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University of Strasbourg, France

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6 Papers

Deep Neural Network Architectures for Advanced Hiking Map Generation

Topics: Deep Learning and Neural Networks; Data Mining and Algorithms for Big Data

Authors: Olivier Schirm ¹ , Maxime Devanne ² , Jonathan Weber ² , Arnaud Lecus ¹ , Germain Forestier ² and Cédric Wemmert ³

Affiliations: ¹ Visorando, Soultz-Haut-Rhin, France , ² IRIMAS, University of Haute Alsace, Mulhouse, France , ³ ICube, University of Strasbourg, Strasbourg, France

Digital Object Identifier: 10.5220/0013351700003905

Proceedings of ICPRAM 2025, ISBN: 978-989-758-730-6

Quick Abstract

The automation of hiking map generation using deep learning represents a pivotal advancement in geospatial analysis. This study investigates the application of neural network architectures to derive accurate hiking maps from GPS trajectory data, exclusively collected via the Visorando mobile application. By exploring the utility of 17 distinct raster features derived from geospatial data, we identify the heatmap as the most effective input for mapping intricate trail networks, achieving superior performance across accuracy, segmentation, and connectivity metrics. Among various architectures evaluated, HRNet emerged as the most efficient model, demonstrating exceptional results when combined with optimal input features, significantly outperforming state-of-the-art approaches in intersection detection and trail segmentation. This research introduces a novel framework for converting vector-based GPS traces into rasterized data suitable for convolutional neural networks, overcoming challenges like noisy inputs and terrain variability. The findings establish a new benchmark for efficiency and accuracy in hiking map generation, offering valuable insights for the broader field of automated map inference reliant on GPS data. Future work will explore direct spatio-temporal processing of vector data, eliminating raster conversion for enhanced scalability and precision.

Ensemble Clustering for Histopathological Images Segmentation using Convolutional Autoencoders

Topics: Medical Image Applications; Deep Learning for Visual Understanding

Authors: Ilias Rmouque ¹ , Maxime Devanne ² , Jonathan Weber ² , Germain Forestier ² and Cédric Wemmert ¹

Affiliations: ¹ ICube, University of Strasbourg, France , ² IRIMAS, University of Haute-Alsace, France

Digital Object Identifier: 10.5220/0010835300003124

Proceedings of VISAPP 2022/VISIGRAPP, ISBN: 978-989-758-555-5

Quick Abstract

Unsupervised deep learning using autoencoders has shown excellent results in image analysis and computer vision. However, only few studies have been presented in the field of digital pathology, where proper labelling of the objects of interest is a particularly costly and difficult task. Thus, having a first fully unsupervised segmentation could greatly help in the analysis process of such images. In this paper, many architectures of convolutional autoencoders have been compared to study the influence of three main hyperparameters: (1) number of convolutional layers, (2) number of convolutions in each layer and (3) size of the latent space. Different clustering algorithms are also compared and we propose a new way to obtain more precise results by applying ensemble clustering techniques which consists in combining multiple clustering results.

Angular Uncertainty Refinement and Image Reconstruction Improvement in Cryo-electron Tomography

Topics: Image Enhancement and Restoration; Early and Biologically-Inspired Vision; Medical Image Applications

Authors: Hmida Rojbani ¹ , Étienne Baudrier ² , Benoît Naegel ² , Loïc Mazo ² and Atef Hamouda ³

Affiliations: ¹ University of Strasbourg and University El-Manar, France , ² University of Strasbourg, France , ³ University El-Manar, Tunisia

Digital Object Identifier: 10.5220/0005680600940100

Proceedings of VISAPP 2016/VISIGRAPP, ISBN: 978-989-758-175-5

Quick Abstract

In the field of cryo-electron tomography (cryo-ET), numerous approaches have been proposed to tackle the difficulties of the three-dimensional reconstruction problem. And that, in order to cope with (1) the missing and noisy data from the collected projections, (2) errors in projection images due to acquisition problems, (3) the capacity of processing large data sets and parameterizing the contrast function of the electron microscopy. In this paper, we present a novel approach for dealing with angular uncertainty in cryo-ET. To accomplish this task we propose a cost function and with the use of the nonlinear version of the optimization algorithm called Conjugate Gradient, we minimize it. We test the efficiency of our algorithm with both simulated and real data.

Related Ontology Subjects/Areas/Topics (7)

Applications and Services	Image and Video Analysis	Image Formation and Preprocessing
Computer Vision, Visualization and Computer Graphics	Image Enhancement and Restoration	Medical Image Applications
Early and Biologically-Inspired Vision

A Fuzzy-Rule Based Ontology for Urban Object Recognition

Topics: Applications and Case-Studies; Domain Analysis and Modeling

Authors: Stella Marc-Zwecker , Khalid Asnoune and Cédric Wemmert

Affiliation: University of Strasbourg and CNRS, France

Digital Object Identifier: 10.5220/0005026601530160

Proceedings of KEOD 2014/IC3K, ISBN: 978-989-758-049-9

Quick Abstract

In this paper we outline the principles of a methodology for semi-automatic recognition of urban objects from satellite images. The methodology aims to provide a framework for bridging the semantic gap problem. Its principle consists in linking abstract geographical domain concepts with image segments, by the means of ontologies use. The imprecision of image data and of qualitative rules formulated by experts geographers are handled by fuzzy logic mechanisms. We have defined fuzzy rules, implemented in SWRL (Semantic Web Rule Language), which allow classification of image segments in the ontology. We propose some fuzzy classification strategies, which are compared and evaluated through an experimentation performed on a VHR image of Strasbourg region.

Related Ontology Subjects/Areas/Topics (6)

Applications and Case-studies		Domain Analysis and Modeling		Knowledge-Based Systems
Artificial Intelligence		Knowledge Engineering and Ontology Development		Symbolic Systems

Fast Segmentation for Texture-based Cartography of whole Slide Images

Topics: Segmentation and Grouping; Color and Texture Analyses; Medical Image Applications

Authors: Grégory Apou ¹ , Benoît Naegel ¹ , Germain Forestier ² , Friedrich Feuerhake ³ and Cédric Wemmert ¹

Affiliations: ¹ University of Strasbourg, France , ² University of Haute Alsace, France , ³ Hannover Medical School, Germany

Digital Object Identifier: 10.5220/0004687403090319

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-003-1

Quick Abstract

In recent years, new optical microscopes have been developed, providing very high spatial resolution images called Whole Slide Images (WSI). The fast and accurate display of such images for visual analysis by pathologists and the conventional automated analysis remain challenging, mainly due to the image size (sometimes billions of pixels) and the need to analyze certain image features at high resolution. To propose a decision support tool to help the pathologist interpret the information contained by the WSI, we present a new approach to establish an automatic cartography of WSI in reasonable time. The method is based on an original segmentation algorithm and on a supervised multiclass classification using a textural characterization of the regions computed by the segmentation. Application to breast cancer WSI shows promising results in terms of speed and quality.

Related Ontology Subjects/Areas/Topics (6)

Applications and Services		Computer Vision, Visualization and Computer Graphics		Medical Image Applications
Color and Texture Analyses		Image and Video Analysis		Segmentation and Grouping

MULTI-SEMANTIC APPROACH TOWARDS A GENERIC FORMAL SOLVER OF TOOL PLACEMENT FOR PERCUTANEOUS SURGERY

Topic: Intelligent Problem Solving

Authors: Caroline Essert-Villard , Claire Baegert and Pascal Schreck

Affiliation: Université de Strasbourg, France

Digital Object Identifier: 10.5220/0002303804430446

Proceedings of KEOD 2009/IC3K, ISBN: 978-989-674-012-2

Quick Abstract

In this paper, we study the multiple points of view when generalizing a method based on many criteria optimization, in the framework of percutaneous surgery planning. The aim of the prototype is to find an optimal position of surgical tools to help the surgeons in planning the intervention. We explain how, with a formal geometric solver and meta programming, we intend to build a modular tool, capable of being extended to new interventions, with few programming efforts.

Related Ontology Subjects/Areas/Topics (7)

Artificial Intelligence	Knowledge Engineering and Ontology Development	Software Engineering
Enterprise Software Technologies	Knowledge-Based Systems	Symbolic Systems
Intelligent Problem Solving

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