Nicolas Boutry

In Information Sciences

Abstract Providing interpretability of deep-learning models to non-experts, while fundamental for a responsible real-world usage, is challenging. Attribution maps from xAI techniques, such as Integrated Gradients, are a typical example of a visualization technique containing a high level of information, but with difficult interpretation. In this paper, we propose two methods, Maximum Activation Groups Extraction (MAGE) and Multiscale Interpretable Visualization (Ms-IV), to explain the model’s decision, enhancing global interpretability. MAGE finds, for a given CNN, combinations of features which, globally, form a semantic meaning, that we call concepts.

Bridging human concepts and computer vision for explainable face verification

By Miriam Doh, Caroline Mazini-Rodrigues, Nicolas Boutry, Laurent Najman, Mancas Matei, Hugues Bersini

2023-10-10

In 2nd international workshop on emerging ethical aspects of AI (BEWARE-23)

Abstract With Artificial Intelligence (AI) influencing the decision-making process of sensitive applications such as Face Verification, it is fundamental to ensure the transparency, fairness, and accountability of decisions. Although Explainable Artificial Intelligence (XAI) techniques exist to clarify AI decisions, it is equally important to provide interpretability of these decisions to humans. In this paper, we present an approach to combine computer and human vision to increase the explanation’s interpretability of a face verification algorithm.

Discrete Morse functions and watersheds

By Gilles Bertrand, Nicolas Boutry, Laurent Najman

2023-08-10

In Journal of Mathematical Imaging and Vision

Abstract Any watershed, when defined on a stack on a normal pseudomanifold of dimension $d$, is a pure $(d-1)$-subcomplex that satisfies a drop-of-water principle. In this paper, we introduce Morse stacks, a class of functions that are equivalent to discrete Morse functions. We show that the watershed of a Morse stack on a normal pseudomanifold is uniquely defined, and can be obtained with a linear-time algorithm relying on a sequence of collapses.

Introducing PC $n$-manifolds and $P$-well-composedness in partially ordered sets

By Nicolas Boutry

2023-08-01

In Journal of Mathematical Imaging and Vision

Abstract In discrete topology, discrete surfaces are well-known for their strong topological and regularity properties. Their definition is recursive, and checking if a poset is a discrete surface is tractable. Their applications are numerous: when domain unicoherence is ensured, they lead access to the tree of shapes, and then to filtering in the shape space (shapings); they also lead to Laplacian zero-crossing extraction, to brain tumor segmentation, and many other applications related to mathematical morphology.

Structural and spectral analysis of dynamic graphs for attack detection

By Majed Jaber, Nicolas Boutry, Pierre Parrend

2023-07-01

In Rencontre des jeunes chercheurs en inteligence artificielle (RJCIA-2023)

Abstract At this time, cyberattacks represent a constant threat. Many approaches exist for detecting suspicious behaviors, but very few of them seem to benefit from the huge potential of mathematical approaches like spectral graph analysis, known to be able to extract topological features of a graph using its Laplacian spectrum. For this reason, we consider our network as a dynamic graph composed of nodes (representing the devices) and of edges (representing the requests), and we compute its Laplacian spectrum across time.

Towards attack detection in traffic data based on spectral graph analysis

By Majed Jaber, Nicolas Boutry, Pierre Parrend

2023-04-01

In

Abstract Nowadays, cyberattacks have become a significant concern for individuals, organizations, and governments. These attacks can take many forms, and the consequences can be severe. In order to protect ourselves from these threats, it is essential to employ a range of different strategies and techniques like detection of patterns, classification of system behaviors against previously known attacks, and anomaly detection techniques. This way, we can identify unknown forms of attacks. Few of these existing techniques seem to fully utilize the potential of mathematical approaches such as spectral graph analysis.

Gradients intégrés renforcés

By Caroline Mazini-Rodrigues, Nicolas Boutry, Laurent Najman

2022-12-15

In Explain’AI - EGC workshop

Abstract Les visualisations fournies par les techniques d’Intelligence Artificielle Explicable xAI) pour expliquer les réseaux de neurones convolutionnels (CNN’s) sont parfois difficile á interpréter. La richesse des motifs d’une image qui sont fournis en entrées (les pix l d’une image) entraîne des corrélations complexes entre les classes. Les techniques basées sur les gradients, telles que les gradients intégrés, mettent en évidence l’import nce de ces caractéristiques. Cependant, lorsqu’on les visualise sous forme d’images, on peut e retrouver avec un bruit excessif et donc une difficulté á interpréter les explic tions fournies.

Topology-aware method to segment 3D plan tissue images

By Minh Ôn Vũ Ngọc, Nicolas Boutry, Jonathan Fabrizio

2022-10-25

In 36th conference on neural information processing systems, AI for science workshop

Abstract The study of genetic and molecular mechanisms underlying tissue morphogenesis has received a lot of attention in biology. Especially, accurate segmentation of tissues into individual cells plays an important role for quantitative analyzing the development of the growing organs. However, instance cell segmentation is still a challenging task due to the quality of the image and the fine-scale structure. Any small leakage in the boundary prediction can merge different cells together, thereby damaging the global structure of the image.

Some equivalence relation between persistent homology and morphological dynamics

By Nicolas Boutry, Laurent Najman, Thierry Géraud

2022-05-17

In Journal of Mathematical Imaging and Vision

Abstract In Mathematical Morphology (MM), connected filters based on dynamics are used to filter the extrema of an image. Similarly, persistence is a concept coming from Persistent Homology (PH) and Morse Theory (MT) that represents the stability of the extrema of a Morse function. Since these two concepts seem to be closely related, in this paper we examine their relationship, and we prove that they are equal on $n$-D Morse functions, $n\geq 1$.

Local intensity order transformation for robust curvilinear object segmentation

By Tianyi Shi, Nicolas Boutry, Yongchao Xu, Thierry Géraud

2022-03-22

In IEEE Transactions on Image Processing

Abstract Segmentation of curvilinear structures is important in many applications, such as retinal blood vessel segmentation for early detection of vessel diseases and pavement crack segmentation for road condition evaluation and maintenance. Currently, deep learning-based methods have achieved impressive performance on these tasks. Yet, most of them mainly focus on finding powerful deep architectures but ignore capturing the inherent curvilinear structure feature (e.g., the curvilinear structure is darker than the context) for a more robust representation.

Unsupervised discovery of interpretable visual concepts

Bridging human concepts and computer vision for explainable face verification

Discrete Morse functions and watersheds

Introducing PC $n$-manifolds and $P$-well-composedness in partially ordered sets

Structural and spectral analysis of dynamic graphs for attack detection

Towards attack detection in traffic data based on spectral graph analysis

Gradients intégrés renforcés

Topology-aware method to segment 3D plan tissue images

Some equivalence relation between persistent homology and morphological dynamics

Local intensity order transformation for robust curvilinear object segmentation

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