Relevant bibliographies by topics / Hierarchical representations of images

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Hierarchical representations of images'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Hierarchical representations of images"

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Abdelhack, Mohamed, and Yukiyasu Kamitani. "Sharpening of Hierarchical Visual Feature Representations of Blurred Images." eneuro 5, no. 3 (2018): ENEURO.0443–17.2018. http://dx.doi.org/10.1523/eneuro.0443-17.2018.

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Gao, Hongchao, Yujia Li, Jiao Dai, Xi Wang, Jizhong Han, and Ruixuan Li. "Multi-granularity Deep Local Representations for Irregular Scene Text Recognition." ACM/IMS Transactions on Data Science 2, no. 2 (2021): 1–18. http://dx.doi.org/10.1145/3446971.

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Recognizing irregular text from natural scene images is challenging due to the unconstrained appearance of text, such as curvature, orientation, and distortion. Recent recognition networks regard this task as a text sequence labeling problem and most networks capture the sequence only from a single-granularity visual representation, which to some extent limits the performance of recognition. In this article, we propose a hierarchical attention network to capture multi-granularity deep local representations for recognizing irregular scene text. It consists of several hierarchical attention bloc
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Ramos Lima, Gustavo, Thiago Oliveira Santos, Patrick Marques Ciarelli, and 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 (May 3, 2023): 355–62. http://dx.doi.org/10.14210/cotb.v14.p355-362.

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ABSTRACTProduct retrieval from images has multiple applications rangingfrom providing information and recommentations for customersin supermarkets to automatic invoice generation in smart stores.However, this task present important challenges such as the largenumber of products, the scarcity of images of items, differencesbetween real and iconic images of the products, and the constantchanges in the portfolio due to the addition or removal of products.Hence, this work investigates ways of generating vector representationsof images using deep neural networks such that theserepresentations can b
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Ferreira, João Elias Vidueira, and 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.

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Teachers select multiple representations and adopt multiple visualization approaches in supporting their students to make meaning of chemical phenomena. Representational competence underpins students’ construction of their mental models of concepts thus it is important that teachers consider this while developing instructional resources. In tertiary chemistry, teachers typically use PowerPoint slides to guide lectures. This instructional resource is transferred between different teachers each semester and, while the sequence of topics are likely to be discussed and agreed upon, the content of
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Liu, Tao, Rongjie Li, Chongyu Wang, and Xuming He. "Relation-aware Hierarchical Prompt for Open-vocabulary Scene Graph Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 5 (2025): 5576–84. https://doi.org/10.1609/aaai.v39i5.32594.

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Open-vocabulary Scene Graph Generation (OV-SGG) overcomes the limitations of the closed-set assumption by aligning visual relationship representations with open-vocabulary textual representations. This enables the identification of novel visual relationships, making it applicable to real-world scenarios with diverse relationships. However, existing OV-SGG methods are constrained by fixed text representations, limiting diversity and accuracy in image-text alignment. To address these challenges, we propose the Relation-Aware Hierarchical Prompting (RAHP) framework, which enhances text representa
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Liu, Hao, Bin Wang, Zhimin Bao, et al. "Perceiving Stroke-Semantic Context: Hierarchical Contrastive Learning for Robust Scene Text Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1702–10. http://dx.doi.org/10.1609/aaai.v36i2.20062.

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We introduce Perceiving Stroke-Semantic Context (PerSec), a new approach to self-supervised representation learning tailored for Scene Text Recognition (STR) task. Considering scene text images carry both visual and semantic properties, we equip our PerSec with dual context perceivers which can contrast and learn latent representations from low-level stroke and high-level semantic contextual spaces simultaneously via hierarchical contrastive learning on unlabeled text image data. Experiments in un- and semi-supervised learning settings on STR benchmarks demonstrate our proposed framework can y
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Darsan Mukhopadhyay, Nilav. "Empirical Evaluation of Convolutional Neural Networks for Denoising Images." African Journal of Biological Sciences 6, no. 13 (2024): 751–65. http://dx.doi.org/10.48047/afjbs.6.13.2024.751-765.

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Noise removal from images, or image denoising, is a critical task in computer vision, aiming to enhance image quality by suppressing unwanted random variations while preserving essential features. Convolutional Neural Networks (CNNs) have emerged as a powerful tool for this purpose, leveraging their ability to learn hierarchical representations of data.
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Liu, Weijun. "HFDANet: A Hierarchical Feature and Dual-Branch Adaptive Refinement Network for Salient Object Detection in Optical Remote Sensing Images." Journal of Physics: Conference Series 3055, no. 1 (2025): 012027. https://doi.org/10.1088/1742-6596/3055/1/012027.

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Abstract The complex background and scale variations of the same objects pose significant challenges, making them difficult to recognize in an ORSI image. To enhance feature representation capability and achieve refined object localization, this paper proposes an innovative network, HFDANet (Hierarchical Feature and Dual-Branch Adaptive Refinement Network), which effectively addresses these issues. The core of HFDANet is the DARE (Dual-Branch Adaptive Refinement Engine), complemented by the HFAIM (Hierarchical Feature Aggregation Integration Module). HFAIM integrates hierarchical feature fusio
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PAJAROLA, RENATO, MIGUEL SAINZ, and YU MENG. "DMESH: FAST DEPTH-IMAGE MESHING AND WARPING." International Journal of Image and Graphics 04, no. 04 (2004): 653–81. http://dx.doi.org/10.1142/s0219467804001580.

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In this paper we present a novel and efficient depth-image representation and warping technique called DMesh which is based on a piece-wise linear approximation of the depth-image as a textured and simplified triangle mesh. We describe the application of a hierarchical multiresolution triangulation method to generate adaptively triangulated depth-meshes efficiently from reference depth-images, discuss depth-mesh segmentation methods to avoid occlusion artifacts and propose a new hardware accelerated depth-image rendering technique that supports per-pixel weighted blending of multiple depth-ima
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Zhang, Guangyi, Hanlei Li, Yunlong Cai, Qiyu Hu, Guanding Yu, and Runmin Zhang. "Learned Image Transmission with Hierarchical Variational Autoencoder." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 13215–23. https://doi.org/10.1609/aaai.v39i12.33442.

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In this paper, we introduce an innovative hierarchical joint source-channel coding (HJSCC) framework for image transmission, utilizing a hierarchical variational autoencoder (VAE). Our approach leverages a combination of bottom-up and top-down paths at the transmitter to autoregressively generate multiple hierarchical representations of the original image. These representations are then directly mapped to channel symbols for transmission by the JSCC encoder. We extend this framework to scenarios with a feedback link, modeling transmission over a noisy channel as a probabilistic sampling proces
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Dissertations / Theses on the topic "Hierarchical representations of images"

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Fehri, Amin. "Image Characterization by Morphological Hierarchical Representations." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEM063/document.

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Cette thèse porte sur l'extraction de descripteurs hiérarchiques et multi-échelles d'images, en vue de leur interprétation, caractérisation et segmentation. Elle se décompose en deux parties.La première partie expose des éléments théoriques et méthodologiques sur l'obtention de classifications hiérarchiques des nœuds d'un graphe valué aux arêtes. Ces méthodes sont ensuite appliquées à des graphes représentant des images pour obtenir différentes méthodes de segmentation hiérarchique d'images. De plus, nous introduisons différentes façons de combiner des segmentations hiérarchiques. Nous proposo
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Cui, Yanwei. "Kernel-based learning on hierarchical image representations : applications to remote sensing data classification." Thesis, Lorient, 2017. http://www.theses.fr/2017LORIS448/document.

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La représentation d’image sous une forme hiérarchique a été largement utilisée dans un contexte de classification. Une telle représentation est capable de modéliser le contenu d’une image à travers une structure arborescente. Dans cette thèse, nous étudions les méthodes à noyaux qui permettent de prendre en entrée des données sous une forme structurée et de tenir compte des informations topologiques présentes dans chaque structure en concevant des noyaux structurés. Nous présentons un noyau structuré dédié aux structures telles que des arbres non ordonnés et des chemins (séquences de noeuds) é
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Lagrange, Adrien. "From representation learning to thematic classification - Application to hierarchical analysis of hyperspectral images." Thesis, Toulouse, INPT, 2019. http://www.theses.fr/2019INPT0095.

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De nombreuses approches ont été développées pour analyser la quantité croissante de donnée image disponible. Parmi ces méthodes, la classification supervisée a fait l'objet d'une attention particulière, ce qui a conduit à la mise au point de méthodes de classification efficaces. Ces méthodes visent à déduire la classe de chaque observation en se basant sur une nomenclature de classes prédéfinie et en exploitant un ensemble d'observations étiquetées par des experts. Grâce aux importants efforts de recherche de la communauté, les méthodes de classification sont devenues très précises. Néanmoins,
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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.

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Huynh, 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.

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Les relations d'inclusion et d'adjacence des regions dans l'images comportent des informations contextuelles. Le relation d'adjacence est largement utilisé car il indique comment les régions sont organisées dans l'images. La relation d'inclusion n'est généralement pas prise en compte, bien qu'il assimile la relation d'objet-fond. Il existe plusieurs représentations morphologiques hiérarchiques: l'arbre des formes (AdF) qui représentent l'inclusion de lignes de niveaux d'image, ainsi que les hiérarchies de segmentation (i.e. la hiérarchie des quasi-zones plates) qui est utile dans l'analyse de
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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.

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Les bibliothèques de traitement d’images jouent un rôle important dans la boîte à outils du chercheur et devraient respecter trois critères : généricité, performance et interactivité. La généricité favorise la réutilisation du code et la flexibilité des algorithmes pour diverses structures de données en entrée, tandis que la performance accélère les expériences et permet l’utilisation d’algorithmes dans le cas d’applications en temps réel. De plus, l’interactivité dans la chaîne de traitement d’une image permet d’effectuer des expérimentations en échangeant des données avec cette dernière. Ce
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Drumetz, Lucas. "Endmember Variability in hyperspectral image unmixing." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT075/document.

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La finesse de la résolution spectrale des images hyperspectrales en télédétection permet une analyse précise de la scène observée, mais leur résolution spatiale est limitée, et un pixel acquis par le capteur est souvent un mélange des contributions de différents matériaux. Le démélange spectral permet d'estimer les spectres des matériaux purs (endmembers) de la scène, et leurs abondances dans chaque pixel. Les endmembers sont souvent supposés être parfaitement représentés par un seul spectre, une hypothèse fausse en pratique, chaque matériau ayant une variabilité intra-classe non négligeable.
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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.

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Keeter, Matthew (Matthew Joseph). "Hierarchical volumetric object representations for digital fabrication workflows." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82426.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 111-114).<br>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
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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.

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The traditional view of vision is that neurons in early cortical areas process information about simple features (e.g. orientation and spatial frequency) in small, spatially localised regions of visual space (the neuron's receptive field). This piecemeal information is then fed-forward into later stages of the visual system where it gets combined to form coherent and meaningful global (higher-level) representations. The overall aim of this thesis is to examine and quantify this higher level processing; how we encode global features in natural images and to understand the extent to which our pe
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Books on the topic "Hierarchical representations of images"

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Russell, Ian, ed. Images, Representations and Heritage. Springer US, 2006. http://dx.doi.org/10.1007/0-387-32216-7.

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Hrsg.: G orner, R udiger, ed. Images of words: literary representations of pictorial themes. Iudicium Verlag GmbH, 2005.

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Rüdiger, Görner, and University of London. Institute of Germanic & Romance Studies., eds. Images of words: Literary representations of pictorial themes. Iudicium, 2005.

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Mike, Featherstone, and Wernick Andrew, eds. Images of aging: Cultural representations of later life. Routledge, 1995.

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Doris, Gutsmiedl-Schümann, Matias Jo Zalea, and Rambuscheck Ulrike 1966-, eds. Images of the past: Gender and its representations. Waxmann, 2017.

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R, Moyles Janet, ed. Images of violence: Responding to children's representations of the violence they see. Featherstone Education, 2005.

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Valéry, Université Paul, ed. Images de soi dans l'univers domestique: XIIIe-XVIe siècle. Presses universitaires de Rennes, 2018.

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1950-, Albrecht Hans-Jörg, Koukoutsaki Afroditi, and Serassis Telemach, eds. Images of crime: Representations of crime and the criminal in science, the arts, and the media. Edition Iuscrim, 2001.

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Po, Ming Jack. Multi-scale Representations for Classification of Protein Crystal Images and Multi-Modal Registration of the Lung. [publisher not identified], 2015.

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1950-, Albrecht Hans-Jörg, Koukoutsaki Afroditi, and Serassis Telemach, eds. Images of crime: Representations of crime and the criminal in science, the arts, and the media. Edition Iuscrim, 2001.

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Book chapters on the topic "Hierarchical representations of images"

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Díaz-del-Río, Fernando, Pablo Sanchez-Cuevas, Helena Molina-Abril, Pedro Real, and María José Moron-Fernández. "Building Hierarchical Tree Representations Using Homological-Based Tools." In Computer Analysis of Images and Patterns. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89131-2_11.

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Ying-Lie, O., and Alexander Toet. "Mathematical Morphology in Hierarchical Image Representation." In Medical Images: Formation, Handling and Evaluation. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77888-9_21.

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Høj, Benjamin J., and Andreas Møgelmose. "Synthesizing Hard Training Data from Latent Hierarchical Representations." In Image Analysis. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31438-4_4.

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Gerstmayer, Michael, Yll Haxhimusa, and Walter G. Kropatsch. "Hierarchical Interactive Image Segmentation Using Irregular Pyramids." In Graph-Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20844-7_25.

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Hidane, Moncef, Olivier Lézoray, and Abderrahim Elmoataz. "Hierarchical Representation of Discrete Data on Graphs." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23672-3_23.

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Cui, Yanwei, Laetitia Chapel, and Sébastien Lefèvre. "A Subpath Kernel for Learning Hierarchical Image Representations." In Graph-Based Representations in Pattern Recognition. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18224-7_4.

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Hollander, Allan D., Frank W. Davis, and David M. Stoms. "Hierarchical representations of species distributions using maps, images and sighting data." In Mapping the Diversity of Nature. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0719-8_5.

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Jin, Chuan, Anqi Zheng, Zhaoying Wu, and Changqing Tong. "TransVQ-VAE: Generating Diverse Images Using Hierarchical Representation Learning." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44213-1_16.

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Ge, Hongkun, Guorong Wu, Li Wang, Yaozong Gao, and Dinggang Shen. "Hierarchical Multi-modal Image Registration by Learning Common Feature Representations." In Machine Learning in Medical Imaging. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24888-2_25.

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Broelemann, Klaus, Anjan Dutta, Xiaoyi Jiang, and Josep Lladós. "Hierarchical Graph Representation for Symbol Spotting in Graphical Document Images." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34166-3_58.

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Conference papers on the topic "Hierarchical representations of images"

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Kats, Eytan, Jochen G. Hirsch, and Mattias P. Heinrich. "Self-Supervised Learning of Dense Hierarchical Representations for Medical Image Segmentation." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635522.

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Wang, Hengye, and Jianing Wang. "DeepMoE: MoE for deep non-hierarchical representation mechanisms." In International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2025), edited by Haiquan Zhao and Xinhua Tang. SPIE, 2025. https://doi.org/10.1117/12.3070893.

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Mukuddem, Adam, and Paul Amayo. "Osiris: Building Hierarchical Representations for Agricultural Environments." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610723.

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Cao, Jinkun, Jiangmiao Pang, and Kris Kitani. "Multi-Object Tracking by Hierarchical Visual Representations." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611201.

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Krosnick, Rebecca, and Steve Oney. "ScrapeViz: Hierarchical Representations for Web Scraping Macros." In 2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 2024. http://dx.doi.org/10.1109/vl/hcc60511.2024.00040.

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Batisteli, João Pedro Oliveira, and Zenilton K. G. Patrocínio Jr. "Hierarchical Graph Neural Networks Based on Multi-Scale Image Representations." In Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2024. https://doi.org/10.5753/sibgrapi.est.2024.31646.

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Image representation as graphs can enhance the understanding of image semantics and facilitate multi-scale image representation. However, existing methods often overlook the significance of the relationship between elements at each scale or fail to encode the hierarchical relationship between graph elements. To cope with that, we introduce four novel approaches for graph construction from images. These approaches utilize hierarchical image segmentation techniques to generate segmentations at multiple scales, and one of them incorporates edges to encode the relationships at each scale. Leveragi
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Zhang, Qiang, Xiuwen Liu, and Anuj Srivastava. "Statistical Search for Hierarchical Linear Optimal Representations of Images." In 2003 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW). IEEE, 2003. http://dx.doi.org/10.1109/cvprw.2003.10095.

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Mo, Guoliang, and Sanyuan Zhang. "Point Set Surfaces Representations Based on Hierarchical Geometry Images." In 2006 International Multi-Symposiums on Computer and Computational Sciences (IMSCCS). IEEE, 2006. http://dx.doi.org/10.1109/imsccs.2006.105.

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Potapov, Alexey S., and Olga S. Gamayunova. "Information criterion for constructing the hierarchical structural representations of images." In Defense and Security, edited by Firooz A. Sadjadi. SPIE, 2005. http://dx.doi.org/10.1117/12.602709.

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Yu, Chang, Xiangyu Zhu, Xiaomei Zhang, Zhaoxiang Zhang, and Zhen Lei. "Graphics Capsule: Learning Hierarchical 3D Face Representations from 2D Images." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.02010.

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Reports on the topic "Hierarchical representations of images"

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Lee, Chung-Nim, and Azriel Rosenfeld. Continuous Representations of Digital Images. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada164189.

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Munoz-Avila, Hector. Transfer Learning and Hierarchical Task Network Representations and Planning. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada500020.

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Tadmor, Eitan, Suzanne Nezzar, and Luminita Vese. Multiscale Hierarchical Decomposition of Images with Applications to Deblurring, Denoising and Segmentation. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada489758.

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Jablonski, David. DTRT57-09-C-10046 Digital Imaging of Pipeline Mechanical Damage and Residual Stress. Pipeline Research Council International, Inc. (PRCI), 2010. http://dx.doi.org/10.55274/r0011872.

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The purpose of this program was to enhance the characterization of mechanical damage in pipelines through the application of digital eddy current imaging. Lift-off maps can be used to develop quantitative representations of mechanical damage and magnetic permeability maps can be used to determine residual stress patterns around mechanical damage sites. Note that magnetic permeability is also affected by microstructure variations due to plastic deformation and plowing. High-resolution digital images provide an opportunity for automated analysis of both size and shape of damage and a permanent a
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Ley, Matt, Tom Baldvins, Hannah Pilkington, David Jones, and Kelly Anderson. Vegetation classification and mapping project: Big Thicket National Preserve. National Park Service, 2024. http://dx.doi.org/10.36967/2299254.

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The Big Thicket National Preserve (BITH) vegetation inventory project classified and mapped vegetation within the administrative boundary and estimated thematic map accuracy quantitatively. National Park Service (NPS) Vegetation Mapping Inventory Program provided technical guidance. The overall process included initial planning and scoping, imagery procurement, vegetation classification field data collection, data analysis, imagery interpretation/classification, accuracy assessment (AA), and report writing and database development. Initial planning and scoping meetings took place during May, 2
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Oblak Črnič, Tanja, and Katja Koren Ošljak. Digital Strategies of Political Parties in the 2024 European Elections: The Case of Slovenia. European Center for Populism Studies (ECPS), 2024. http://dx.doi.org/10.55271/rp0083.

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This report offers a systematic analysis of Slovenian political parties in online campaigning during the 2024 EP elections. It draws on a dataset of political parties and their online representations, selected from official party websites and dominant social media platforms, such as Facebook, Instagram and TikTok, in May 2024. The results show that Slovenian parties’ communication during the 2024 EP campaign was quite self-referential, accompanied by images of the candidates, indicating a high degree of personalization of politics. Moreover, the results show the “non-European orientation” of t
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Ley, Matt, Tom Baldvins, David Jones, Hanna Pilkington, and Kelly Anderson. Vegetation classification and mapping: Gulf Islands National Seashore. National Park Service, 2023. http://dx.doi.org/10.36967/2299028.

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The Gulf Islands National Seashore (GUIS) vegetation inventory project classified and mapped vegetation on park-owned lands within the administrative boundary and estimated thematic map accuracy quantitatively. The project began in June 2016. National Park Service (NPS) Vegetation Mapping Inventory Program provided technical guidance. The overall process included initial planning and scoping, imagery procurement, field data collection, data analysis, imagery interpretation/classification, accuracy assessment (AA), and report writing and database development. Initial planning and scoping meetin
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Zerla, Pauline. Trauma, Violence Prevention, and Reintegration: Learning from Youth Conflict Narratives in the Central African Republic. RESOLVE Network, 2024. http://dx.doi.org/10.37805/lpbi2024.1.

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This research report is a case study on the relationship between trauma, peacebuilding, and reintegration for conflict-affected youth in the Central African Republic (CAR) following the 2019 peace agreement. Based on qualitative research fielded in Spring 2022, the study examines how youth experience conflict, trauma, and reintegration in CAR, highlighting individual experiences through a participant narrative approach. In doing so, the report provides localized insight into the challenges that impact social reintegration and cohesion in fragile, conflict-affected contexts. The report further
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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-c
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