Publications

A portable, simple, embeddable type system

By Jim Newton, Adrien Pommellet

2021-04-26

In ELS 2021, the 14th european lisp symposium

Abstract

We present a simple type system inspired by that of Common Lisp. The type system is intended to be embedded into a host language and accepts certain fundamental types from that language as axiomatically given. The type calculus provided in the type system is capable of expressing union, intersection, and complement types, as well as membership, subtype, disjoint, and habitation (non-emptiness) checks. We present a theoretical foundation and two sample implementations, one in Clojure and one in Scala.

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An innovative and decentralized identity framework based on blockchain technology

By Daniel Maldonado-Ruiz, Jenny Torres, Nour El Madhoun, Mohamad Badra

2021-04-01

In 11th IFIP international conference on new technologies, mobility and security (NTMS)

Abstract

Network users usually need a third party validation to prove that they are who they claim to be. Authentication systems mostly assume the existence of a Trusted Third Party (TTP) in the form of a Certificate Authority (CA) or as an authentication server. However, relying on a TTP implies that users do not directly manage their identities, but delegate this role to a third party. This intrinsic issue can generate trust concerns (e.g., identity theft), as well as privacy concerns towards the third party. The main objective of this research is to present an autonomous and independent solution where users can store their self created credentials without depending on TTPs. To this aim, the use of an TTP autonomous and independent network is needed, where users can manage and assess their identities themselves. In this paper, we propose the framework called Three Blockchains Identity Management with Elliptic Curve Cryptography (3BI-ECC). With our proposed framework, the users’ identities are self-generated and validated by their owners. Moreover, it allows the users to customize the information they want to share with third parties.

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A new matching algorithm between trees of shapes and its application to brain tumor segmentation

By Nicolas Boutry, Thierry Géraud

2021-03-02

In Proceedings of the IAPR international conference on discrete geometry and mathematical morphology (DGMM)

Abstract

Many approaches exist to compute the distance between two trees in pattern recognition. These trees can be structures with or without values on their nodes or edges. However, none of these distances take into account the shapes possibly associated to the nodes of the tree. For this reason, we propose in this paper a new distance between two trees of shapes based on the Hausdorff distance. This distance allows us to make inexact tree matching and to compute what we call residual trees, representing where two trees differ. We will also see that thanks to these residual trees, we can obtain good results in matter of brain tumor segmentation. This segmentation does not provide only a segmentation but also the tree of shapes corresponding to the segmentation and its depth map.

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An equivalence relation between morphological dynamics and persistent homology in $n$-D

By Nicolas Boutry, Thierry Géraud, Laurent Najman

2021-03-02

In Proceedings of the IAPR international conference on discrete geometry and mathematical morphology (DGMM)

Abstract

In Mathematical Morphology (MM), dynamics are used to compute markers to proceed for example to watershed-based image decomposition. At the same time, persistence is a concept coming from Persistent Homology (PH) and Morse Theory (MT) and represents the stability of the extrema of a Morse function. Since these concepts are similar on Morse functions, we studied their relationship and we found, and proved, that they are equal on 1D Morse functions. Here, we propose to extend this proof to $n$-D, $n \geq 2$, showing that this equality can be applied to $n$-D images and not only to 1D functions. This is a step further to show how much MM and MT are related.

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Stability of the tree of shapes to additive noise

By Nicolas Boutry, Guillaume Tochon

2021-03-02

In Proceedings of the IAPR international conference on discrete geometry and mathematical morphology (DGMM)

Abstract

The tree of shapes (ToS) is a famous self-dual hierarchical structure in mathematical morphology, which represents the inclusion relationship of the shapes (i.e. the interior of the level lines with holes filled) in a grayscale image. The ToS has already found numerous applications in image processing tasks, such as grain filtering, contour extraction, image simplification, and so on. Its structure consistency is bound to the cleanliness of the level lines, which are themselves deeply affected by the presence of noise within the image. However, according to our knowledge, no one has measured before how resistant to (additive) noise this hierarchical structure is. In this paper, we propose and compare several measures to evaluate the stability of the ToS structure to noise.

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Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

By Sharib Ali, Mariia Dmitrieva, Noha Ghatwary, Sophia Bano, Gorkem Polat, Alptekin Temizel, Adrian Krenzer, Amar Hekalo, Yun Bo Guo, Bogdan Matuszewski, Mourad Gridach, Irina Voiculescu, Vishnusai Yoganand, Arnav Chavan, Aryan Raj, Nhan T. Nguyen, Dat Q. Tran, Lê Duy Huỳnh, Nicolas Boutry, Shahadate Rezvy, Haijian Chen, Yoon Ho Choi, Anand Subramanian, Velmurugan Balasubramanian, Xiaohong W. Gao, Hongyu Hu, Yusheng Liao, Danail Stoyanov, Christian Daul, Stefano Realdon, Renato Cannizzaro, Dominique Lamarque, Terry Tran-Nguyen, Adam Bailey, Barbara Braden, James East, Jens Rittscher

2021-02-24

In Medical Image Analysis

Abstract

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques.

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Combining deep learning and mathematical morphology for historical map segmentation

By Yizi Chen, Edwin Carlinet, Joseph Chazalon, Clément Mallet, Bertrand Duménieu, Julien Perret

2021-02-16

In Proceedings of the IAPR international conference on discrete geometry and mathematical morphology (DGMM)

Abstract

The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through the time for subsequent thematic analysis. The goal of this work is the vectorization step, i.e., the extraction of vector shapes of the objects of interest from raster images of maps. We are particularly interested in closed shape detection such as buildings, building blocks, gardens, rivers, etc. in order to monitor their temporal evolution. Historical map images present significant pattern recognition challenges. The extraction of closed shapes by using traditional Mathematical Morphology (MM) is highly challenging due to the overlapping of multiple map features and texts. Moreover, state-of-the-art Convolutional Neural Networks (CNN) are perfectly designed for content image filtering but provide no guarantee about closed shape detection. Also, the lack of textural and color information of historical maps makes it hard for CNN to detect shapes that are represented by only their boundaries. Our contribution is a pipeline that combines the strengths of CNN (efficient edge detection and filtering) and MM (guaranteed extraction of closed shapes) in order to achieve such a task. The evaluation of our approach on a public dataset shows its effectiveness for extracting the closed boundaries of objects in historical maps.

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Going beyond p-convolutions to learn grayscale morphological operators

By Alexandre Kirszenberg, Guillaume Tochon, Élodie Puybareau, Jesus Angulo

2021-02-16

In Proceedings of the IAPR international conference on discrete geometry and mathematical morphology (DGMM)

Abstract

Integrating mathematical morphology operations within deep neural networks has been subject to increasing attention lately. However, replacing standard convolution layers with erosions or dilations is particularly challenging because the min and max operations are not differentiable. Relying on the asymptotic behavior of the counter-harmonic mean, p-convolutional layers were proposed as a possible workaround to this issue since they can perform pseudo-dilation or pseudo-erosion operations (depending on the value of their inner parameter p), and very promising results were reported. In this work, we present two new morphological layers based on the same principle as the p-convolutional layer while circumventing its principal drawbacks, and demonstrate their potential interest in further implementations within deep convolutional neural network architectures.

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On some associations between mathematical morphology and artificial intelligence

By Isabelle Bloch, Samy Blusseau, Ramón Pino Pérez, Élodie Puybareau, Guillaume Tochon

2021-02-16

In Proceedings of the IAPR international conference on discrete geometry and mathematical morphology (DGMM)

Abstract

This paper aims at providing an overview of the use of mathematical morphology, in its algebraic setting, in several fields of artificial intelligence (AI). Three domains of AI will be covered. In the first domain, mathematical morphology operators will be expressed in some logics (propositional, modal, description logics) to answer typical questions in knowledge representation and reasoning, such as revision, fusion, explanatory relations, satisfying usual postulates. In the second domain, spatial reasoning will benefit from spatial relations modeled using fuzzy sets and morphological operators, with applications in model-based image understanding. In the third domain, interactions between mathematical morphology and deep learning will be detailed. Morphological neural networks were introduced as an alternative to classical architectures, yielding a new geometry in decision surfaces. Deep networks were also trained to learn morphological operators and pipelines, and morphological algorithms were used as companion tools to machine learning, for pre/post processing or even regularization purposes. These ideas have known a large resurgence in the last few years and new ones are emerging.

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A machine learning based splitting heuristic for divide-and-conquer solvers

By Saeed Nejati, Ludovic Le Frioux, Vijay Ganesh

2020-12-31

In Proceedings of the 26 th international conference on principles and practice of constraint programming (CP’20)

Abstract

In this paper, we present a machine learning based splitting heuristic for divide-and-conquer parallel Boolean SAT solvers. Splitting heuristics, whether they are look-ahead or look-back, are designed using proxy metrics, which when optimized, approximate the true metric of minimizing solver runtime on sub-formulas resulting from a split. The rationale for such metrics is that they have been empirically shown to be excellent proxies for runtime of solvers, in addition to being cheap to compute in an online fashion. However, the design of traditional splitting methods are often ad-hoc and do not leverage the copious amounts of data that solvers generate. To address the above-mentioned issues, we propose a machine learning based splitting heuristic that leverages the features of input formulas and data generated during the run of a divide-and-conquer (DC) parallel solver. More precisely, we reformulate the splitting problem as a ranking problem and develop two machine learning models for pairwise ranking and computing the minimum ranked variable. Our model can compare variables according to their splitting quality, which is based on a set of features extracted from structural properties of the input formula, as well as dynamic probing statistics, collected during the solver’s run. We derive the true labels through offline collection of runtimes of a parallel DC solver on sample formulas and variables within them. At each splitting point, we generate a predicted ranking (pairwise or minimum rank) of candidate variables and split the formula on the top variable. We implemented our heuristic in the Painless parallel SAT framework and evaluated our solver on a set of cryptographic instances encoding the SHA-1 preimage as well as SAT competition 2018 and 2019 benchmarks. We solve significantly more instances compared to the baseline Painless solver and outperform top divide-and-conquer solvers from recent SAT competitions, such as Treengeling. Furthermore, we are much faster than these top solvers on cryptographic benchmarks.

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