Auswahl der wissenschaftlichen Literatur zum Thema „Représentations hiérarchiques d'images“
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Dissertationen zum Thema "Représentations hiérarchiques d'images"
Morand, Claire. „Segmentation spatio-temporelle et indexation vidéo dans le domaine des représentations hiérarchiques“. Thesis, Bordeaux 1, 2009. http://www.theses.fr/2009BOR13888/document.
Der volle Inhalt der QuelleThis thesis aims at proposing a solution of scalable object-based indexing of HD video flow compressed by MJPEG2000. In this context, on the one hand, we work in the hierarchical transform domain of the 9/7 Daubechies' wavelets and, on the other hand, the scalable representation implies to search for multiscale methods, from low to high resolution. The first part of this manuscript is dedicated to the definition of a method for automatic extraction of objects having their own motion. It is based on a combination of a robust global motion estimation with a morphological color segmentation at low resolution. The obtained result is then refined following the data order of the scalable flow. The second part is the definition of an object descriptor which is based on the multiscale histograms of the wavelet coefficients. Finally, the performances of the proposed method are evaluated in the context of scalable content-based queries
Thome, Nicolas. „Représentations hiérarchiques et discriminantes pour la reconnaissance des formes, l'identification des personnes et l'analyse des mouvements dans les séquences d'images“. Lyon 2, 2007. http://theses.univ-lyon2.fr/sdx/theses/lyon2/2007/thome_n.
Der volle Inhalt der QuelleThe field of human motion analysis by means of computer vision technologies has known a huge increase over the last decade. We propose here to tackle the problem in the following aspects : Licence Plate Recognition (LPR) and Human Motion Analysis. The proposed approach for LPR decomposes into the following steps: plate detection, binarization, character segmentation, shape classification and temporal averaging. Concerning Human Motion Analysis, our approach can be considered intermediate with respect to the Top Down and Bottom Up classes, When the tracking can be successfully achieved by a simple region association strategy, we analyse the shape silhouette at a finer scale, aiming at detecting and labelling body parts. The body parts labelling makes it possible to update an appearance model for each limb, capturing shape, color and texture properties. The appearance model is used in difficult situations to identify people. Concerning human motion analysis, we propose an approach dedicated to fall detection, whose contribution decomposes into two points. Firstly, we propose to analyse the relevance of a proposed verticality detector, computed from an image feature and that devoted to discriminating standing from lying poses. The second contribution corresponds to the application of a Hierarchical Hidden Markov Model (HHMM) to classify the pose sequences. The different proposed approaches for License Plate recognition or human motion analysis have proved the efficiency of Bottom Up approaches for a real time purpose, and pointed out the importance of generatives back propagations to revolve ambiguous situations
Soler, Cyril. „Représentations hiérarchiques de la visibilité pour le contrôle de l'erreur en simulation de l'éclairage“. Phd thesis, Université Joseph Fourier (Grenoble), 1998. http://tel.archives-ouvertes.fr/tel-00004905.
Der volle Inhalt der QuelleLaporterie, Florence. „Représentations hiérarchiques d'images avec des pyramides morphologiques : application à l'analyse et à la fusion spatio-temporelle de données en observation de la Terre“. Toulouse, ENSAE, 2002. http://www.theses.fr/2002ESAE0001.
Der volle Inhalt der QuelleRandrianasoa, Tianatahina Jimmy Francky. „Représentation d'images hiérarchique multi-critère“. Thesis, Reims, 2017. http://www.theses.fr/2017REIMS040/document.
Der volle Inhalt der QuelleSegmentation is a crucial task in image analysis. Novel acquisition devices bring new images with higher resolutions, containing more heterogeneous objects. It becomes also easier to get many images of an area from different sources. This phenomenon is encountered in many domains (e.g. remote sensing, medical imaging) making difficult the use of classical image segmentation methods. Hierarchical segmentation approaches provide solutions to such issues. Particularly, the Binary Partition Tree (BPT) is a hierarchical data-structure modeling an image content at different scales. It is built in a mono-feature way (i.e. one image, one metric) by merging progressively similar connected regions. However, the metric has to be carefully thought by the user and the handling of several images is generally dealt with by gathering multiple information provided by various spectral bands into a single metric. Our first contribution is a generalized framework for the BPT construction in a multi-feature way. It relies on a strategy setting up a consensus between many metrics, allowing us to obtain a unified hierarchical segmentation space. Surprisingly, few works were devoted to the evaluation of hierarchical structures. Our second contribution is a framework for evaluating the quality of BPTs relying both on intrinsic and extrinsic quality analysis based on ground-truth examples. We also discuss about the use of this evaluation framework both for evaluating the quality of a given BPT and for determining which BPT should be built for a given application. Experiments using satellite images emphasize the relevance of the proposed frameworks in the context of image segmentation
Bosilj, Petra. „Image indexing and retrieval using component trees“. Thesis, Lorient, 2016. http://www.theses.fr/2016LORIS396/document.
Der volle Inhalt der QuelleThis thesis explores component trees, hierarchical structures from Mathematical Morphology, and their application to image retrieval and related tasks. The distinct component trees are analyzed and a novel classification into two superclasses is proposed, as well as a contribution to indexing and representation of the hierarchies using dendrograms. The first contribution to the field of image retrieval is in developing a novel feature detector, built upon the well-established MSER detection. The tree-based implementation of the MSER detector allows for changing the underlying tree in order to produce features of different stability properties. This resulted in the Tree of Shapes based Maximally Stable Region detector, leading to improvements over MSER in retrieval performance. Focusing on feature description, we extend the concept of 2D pattern spectra and adapt their global variant to more powerful, local schemes. Computed on the components of Min/Max-tree, they are histograms holding the information on distribution of image region attributes. The rotation and translation invariance is preserved from the global descriptor, while special attention is given to achieving scale invariance. We report comparable results to SIFT in image classification, as well as outperforming Morphology-based descriptors in satellite image retrieval, with a descriptor shorter than SIFT. Finally, a preprocessing or simplification technique for component trees is also presented, allowing the user to reevaluate the measures of region level of aggregation imposed on a component tree. The thesis is concluded by outlining the future perspectives based on the content of the thesis
Tochon, Guillaume. „Analyse hiérarchique d'images multimodales“. Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT100/document.
Der volle Inhalt der QuelleThere is a growing interest in the development of adapted processing tools for multimodal images (several images acquired over the same scene with different characteristics). Allowing a more complete description of the scene, multimodal images are of interest in various image processing fields, but their optimal handling and exploitation raise several issues. This thesis extends hierarchical representations, a powerful tool for classical image analysis and processing, to multimodal images in order to better exploit the additional information brought by the multimodality and improve classical image processing techniques. %when applied to real applications. This thesis focuses on three different multimodalities frequently encountered in the remote sensing field. We first investigate the spectral-spatial information of hyperspectral images. Based on an adapted construction and processing of the hierarchical representation, we derive a segmentation which is optimal with respect to the spectral unmixing operation. We then focus on the temporal multimodality and sequences of hyperspectral images. Using the hierarchical representation of the frames in the sequence, we propose a new method to achieve object tracking and apply it to chemical gas plume tracking in thermal infrared hyperspectral video sequences. Finally, we study the sensorial multimodality, being images acquired with different sensors. Relying on the concept of braids of partitions, we propose a novel methodology of image segmentation, based on an energetic minimization framework
Esteban, Baptiste. „A Generic, Efficient, and Interactive Approach to Image Processing with Applications in Mathematical Morphology“. Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS623.
Der volle Inhalt der QuelleImage processing libraries play an important role in the researcher toolset and should respect three criteria: genericity, performance, and interactivity. In short, genericity boosts code reuse and algorithm flexibility for various data inputs, while performance speeds up experiments and supports real-time applications. Additionally, interactivity allows software evolution and maintenance without full recompilation, often through integration with dynamic languages like Python or Julia. The first two criteria are not straightforward to reach with static languages such as C++ or Rust which require knowing some information at compile time to optimize generated machine code related to the different input and output data types of an algorithm. The latest criterion usually requires waiting until runtime to obtain type information and is thus performed at the cost of runtime efficiency. The work presented in this thesis aims to go beyond this limitation in the context of image processing algorithms. To do so, a methodology to develop generic algorithms whose type information about its input and output data may be known either at compile-time or at runtime is presented. This methodology is evaluated on different image processing algorithmic schemes, and it is concluded that the performance gap between the runtime and compile-time versions of the construction algorithm for hierarchical representations of images is negligible. As an application, hierarchical representations are employed to expand the applicability of grayscale noise level estimation to color images to enhance its genericity. That raises the importance of studying the impact of such corruption in the hierarchies built on noisy images to improve their efficiency in the presence of noise. It is demonstrated that the noise has an impact on the tree structure, and this impact is related to some kinds of functional in the context of energy optimization on hierarchies
Cui, Yanwei. „Kernel-based learning on hierarchical image representations : applications to remote sensing data classification“. Thesis, Lorient, 2017. http://www.theses.fr/2017LORIS448/document.
Der volle Inhalt der QuelleHierarchical image representations have been widely used in the image classification context. Such representations are capable of modeling the content of an image through a tree structure. In this thesis, we investigate kernel-based strategies that make possible taking input data in a structured form and capturing the topological patterns inside each structure through designing structured kernels. We develop a structured kernel dedicated to unordered tree and path (sequence of nodes) structures equipped with numerical features, called Bag of Subpaths Kernel (BoSK). It is formed by summing up kernels computed on subpaths (a bag of all paths and single nodes) between two bags. The direct computation of BoSK yields a quadratic complexity w.r.t. both structure size (number of nodes) and amount of data (training size). We also propose a scalable version of BoSK (SBoSK for short), using Random Fourier Features technique to map the structured data in a randomized finite-dimensional Euclidean space, where inner product of the transformed feature vector approximates BoSK. It brings down the complexity from quadratic to linear w.r.t. structure size and amount of data, making the kernel compliant with the large-scale machine-learning context. Thanks to (S)BoSK, we are able to learn from cross-scale patterns in hierarchical image representations. (S)BoSK operates on paths, thus allowing modeling the context of a pixel (leaf of the hierarchical representation) through its ancestor regions at multiple scales. Such a model is used within pixel-based image classification. (S)BoSK also works on trees, making the kernel able to capture the composition of an object (top of the hierarchical representation) and the topological relationships among its subparts. This strategy allows tile/sub-image classification. Further relying on (S)BoSK, we introduce a novel multi-source classification approach that performs classification directly from a hierarchical image representation built from two images of the same scene taken at different resolutions, possibly with different modalities. Evaluations on several publicly available remote sensing datasets illustrate the superiority of (S)BoSK compared to state-of-the-art methods in terms of classification accuracy, and experiments on an urban classification task show the effectiveness of proposed multi-source classification approach
Demaret, Laurent. „Etude de la scalabilité et de la représentation d'images fixes par maillages hiérarchiques exploitant les éléments finis et les ondelettes bidimensionnelles. Application au codage vidéo“. Rennes 1, 2002. http://www.theses.fr/2002REN10147.
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