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Contributions to graph-based hierarchical analysis for images and 3D point clouds

Abstract : Graphs are powerful mathematical structures representing a set of objects and the underlying links between pairs of objects somehow related. They are becoming increasingly popular in data science in general and in particular in image or 3D point cloud analysis. Among the wide spectra of applications, they are involved in most of the hierarchical approaches.Hierarchies are particularly important because they allow us to efficiently organize the information required and to analyze the problems at different levels of detail. In this thesis, we address the following topics. Many morphological hierarchical approaches rely on the Minimum Spanning Tree (MST). We propose an algorithm for MST computation in streaming based on a graph decomposition strategy. Thanks to this decomposition, larger images can be processed or can benefit from partial reliable information while the whole image is not completely available.Recent LiDAR developments are able to acquire large-scale and precise 3D point clouds. Many applications, such as infrastructure monitoring, urban planning, autonomous driving, precision forestry, environmental assessment, archaeological discoveries, to cite a few, are under development nowadays. We introduce a ground detection algorithm and compare it with the state of the art. The impact of reducing the point cloud density with low-cost scanners is studied, in the context of an autonomous driving application. Finally, in many hierarchical methods similarities between points are given as input. However, the metric used to compute similarities influences the quality of the final results. We exploit metric learning as a complementary tool that helps to improve the quality of hierarchies. We demonstrate the capabilities of these methods in two contexts. The first one,a texture classification of 3D surfaces. Our approach ranked second in a task organized by SHREC’20 international challenge. The second one learning the similarity function together with the optimal hierarchical clustering, in a continuous feature-based hierarchical clustering formulation.
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Submitted on : Wednesday, January 5, 2022 - 12:22:08 PM
Last modification on : Thursday, April 28, 2022 - 11:08:32 AM
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  • HAL Id : tel-03512298, version 1


Leonardo Gigli. Contributions to graph-based hierarchical analysis for images and 3D point clouds. Image Processing [eess.IV]. Université Paris sciences et lettres, 2021. English. ⟨NNT : 2021UPSLM029⟩. ⟨tel-03512298⟩



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