Littérature scientifique sur le sujet « Hierarchical representations of images »
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Articles de revues sur le sujet "Hierarchical representations of images"
Abdelhack, Mohamed, et Yukiyasu Kamitani. « Sharpening of Hierarchical Visual Feature Representations of Blurred Images ». eneuro 5, no 3 (mai 2018) : ENEURO.0443–17.2018. http://dx.doi.org/10.1523/eneuro.0443-17.2018.
Texte intégralGao, Hongchao, Yujia Li, Jiao Dai, Xi Wang, Jizhong Han et Ruixuan Li. « Multi-granularity Deep Local Representations for Irregular Scene Text Recognition ». ACM/IMS Transactions on Data Science 2, no 2 (2 avril 2021) : 1–18. http://dx.doi.org/10.1145/3446971.
Texte intégralRamos Lima, Gustavo, Thiago Oliveira Santos, Patrick Marques Ciarelli et Filipe Mutz. « Comparação de Técnicas para Representação Vetorial de Imagens com Redes Neurais para Aplicações de Recuperação de Produtos do Varejo ». Anais do Computer on the Beach 14 (3 mai 2023) : 355–62. http://dx.doi.org/10.14210/cotb.v14.p355-362.
Texte intégralFerreira, João Elias Vidueira, et Gwendolyn Angela Lawrie. « Profiling the combinations of multiple representations used in large-class teaching : pathways to inclusive practices ». Chemistry Education Research and Practice 20, no 4 (2019) : 902–23. http://dx.doi.org/10.1039/c9rp00001a.
Texte intégralLiu, Hao, Bin Wang, Zhimin Bao, Mobai Xue, Sheng Kang, Deqiang Jiang, Yinsong Liu et Bo Ren. « Perceiving Stroke-Semantic Context : Hierarchical Contrastive Learning for Robust Scene Text Recognition ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 2 (28 juin 2022) : 1702–10. http://dx.doi.org/10.1609/aaai.v36i2.20062.
Texte intégralGazagnes, Simon, et Michael H. F. Wilkinson. « Distributed Component Forests in 2-D : Hierarchical Image Representations Suitable for Tera-Scale Images ». International Journal of Pattern Recognition and Artificial Intelligence 33, no 11 (octobre 2019) : 1940012. http://dx.doi.org/10.1142/s0218001419400123.
Texte intégralPAJAROLA, RENATO, MIGUEL SAINZ et YU MENG. « DMESH : FAST DEPTH-IMAGE MESHING AND WARPING ». International Journal of Image and Graphics 04, no 04 (octobre 2004) : 653–81. http://dx.doi.org/10.1142/s0219467804001580.
Texte intégralBai, Jie, Huiyan Jiang, Siqi Li et Xiaoqi Ma. « NHL Pathological Image Classification Based on Hierarchical Local Information and GoogLeNet-Based Representations ». BioMed Research International 2019 (21 mars 2019) : 1–13. http://dx.doi.org/10.1155/2019/1065652.
Texte intégralPham, Hai X., Ricardo Guerrero, Vladimir Pavlovic et Jiatong Li. « CHEF : Cross-modal Hierarchical Embeddings for Food Domain Retrieval ». Proceedings of the AAAI Conference on Artificial Intelligence 35, no 3 (18 mai 2021) : 2423–30. http://dx.doi.org/10.1609/aaai.v35i3.16343.
Texte intégralQiu, Zexuan, Jiahong Liu, Yankai Chen et Irwin King. « HiHPQ : Hierarchical Hyperbolic Product Quantization for Unsupervised Image Retrieval ». Proceedings of the AAAI Conference on Artificial Intelligence 38, no 5 (24 mars 2024) : 4614–22. http://dx.doi.org/10.1609/aaai.v38i5.28261.
Texte intégralThèses sur le sujet "Hierarchical representations of images"
Fehri, Amin. « Image Characterization by Morphological Hierarchical Representations ». Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEM063/document.
Texte intégralThis thesis deals with the extraction of hierarchical and multiscale descriptors on images, in order to interpret, characterize and segment them. It breaks down into two parts.The first part outlines a theoretical and methodological approach for obtaining hierarchical clusterings of the nodes of an edge-weighted graph. In addition, we introduce different approaches to combine hierarchical segmentations. These methods are then applied to graphs representing images and derive different hierarchical segmentation techniques. Finally, we propose a methodology for structuring and studying the space of hierarchies by using the Gromov-Hausdorff distance as a metric.The second part explores several applications of these hierarchical descriptions for images. We expose a method to learn how to automatically extract a segmentation of an image, given a type of images and a score of evaluation for a segmentation. We also propose image descriptors obtained by measuring inter-hierarchical distances, and expose their efficiency on real and simulated data. Finally, we extend the potential applications of these hierarchies by introducing a technique to take into account any spatial prior information during their construction
Cui, Yanwei. « Kernel-based learning on hierarchical image representations : applications to remote sensing data classification ». Thesis, Lorient, 2017. http://www.theses.fr/2017LORIS448/document.
Texte intégralHierarchical 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
Lagrange, Adrien. « From representation learning to thematic classification - Application to hierarchical analysis of hyperspectral images ». Thesis, Toulouse, INPT, 2019. http://www.theses.fr/2019INPT0095.
Texte intégralNumerous frameworks have been developed in order to analyze the increasing amount of available image data. Among those methods, supervised classification has received considerable attention leading to the development of state-of-the-art classification methods. These methods aim at inferring the class of each observation given a specific class nomenclature by exploiting a set of labeled observations. Thanks to extensive research efforts of the community, classification methods have become very efficient. Nevertheless, the results of a classification remains a highlevel interpretation of the scene since it only gives a single class to summarize all information in a given pixel. Contrary to classification methods, representation learning methods are model-based approaches designed especially to handle high-dimensional data and extract meaningful latent variables. By using physic-based models, these methods allow the user to extract very meaningful variables and get a very detailed interpretation of the considered image. The main objective of this thesis is to develop a unified framework for classification and representation learning. These two methods provide complementary approaches allowing to address the problem using a hierarchical modeling approach. The representation learning approach is used to build a low-level model of the data whereas classification is used to incorporate supervised information and may be seen as a high-level interpretation of the data. Two different paradigms, namely Bayesian models and optimization approaches, are explored to set up this hierarchical model. The proposed models are then tested in the specific context of hyperspectral imaging where the representation learning task is specified as a spectral unmixing problem
Xu, Zijian. « A hierarchical compositional model for representation and sketching of high-resolution human images ». Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1495960431&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Texte intégralHuynh, Lê Duy. « Taking into account inclusion and adjacency information in morphological hierarchical representations, with application to the extraction of text in natural images and videos ». Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS341.
Texte intégralThe inclusion and adjacency relationship between image regions usually carry contextual information. The later is widely used since it tells how regions are arranged in images. The former is usually not taken into account although it parallels the object-background relationship. The mathematical morphology framework provides several hierarchical image representations. They include the Tree of Shapes (ToS), which encodes the inclusion of level-line, and the hierarchies of segmentation (e.g., alpha-tree, BPT), which is useful in the analysis of the adjacency relationship. In this work, we take advantage of both inclusion and adjacency information in these representations for computer vision applications. We introduce the spatial alignment graph w.r.t inclusion that is constructed by adding a new adjacency relationship to nodes of the ToS. In a simple ToS such as our Tree of Shapes of Laplacian sign, which encodes the inclusion of Morphological Laplacian 0-crossings, the graph is reduced to a disconnected graph where each connected component is a semantic group. In other cases, e.g., classic ToS, the spatial alignment graph is more complex. To address this issue, we expand the shape-spaces morphology. Our expansion has two primary results: 1)It allows the manipulation of any graph of shapes. 2)It allows any tree filtering strategy proposed by the connected operators frameworks. With this expansion, the spatial graph could be analyzed with the help of an alpha-tree. We demonstrated the application aspect of our method in the application of text detection. The experiment results show the efficiency and effectiveness of our methods, which is appealing to mobile applications
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.
Texte intégralImage 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
Drumetz, Lucas. « Endmember Variability in hyperspectral image unmixing ». Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT075/document.
Texte intégralThe fine spectral resolution of hyperspectral remote sensing images allows an accurate analysis of the imaged scene, but due to their limited spatial resolution, a pixel acquired by the sensor is often a mixture of the contributions of several materials. Spectral unmixing aims at estimating the spectra of the pure materials (called endmembers) in the scene, and their abundances in each pixel. The endmembers are usually assumed to be perfectly represented by a single spectrum, which is wrong in practice since each material exhibits a significant intra-class variability. This thesis aims at designing unmixing algorithms to better handle this phenomenon. First, we perform the unmixing locally in well chosen regions of the image where variability effects are less important, and automatically discard wrongly estimated local endmembers using collaborative sparsity. In another approach, we refine the abundance estimation of the materials by taking into account the group structure of an image-derived endmember dictionary. Second, we introduce an extended linear mixing model, based on physical considerations, modeling spectral variability in the form of scaling factors, and develop optimization algorithms to estimate its parameters. This model provides easily interpretable results and outperforms other state-of-the-art approaches. We finally investigate two applications of this model to confirm its relevance
Yeh, Hur-jye. « 3-D reconstruction and image encoding using an efficient representation of hierarchical data structure / ». The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu148732651171353.
Texte intégralKeeter, Matthew (Matthew Joseph). « Hierarchical volumetric object representations for digital fabrication workflows ». Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82426.
Texte intégralCataloged from PDF version of thesis.
Includes bibliographical references (p. 111-114).
Modern systems for computer-aided design and manufacturing (CAD/CAM) have a history dating back to drafting boards, early computers, and machine shops with specialized technicians for each stage in a manufacturing workflow. In recent years, personal-scale digital fabrication has challenged many of these workflows' build-in assumptions. A single individual may control the entire workflow, from design to manufacture; they will be using computers that are exponentially more powerful than those in the 1970s; and they may be using a wide variety of tools, machines, and processes. The variety of tools and machines leads to a combinatorial explosion of possible workflows. In addition, tools are based on boundary representations, which are fragile and can easily describe nonsensical objects. This thesis addresses these issues with a set of tools for end-to-end digital fabrication based on volumetric solid models. Workflows are modular, making it easy to add new machines, and a shared core of path-planning operations reduces system complexity. Replacing boundary representations with volumetric representations guarantees that models represent reasonable real-world solids. Adaptively sampled distance fields are used as a generic interchange format. Functional representations are used as a design representation, and we examine scaling behavior and efficient rendering. We present interactive design tools that use these representations as their geometry engine. Data from CT scans is also used to populate these distance fields, showing significant benefits in file size and resolution compared to meshes. Finally, these representations are used as inputs to a modular multimachine CAM workflow. Toolpath generation is implemented, characterized, and tested on a complex solid model. We conclude with a summary of results and recommendations for future research directions.
by Matthew Keeter.
S.M.
Miflah, Hussain Ismail Ahamed. « Higher-level representations of natural images ». Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/39759.
Texte intégralLivres sur le sujet "Hierarchical representations of images"
Russell, Ian, dir. Images, Representations and Heritage. Boston, MA : Springer US, 2006. http://dx.doi.org/10.1007/0-387-32216-7.
Texte intégralMedia images and representations. Philadelphia : Chelsea House Publishers, 2006.
Trouver le texte intégralWar & trauma images in Vietnam war representations. Hildesheim : George Olms Verlag, 2008.
Trouver le texte intégralThe matter of images : Essays on representations. London : Routledge, 1993.
Trouver le texte intégralControversial images : Media representations on the edge. Houndmills, Basingstoke, Hampshire [England] : Palgrave Macmillan, 2013.
Trouver le texte intégralCreating sociological awareness : Collective images and symbolic representations. New Brunswick (U.S.A.) : Transaction Publishers, 1991.
Trouver le texte intégralRepresentations : Images of the world in Ciceronian oratory. Berkeley : University of California Press, 1993.
Trouver le texte intégralRüdiger, Görner, et University of London. Institute of Germanic & Romance Studies., dir. Images of words : Literary representations of pictorial themes. Munchen : Iudicium, 2005.
Trouver le texte intégralMike, Featherstone, et Wernick Andrew, dir. Images of aging : Cultural representations of later life. London : Routledge, 1995.
Trouver le texte intégralRepresentations and contradictions : Ambivalence towards images, theatre, fiction, relics, and sexuality. Oxford : Blackwell Publishers, 1997.
Trouver le texte intégralChapitres de livres sur le sujet "Hierarchical representations of images"
Díaz-del-Río, Fernando, Pablo Sanchez-Cuevas, Helena Molina-Abril, Pedro Real et María José Moron-Fernández. « Building Hierarchical Tree Representations Using Homological-Based Tools ». Dans Computer Analysis of Images and Patterns, 120–30. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89131-2_11.
Texte intégralYing-Lie, O., et Alexander Toet. « Mathematical Morphology in Hierarchical Image Representation ». Dans Medical Images : Formation, Handling and Evaluation, 445–60. Berlin, Heidelberg : Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77888-9_21.
Texte intégralGerstmayer, Michael, Yll Haxhimusa et Walter G. Kropatsch. « Hierarchical Interactive Image Segmentation Using Irregular Pyramids ». Dans Graph-Based Representations in Pattern Recognition, 245–54. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20844-7_25.
Texte intégralHøj, Benjamin J., et Andreas Møgelmose. « Synthesizing Hard Training Data from Latent Hierarchical Representations ». Dans Image Analysis, 49–58. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31438-4_4.
Texte intégralHidane, Moncef, Olivier Lézoray et Abderrahim Elmoataz. « Hierarchical Representation of Discrete Data on Graphs ». Dans Computer Analysis of Images and Patterns, 186–93. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23672-3_23.
Texte intégralCui, Yanwei, Laetitia Chapel et Sébastien Lefèvre. « A Subpath Kernel for Learning Hierarchical Image Representations ». Dans Graph-Based Representations in Pattern Recognition, 34–43. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18224-7_4.
Texte intégralHollander, Allan D., Frank W. Davis et David M. Stoms. « Hierarchical representations of species distributions using maps, images and sighting data ». Dans Mapping the Diversity of Nature, 71–88. Dordrecht : Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0719-8_5.
Texte intégralJin, Chuan, Anqi Zheng, Zhaoying Wu et Changqing Tong. « TransVQ-VAE : Generating Diverse Images Using Hierarchical Representation Learning ». Dans Artificial Neural Networks and Machine Learning – ICANN 2023, 185–96. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44213-1_16.
Texte intégralGe, Hongkun, Guorong Wu, Li Wang, Yaozong Gao et Dinggang Shen. « Hierarchical Multi-modal Image Registration by Learning Common Feature Representations ». Dans Machine Learning in Medical Imaging, 203–11. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24888-2_25.
Texte intégralBroelemann, Klaus, Anjan Dutta, Xiaoyi Jiang et Josep Lladós. « Hierarchical Graph Representation for Symbol Spotting in Graphical Document Images ». Dans Lecture Notes in Computer Science, 529–38. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34166-3_58.
Texte intégralActes de conférences sur le sujet "Hierarchical representations of images"
Zhang, Qiang, Xiuwen Liu et Anuj Srivastava. « Statistical Search for Hierarchical Linear Optimal Representations of Images ». Dans 2003 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW). IEEE, 2003. http://dx.doi.org/10.1109/cvprw.2003.10095.
Texte intégralMo, Guoliang, et Sanyuan Zhang. « Point Set Surfaces Representations Based on Hierarchical Geometry Images ». Dans 2006 International Multi-Symposiums on Computer and Computational Sciences (IMSCCS). IEEE, 2006. http://dx.doi.org/10.1109/imsccs.2006.105.
Texte intégralPotapov, Alexey S., et Olga S. Gamayunova. « Information criterion for constructing the hierarchical structural representations of images ». Dans Defense and Security, sous la direction de Firooz A. Sadjadi. SPIE, 2005. http://dx.doi.org/10.1117/12.602709.
Texte intégralYu, Chang, Xiangyu Zhu, Xiaomei Zhang, Zhaoxiang Zhang et Zhen Lei. « Graphics Capsule : Learning Hierarchical 3D Face Representations from 2D Images ». Dans 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.02010.
Texte intégralAlmeida, Raquel, Ewa Kijak, Simon Malinowski et Silvio Jamil F. Guimarães. « Learning on graphs and hierarchies ». Dans Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sibgrapi.est.2023.27449.
Texte intégralMa, Libo, et Liqing Zhang. « A Hierarchical Generative Model for Overcomplete Topographic Representations in Natural Images ». Dans International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371128.
Texte intégralYu, Feiwu, Xinxiao Wu, Yuchao Sun et Lixin Duan. « Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks ». Dans Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California : International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/154.
Texte intégralJia, Yiwei, Shiyong Huang, Xueming Li et Xianlin Zhang. « HFFR-SR : Hierarchical Fusion Feature Representations for Super Resolution of Old Images ». Dans ICCIP 2023 : 2023 the 9th International Conference on Communication and Information Processing. New York, NY, USA : ACM, 2023. http://dx.doi.org/10.1145/3638884.3638885.
Texte intégralAblameyko, Sergey V., Vladimir V. Bereishik, Nadeshda Paramonova, Angelo Marcelli et Sachiko Ishikawa. « Hierarchical vector representation of document images ». Dans Berlin - DL tentative, sous la direction de Rudy A. Mattheus, Andre J. Duerinckx et Peter J. van Otterloo. SPIE, 1993. http://dx.doi.org/10.1117/12.160483.
Texte intégralMaren, A. J., et M. Ali. « Hierarchical scene structure representations to facilitate image understanding ». Dans the first international conference. New York, New York, USA : ACM Press, 1988. http://dx.doi.org/10.1145/55674.55678.
Texte intégralRapports d'organisations sur le sujet "Hierarchical representations of images"
Lee, Chung-Nim, et Azriel Rosenfeld. Continuous Representations of Digital Images. Fort Belvoir, VA : Defense Technical Information Center, octobre 1985. http://dx.doi.org/10.21236/ada164189.
Texte intégralMunoz-Avila, Hector. Transfer Learning and Hierarchical Task Network Representations and Planning. Fort Belvoir, VA : Defense Technical Information Center, février 2008. http://dx.doi.org/10.21236/ada500020.
Texte intégralTadmor, Eitan, Suzanne Nezzar et Luminita Vese. Multiscale Hierarchical Decomposition of Images with Applications to Deblurring, Denoising and Segmentation. Fort Belvoir, VA : Defense Technical Information Center, novembre 2007. http://dx.doi.org/10.21236/ada489758.
Texte intégralJablonski, David. DTRT57-09-C-10046 Digital Imaging of Pipeline Mechanical Damage and Residual Stress. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), février 2010. http://dx.doi.org/10.55274/r0011872.
Texte intégralLey, Matt, Tom Baldvins, Hannah Pilkington, David Jones et Kelly Anderson. Vegetation classification and mapping project : Big Thicket National Preserve. National Park Service, 2024. http://dx.doi.org/10.36967/2299254.
Texte intégralLey, Matt, Tom Baldvins, David Jones, Hanna Pilkington et Kelly Anderson. Vegetation classification and mapping : Gulf Islands National Seashore. National Park Service, mai 2023. http://dx.doi.org/10.36967/2299028.
Texte intégralZerla, Pauline. Trauma, Violence Prevention, and Reintegration : Learning from Youth Conflict Narratives in the Central African Republic. RESOLVE Network, février 2024. http://dx.doi.org/10.37805/lpbi2024.1.
Texte intégralYan, Yujie, et Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, mai 2021. http://dx.doi.org/10.17760/d20410114.
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