesh. The approach used consists in slightly changing the position of the vertices, in uencing the length of approximation of the normals to the surface. This technique exhibits relatively low complexity, and o ers robustness to simple geometric tranformations. In addition, it does not introduce any visible distortion to the original model.

This article provides feedback on a multidisciplinary and applied research project to design a data mapping and exploration tool on Twitter for journalists. The approach focuses on the observation and understanding of journalists' uses and a projection of ...

In this work, we propose an automatic evaluation and comparison of the browsing behavior of Wikipedia readers that can be applied to any language editions of Wikipedia. As an example, we focus on English, French, and Russian languages during the last four ...

This report summarizes the 23 tutorials hosted at The Web Conference 2021: nine lecture-style tutorials and 14 hands-on tutorials.

Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatic ...

Nicolas Aspert

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Non-linear subdivision of univariate signals and discrete surfaces

Non-linear subdivision using local spherical coordinates

MESH: Measuring Errors between Surfaces using the Hausdorff distance

Learnable filter-banks for CNN-based audio applications

Steganography for Three-Dimensional Polygonal Meshes

Naviguer dans les traces numériques sur Twitter. Retour sur la conception d’un dispositif de cartographie de données à destination de journalistes

What is Trending on Wikipedia? Capturing Trends and Language Biases Across Wikipedia Editions

Summary of Tutorials at The Web Conference 2021

Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion

Photo-Realistic 3D Model Coding in MPEG-4