Academic literature on the topic 'Hierarchical representations of images'
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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 (May 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 (April 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 blocks, and each block contains a Local Visual Representation Module (LVRM) and a Decoder Module (DM). Based on the hierarchical attention network, we propose a scene text recognition network. The extensive experiments show that our proposed network achieves the state-of-the-art performance on several benchmark datasets including IIIT-5K, SVT, CUTE, SVT-Perspective, and ICDAR datasets under shorter training time.
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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 be used for product retrieval even in face ofthese challenges. Experimental analysis evaluated the effect thatnetwork architecture, data augmentation techniques and objectivefunctions used during training have on representation quality. Thebest configuration was achieved by fine-tuning a VGG-16 modelin the task of classifying products using a mix of Randaugmentand Augmix data augmentations and a hierarchical triplet loss as aregularization function. The representations built using this modelled to a top-1 accuracy of 80,38% and top-5 accuracy of 92.62% inthe Grocery Products dataset.
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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 the slides can evolve organically in this shared resource over time. The aim of this study was to analyse a teacher-generated resource in the form of a consensus set of course slides to characterise the combination and diversity in representations that students had encountered. This study was set in a unique context since the semester's lecture slides represented a distillation of consensus representations used by multiple chemistry lecturers for at least a decade. The representations included: those created by the lecturers; textbook images (from several texts); photographs and images sourced from the internet. Individual representations in each PowerPoint slide were coded in terms of the level of representation, mode and potential function in supporting deeper understanding of chemistry concepts. Three representational organizing frameworks (functional taxonomy of multiple representations, modes of representation and the chemistry triplet levels of thinking) were integrated to categorise the representations. This qualitative data was subjected to hierarchical cluster analysis and several relationships between the categories and topics taught were identified. Additional qualitative data in the form of student reflections on the perceived utility of specific representations were collected at the end of the semester. The findings from this study inform the design and choice of instructional resources for general chemistry particularly in combining representations to support deeper learning of concepts. A broader goal and application of the findings of this study is to identify opportunities for translation of representations into alternative modalities to widen access and participation in learning chemistry for all students. An example of a strategy for translating representations into tactile modes for teaching the topic of phase change is shared.
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Liu, Hao, Bin Wang, Zhimin Bao, Mobai Xue, Sheng Kang, Deqiang Jiang, Yinsong Liu, and 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 (June 28, 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 yield a more robust representation for both CTC-based and attention-based decoders than other contrastive learning methods. To fully investigate the potential of our method, we also collect a dataset of 100 million unlabeled text images, named UTI-100M, covering 5 scenes and 4 languages. By leveraging hundred-million-level unlabeled data, our PerSec shows significant performance improvement when fine-tuning the learned representation on the labeled data. Furthermore, we observe that the representation learned by PerSec presents great generalization, especially under few labeled data scenes.
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Gazagnes, Simon, and 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 (October 2019): 1940012. http://dx.doi.org/10.1142/s0218001419400123.
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The standard representations known as component trees, used in morphological connected attribute filtering and multi-scale analysis, are unsuitable for cases in which either the image itself or the tree do not fit in the memory of a single compute node. Recently, a new structure has been developed which consists of a collection of modified component trees, one for each image tile. It has to-date only been applied to fairly simple image filtering based on area. In this paper, we explore other applications of these distributed component forests, in particular to multi-scale analysis such as pattern spectra, and morphological attribute profiles and multi-scale leveling segmentations.
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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 (October 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-images in real-time. Applications of our technique include image-based object representations and the use of depth-images in large scale walk-through visualization systems.
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Bai, Jie, Huiyan Jiang, Siqi Li, and Xiaoqi Ma. "NHL Pathological Image Classification Based on Hierarchical Local Information and GoogLeNet-Based Representations." BioMed Research International 2019 (March 21, 2019): 1–13. http://dx.doi.org/10.1155/2019/1065652.
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Background. Accurate classification for different non-Hodgkin lymphomas (NHL) is one of the main challenges in clinical pathological diagnosis due to its intrinsic complexity. Therefore, this paper proposes an effective classification model for three types of NHL pathological images, including mantle cell lymphoma (MCL), follicular lymphoma (FL), and chronic lymphocytic leukemia (CLL). Methods. There are three main parts with respect to our model. First, NHL pathological images stained by hematoxylin and eosin (H&E) are transferred into blue ratio (BR) and Lab spaces, respectively. Then specific patch-level textural and statistical features are extracted from BR images and color features are obtained from Lab images both using a hierarchical way, yielding a set of hand-crafted representations corresponding to different image spaces. A random forest classifier is subsequently trained for patch-level classification. Second, H&E images are cropped and fed into a pretrained google inception net (GoogLeNet) for learning high-level representations and a softmax classifier is used for patch-level classification. Finally, three image-level classification strategies based on patch-level results are discussed including a novel method for calculating the weighted sum of patch results. Different classification results are fused at both feature 1 and image levels to obtain a more satisfactory result. Results. The proposed model is evaluated on a public IICBU Malignant Lymphoma Dataset and achieves an improved overall accuracy of 0.991 and area under the receiver operating characteristic curve of 0.998. Conclusion. The experimentations demonstrate the significantly increased classification performance of the proposed model, indicating that it is a suitable classification approach for NHL pathological images.
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Pham, Hai X., Ricardo Guerrero, Vladimir Pavlovic, and Jiatong Li. "CHEF: Cross-modal Hierarchical Embeddings for Food Domain Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2423–30. http://dx.doi.org/10.1609/aaai.v35i3.16343.
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Despite the abundance of multi-modal data, such as image-text pairs, there has been little effort in understanding the individual entities and their different roles in the construction of these data instances. In this work, we endeavour to discover the entities and their corresponding importance in cooking recipes automatically as a visual-linguistic association problem. More specifically, we introduce a novel cross-modal learning framework to jointly model the latent representations of images and text in the food image-recipe association and retrieval tasks. This model allows one to discover complex functional and hierarchical relationships between images and text, and among textual parts of a recipe including title, ingredients and cooking instructions. Our experiments show that by making use of efficient tree-structured Long Short-Term Memory as the text encoder in our computational cross-modal retrieval framework, we are not only able to identify the main ingredients and cooking actions in the recipe descriptions without explicit supervision, but we can also learn more meaningful feature representations of food recipes, appropriate for challenging cross-modal retrieval and recipe adaption tasks.
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Qiu, Zexuan, Jiahong Liu, Yankai Chen, and Irwin King. "HiHPQ: Hierarchical Hyperbolic Product Quantization for Unsupervised Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (March 24, 2024): 4614–22. http://dx.doi.org/10.1609/aaai.v38i5.28261.
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Existing unsupervised deep product quantization methods primarily aim for the increased similarity between different views of the identical image, whereas the delicate multi-level semantic similarities preserved between images are overlooked. Moreover, these methods predominantly focus on the Euclidean space for computational convenience, compromising their ability to map the multi-level semantic relationships between images effectively. To mitigate these shortcomings, we propose a novel unsupervised product quantization method dubbed Hierarchical Hyperbolic Product Quantization (HiHPQ), which learns quantized representations by incorporating hierarchical semantic similarity within hyperbolic geometry. Specifically, we propose a hyperbolic product quantizer, where the hyperbolic codebook attention mechanism and the quantized contrastive learning on the hyperbolic product manifold are introduced to expedite quantization. Furthermore, we propose a hierarchical semantics learning module, designed to enhance the distinction between similar and non-matching images for a query by utilizing the extracted hierarchical semantics as an additional training supervision. Experiments on benchmark image datasets show that our proposed method outperforms state-of-the-art baselines.
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 proposons enfin une méthodologie pour structurer et étudier l'espace des hiérarchies que nous avons construites en utilisant la distance de Gromov-Hausdorff entre elles.La seconde partie explore plusieurs applications de ces descriptions hiérarchiques d'images. Nous exposons une méthode pour apprendre à extraire de ces hiérarchies une bonne segmentation de façon automatique, étant donnés un type d'images et un score de bonne segmentation. Nous proposons également des descripteurs d'images obtenus par mesure des distances inter-hiérarchies, et exposons leur efficacité sur des données réelles et simulées. Enfin, nous étendons les potentielles applications de ces hiérarchies en introduisant une technique permettant de prendre en compte toute information spatiale a priori durant leur constructionThis 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
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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) équipés de caractéristiques numériques. Le noyau proposé, appelé Bag of Subpaths Kernel (BoSK), est formé en sommant les noyaux calculés sur les sous-chemins (un sac de tous les chemins et des noeuds simples) entre deux sacs. Le calcul direct de BoSK amène à une complexité quadratique par rapport à la taille de la structure (nombre de noeuds) et la quantité de données (taille de l’ensemble d’apprentissage). Nous proposons également une version rapide de notre algorithme, appelé Scalable BoSK (SBoSK), qui s’appuie sur la technique des Random Fourier Features pour projeter les données structurées dans un espace euclidien, où le produit scalaire du vecteur transformé est une approximation de BoSK. Cet algorithme bénéficie d’une complexité non plus linéaire mais quadratique par rapport aux tailles de la structure et de l’ensemble d’apprentissage, rendant ainsi le noyau adapté aux situations d’apprentissage à grande échelle. Grâce à (S)BoSK, nous sommes en mesure d’effectuer un apprentissage à partir d’informations présentes à plusieurs échelles dans les représentations hiérarchiques d’image. (S)BoSK fonctionne sur des chemins, permettant ainsi de tenir compte du contexte d’un pixel (feuille de la représentation hiérarchique) par l’intermédiaire de ses régions ancêtres à plusieurs échelles. Un tel modèle est utilisé dans la classification des images au niveau pixel. (S)BoSK fonctionne également sur les arbres, ce qui le rend capable de modéliser la composition d’un objet (racine de la représentation hiérarchique) et les relations topologiques entre ses sous-parties. Cette stratégie permet la classification des tuiles ou parties d’image. En poussant plus loin l’utilisation de (S)BoSK, nous introduisons une nouvelle approche de classification multi-source qui effectue la classification directement à partir d’une représentation hiérarchique construite à partir de deux images de la même scène prises à différentes résolutions, éventuellement selon différentes modalités. Les évaluations sur plusieurs jeux de données de télédétection disponibles dans la communauté illustrent la supériorité de (S)BoSK par rapport à l’état de l’art en termes de précision de classification, et les expériences menées sur une tâche de classification urbaine montrent la pertinence de l’approche de classification multi-source proposéeHierarchical 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
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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, les résultats d'une classification restent une interprétation haut-niveau de la scène observée puisque toutes les informations contenues dans une observation sont résumées en une unique classe. Contrairement aux méthodes de classification, les méthodes d'apprentissage de représentation sont fondées sur une modélisation des données et conçues spécialement pour traiter des données de grande dimension afin d'en extraire des variables latentes pertinentes. En utilisant une modélisation basée sur la physique des observations, ces méthodes permettent à l'utilisateur d'extraire des variables très riches de sens et d'obtenir une interprétation très fine de l'image considérée. L'objectif principal de cette thèse est de développer un cadre unifié pour l'apprentissage de représentation et la classification. Au vu de la complémentarité des deux méthodes, le problème est envisagé à travers une modélisation hiérarchique. L'approche par apprentissage de représentation est utilisée pour construire un modèle bas-niveau des données alors que la classification, qui peut être considérée comme une interprétation haut-niveau des données, est utilisée pour incorporer les informations supervisées. Deux paradigmes différents sont explorés pour mettre en place ce modèle hiérarchique, à savoir une modélisation bayésienne et la construction d'un problème d'optimisation. Les modèles proposés sont ensuite testés dans le contexte particulier de l'imagerie hyperspectrale où la tâche d'apprentissage de représentation est spécifiée sous la forme d'un problème de démélange spectralNumerous 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
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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 la relation d'adjacence. Le but de ce travail est de tirer partie à la fois des relations d’inclusion et d’adjacence dans ces representations pour mener à bien des tâches de vision par ordinateur. Nous introduisons le graphe d'alignement spatial (GAS) qui est construit à partir de l'inclusion et de l'arrangement spatial des régions dans l'AdF. Dans un cas simple tel que notre l'AdF de Laplacien, le GAS est réduit à un graphe déconnecté où chaque composant connecté est un groupe sémantique d'objets. Dans d’autres cases, e.g., l'AdF classique, le GAS est plus complexe. Pour résoudre ce problème, nous proposons d'élargir notre raisonnement à la morphologie basée sur la forme. Notre extension permet de manipuler n'importe quel graphe des formes et permet n'importe stratégie de filtrage dans la cadre de opérateurs connexes. Par conséquent, le GAS pourrait être analysé par une hiérarchie des quasi-zones plates. Les résultats de notre méthode dans la reconnaissance de texte montrent l'efficacité et la performance, qui sont attrayantes notablement pour les applications mobilesThe 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
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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 dernier critère est généralement obtenu en ajoutant du dynamisme à la bibliothèque, et plus particulièrement en interfaçant ses fonctionnalités à un langage dynamique. Les deux premiers critères peuvent être atteints avec des langages statiques tels que C++ ou Rust, qui exigent la connaissance de certaines informations au moment de la compilation pour optimiser le code machine généré en fonction des différents types de données d’entrée et de sortie d’un algorithme. Le dernier critère nécessite généralement d’attendre jusqu’à l’exécution pour obtenir des informations sur le type, et est donc réalisé au détriment de la vitesse d’exécution. Le travail présenté dans cette thèse vise à dépasser cette limitation dans le contexte d’algorithmes de traitement d’images. Pour ce faire, une méthodologie visant à développer des algorithmes génériques dont les informations sur les types d’entrée et de sortie peuvent être connues soit au moment de la compilation, soit à l’exécution, est présentée. Cette méthode est évaluée sur différents schémas algorithmiques de traitement d’images, et il est conclu que l’écart de performance entre les versions où l’information de type est connu à la compilation et à l’exécution de l’algorithme de construction pour les représentations hiérarchiques d’images est négligeable. En tant qu’application, les représentations hiérarchiques sont utilisées pour étendre l’applicabilité de l’estimation du niveau de bruit en niveaux de gris aux images en couleur afin d’améliorer leur caractère générique. Cela soulève l’importance d’étudier l’impact d’une telle altération dans les images à partir desquelles les représentations hiérarchiques sont construites pour améliorer l’efficacité de leurs applications en présence de bruit. Il est démontré que le bruit a un impact sur la structure arborescente, et cet impact est lié à certains types de fonctionnelles dans le cas où les hiérarchies sont contraintes par une énergieImage 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
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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. Le but de cette thèse est de développer des algorithmes prenant mieux en compte ce phénomène. Nous effectuons le démélange localement, dans des régions bien choisies de l'image où les effets de la variabilité sont moindres, en éliminant automatiquement les endmembers non pertinents grâce à de la parcimonie collaborative. Dans une autre approche, nous raffinons l'estimation des abondances en utilisant la structure de groupe d'un dictionnaire d'endmembers extrait depuis les données. Ensuite, nous proposons un modèle de mélange linéaire étendu, basé sur des considérations physiques, qui modélise la variabilité spectrale par des facteurs d'échelle, et développons des algorithmes d'optimisation pour en estimer les paramètres. Ce modèle donne des résultats facilement interprétables et de meilleures performances que d'autres approches de la littérature. Nous étudions enfin deux applications de ce modèle pour confirmer sa pertinenceThe 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
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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.Cataloged 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.
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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 perception of these global representations is determined by the local features within images. Using the tilt after-effect as a tool, the first chapter examined the processing of a low level, local feature and found that the orientation of a sinusoidal grating could be encoded in both a retinally and spatially non-specific manner. Chapter 2 then examined these tilt aftereffects to the global orientation of the image (i.e., uprightness). We found that image uprightness was also encoded in a retinally / spatially non-specific manner, but that this global property could be processed largely independently of its local orientation content. Chapter 3 investigated if our increased sensitivity to cardinal (vertical and horizontal) structures compared to inter-cardinal (45° and 135° clockwise of vertical) structures, influenced classification of unambiguous natural images. Participants required relatively less contrast to classify images when they retained near-cardinal as compared to near-inter-cardinal structures. Finally, in chapter 4, we examined category classification when images were ambiguous. Observers were biased to classify ambiguous images, created by combining structures from two distinct image categories, as carpentered (e.g., a house). This could not be explained by differences in sensitivity to local structures and is most likely the result of our long-term exposure to city views. Overall, these results show that higher-level representations are not fully dependent on the lower level features within an image. Furthermore, our knowledge about the environment influences the extent to which we use local features to rapidly identify an image.
Books on the topic "Hierarchical representations of images"
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Russell, Ian, ed. Images, Representations and Heritage. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-32216-7.
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Media images and representations. Philadelphia: Chelsea House Publishers, 2006.
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War & trauma images in Vietnam war representations. Hildesheim: George Olms Verlag, 2008.
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The matter of images: Essays on representations. London: Routledge, 1993.
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Controversial images: Media representations on the edge. Houndmills, Basingstoke, Hampshire [England]: Palgrave Macmillan, 2013.
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Creating sociological awareness: Collective images and symbolic representations. New Brunswick (U.S.A.): Transaction Publishers, 1991.
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Representations: Images of the world in Ciceronian oratory. Berkeley: University of California Press, 1993.
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Rüdiger, Görner, and University of London. Institute of Germanic & Romance Studies., eds. Images of words: Literary representations of pictorial themes. Munchen: Iudicium, 2005.
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Mike, Featherstone, and Wernick Andrew, eds. Images of aging: Cultural representations of later life. London: Routledge, 1995.
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Representations and contradictions: Ambivalence towards images, theatre, fiction, relics, and sexuality. Oxford: Blackwell Publishers, 1997.
Find full textBook 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, 120–30. Cham: 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, 445–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77888-9_21.
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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, 245–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20844-7_25.
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Høj, Benjamin J., and Andreas Møgelmose. "Synthesizing Hard Training Data from Latent Hierarchical Representations." In Image Analysis, 49–58. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31438-4_4.
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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, 186–93. Berlin, Heidelberg: 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, 34–43. Cham: 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, 71–88. Dordrecht: 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, 185–96. Cham: 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, 203–11. Cham: 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, 529–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34166-3_58.
Full textConference papers on the topic "Hierarchical representations of images"
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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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Almeida, Raquel, Ewa Kijak, Simon Malinowski, and Silvio Jamil F. Guimarães. "Learning on graphs and hierarchies." In 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.
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Hierarchies, as described in mathematical morphology, represent nested regions of interest that facilitate high-level analysis and provide mechanisms for coherent data organization. Represented as hierarchical trees, they have formalisms intersecting with graph theory and applications that can be conveniently generalized. However, due to the deterministic algorithms, the multiform representations, and the absence of a direct way to evaluate the hierarchical structure, it is hard to insert hierarchical information into a learning framework and benefit from the recent advances in the field. This work aims to create a learning framework that can operate with hierarchical data and is agnostic to the input and the application. The idea is to study ways to transform the data to a regular representation required by most learning models while preserving the rich information in the hierarchical structure. The methods in this study use edgeweighted image graphs and hierarchical trees as input, evaluating different proposals on the edge detection and segmentation tasks. The model of choice is the Random Forest, a fast, inspectable, scalable method. The experiments in this work are an outline of the original study in the related Ph.D. thesis. They demonstrate that it is possible to create a learning framework dependent only on the hierarchical data that performs well in multiple tasks.
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Ma, Libo, and Liqing Zhang. "A Hierarchical Generative Model for Overcomplete Topographic Representations in Natural Images." In International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371128.
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Yu, Feiwu, Xinxiao Wu, Yuchao Sun, and Lixin Duan. "Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks." In 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.
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Existing deep learning methods of video recognition usually require a large number of labeled videos for training. But for a new task, videos are often unlabeled and it is also time-consuming and labor-intensive to annotate them. Instead of human annotation, we try to make use of existing fully labeled images to help recognize those videos. However, due to the problem of domain shifts and heterogeneous feature representations, the performance of classifiers trained on images may be dramatically degraded for video recognition tasks. In this paper, we propose a novel method, called Hierarchical Generative Adversarial Networks (HiGAN), to enhance recognition in videos (i.e., target domain) by transferring knowledge from images (i.e., source domain). The HiGAN model consists of a \emph{low-level} conditional GAN and a \emph{high-level} conditional GAN. By taking advantage of these two-level adversarial learning, our method is capable of learning a domain-invariant feature representation of source images and target videos. Comprehensive experiments on two challenging video recognition datasets (i.e. UCF101 and HMDB51) demonstrate the effectiveness of the proposed method when compared with the existing state-of-the-art domain adaptation methods.
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Jia, Yiwei, Shiyong Huang, Xueming Li, and Xianlin Zhang. "HFFR-SR: Hierarchical Fusion Feature Representations for Super Resolution of Old Images." In 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.
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Ablameyko, Sergey V., Vladimir V. Bereishik, Nadeshda Paramonova, Angelo Marcelli, and Sachiko Ishikawa. "Hierarchical vector representation of document images." In Berlin - DL tentative, edited by Rudy A. Mattheus, Andre J. Duerinckx, and Peter J. van Otterloo. SPIE, 1993. http://dx.doi.org/10.1117/12.160483.
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Maren, A. J., and M. Ali. "Hierarchical scene structure representations to facilitate image understanding." In the first international conference. New York, New York, USA: ACM Press, 1988. http://dx.doi.org/10.1145/55674.55678.
Full textReports on the topic "Hierarchical representations of images"
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Lee, Chung-Nim, and Azriel Rosenfeld. Continuous Representations of Digital Images. Fort Belvoir, VA: Defense Technical Information Center, October 1985. http://dx.doi.org/10.21236/ada164189.
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Munoz-Avila, Hector. Transfer Learning and Hierarchical Task Network Representations and Planning. Fort Belvoir, VA: Defense Technical Information Center, February 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. Fort Belvoir, VA: Defense Technical Information Center, November 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. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), February 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 archival record that can be compared against future measurements to detect changes in size or shape of the damage. Also, multiple frequency measurements will enable volumetric and even through-wall imaging at mechanical damage sites to support further risk assessment efforts.
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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, 2016 in Kountze, Texas where representatives gathered from BITH, the NPS Gulf Coast Inventory and Monitoring Network, and Colorado State University. The project acquired new 2014 orthoimagery (30-cm, 4-band (RGB and CIR)) from the Hexagon Imagery Program. Supplemental imagery for the interpretation phase included Texas Natural Resources Information System (TNRIS) 2015 50 cm leaf-off 4-band imagery from the Texas Orthoimagery Program (TOP), Farm Service Agency (FSA) 100-cm (2016) and 60 cm (2018) National Aerial Imagery Program (NAIP) imagery, and current and historical true-color Google Earth and Bing Maps imagery. In addition to aerial and satellite imagery, 2017 Neches River Basin Light Detection and Ranging (LiDAR) data was obtained from the United States Geological Survey (USGS) and TNRIS to analyze vegetation structure at BITH. The preliminary vegetation classification included 110 United States National Vegetation Classification (USNVC) associations. Existing vegetation and mapping data combined with vegetation plot data contributed to the final vegetation classification. Quantitative classification using hierarchical clustering and professional expertise was supported by vegetation data collected from 304 plots surveyed between 2016 and 2019 and 110 additional observation plots. The final vegetation classification includes 75 USNVC associations and 27 park special types including 80 forest and woodland, 7 shrubland, 12 herbaceous, and 3 sparse vegetation types. The final BITH map consists of 51 map classes. Land cover classes include five types: pasture / hay ground agricultural vegetation; non ? vegetated / barren land, borrow pit, cut bank; developed, open space; developed, low ? high intensity; and water. The 46 vegetation classes represent 102 associations or park specials. Of these, 75 represent natural vegetation associations within the USNVC, and 27 types represent unpublished park specials. Of the 46 vegetation map classes, 26 represent a single USNVC association/park special, 7 map classes contain two USNVC associations/park specials, 4 map classes contain three USNVC associations/park specials, and 9 map classes contain four or more USNVC associations/park specials. Forest and woodland types had an abundance of Pinus taeda, Liquidambar styraciflua, Ilex opaca, Ilex vomitoria, Quercus nigra, and Vitis rotundifolia. Shrubland types were dominated by Pinus taeda, Ilex vomitoria, Triadica sebifera, Liquidambar styraciflua, and/or Callicarpa americana. Herbaceous types had an abundance of Zizaniopsis miliacea, Juncus effusus, Panicum virgatum, and/or Saccharum giganteum. The final BITH vegetation map consists of 7,271 polygons totaling 45,771.8 ha (113,104.6 ac). Mean polygon size is 6.3 ha (15.6 ac). Of the total area, 43,314.4 ha (107,032.2 ac) or 94.6% represent natural or ruderal vegetation. Developed areas such as roads, parking lots, and campgrounds comprise 421.9 ha (1,042.5 ac) or 0.9% of the total. Open water accounts for approximately 2,034.9 ha (5,028.3 ac) or 4.4% of the total mapped area. Within the natural or ruderal vegetation types, forest and woodland types were the most extensive at 43,022.19 ha (106,310.1 ac) or 94.0%, followed by herbaceous vegetation types at 129.7 ha (320.5 ac) or 0.3%, sparse vegetation types at 119.2 ha (294.5 ac) or 0.3%, and shrubland types at 43.4 ha (107.2 ac) or 0.1%. A total of 784 AA samples were collected to evaluate the map?s thematic accuracy. When each AA sample was evaluated for a variety of potential errors, a number of the disagreements were overturned. It was determined that 182 plot records disagreed due to either an erroneous field call or a change in the vegetation since the imagery date, and 79 disagreed due to a true map classification error. Those records identified as incorrect due to an erroneous field call or changes in vegetation were considered correct for the purpose of the AA. As a simple plot count proportion, the reconciled overall accuracy was 89.9% (705/784). The spatially-weighted overall accuracy was 92.1% with a Kappa statistic of 89.6%. This method provides more weight to larger map classes in the park. Five map classes had accuracies below 80%. After discussing preliminary results with the parl, we retained those map classes because the community was rare, the map classes provided desired detail for management or the accuracy was reasonably close to the 80% target. When the 90% AA confidence intervals were included, an additional eight classes had thematic accruacies that extend below 80%. In addition to the vegetation polygon database and map, several products to support park resource management include the vegetation classification, field key to the associations, local association descriptions, photographic database, project geodatabase, ArcGIS .mxd files for map posters, and aerial imagery acquired for the project. The project geodatabase links the spatial vegetation data layer to vegetation classification, plot photos, project boundary extent, AA points, and PLOTS database sampling data. The geodatabase includes USNVC hierarchy tables allowing for spatial queries of data associated with a vegetation polygon or sample point. All geospatial products are projected using North American Datum 1983 (NAD83) in Universal Transverse Mercator (UTM) Zone 15 N. The final report includes methods and results, contingency tables showing AA results, field forms, species list, and a guide to imagery interpretation. These products provide useful information to assist with management of park resources and inform future management decisions. Use of standard national vegetation classification and mapping protocols facilitates effective resource stewardship by ensuring the compatibility and widespread use throughout NPS as well as other federal and state agencies. Products support a wide variety of resource assessments, park management and planning needs. Associated information provides a structure for framing and answering critical scientific questions about vegetation communities and their relationship to environmental processes across the landscape.
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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, May 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 meetings took place during May, 2016 in Ocean Springs, Mississippi where representatives gathered from GUIS, the NPS Gulf Coast Inventory and Monitoring Network, and Colorado State University. Primary imagery used for interpretation was 4-band (RGB and CIR) orthoimages from 2014 and 2016 with resolutions of 15 centimeters (cm) (Florida only) and 30 cm. Supplemental imagery with varying coverage across the study area included National Aerial Imagery Program 50 cm imagery for Mississippi (2016) and Florida (2017), 15 and 30 cm true color Digital Earth Model imagery for Mississippi (2016 and 2017), and current and historical true-color Google Earth and Bing Map imagery. National Oceanic Atmospheric Administration National Geodetic Survey 30 cm true color imagery from 2017 (post Hurricane Nate) supported remapping the Mississippi barrier islands after Hurricane Nate. The preliminary vegetation classification included 59 United States National Vegetation Classification (USNVC) associations. Existing vegetation and mapping data combined with vegetation plot data contributed to the final vegetation classification. Quantitative classification using hierarchical clustering and professional expertise was supported by vegetation data collected from 250 plots in 2016 and 29 plots in 2017 and 2018, as well as other observational data. The final vegetation classification includes 39 USNVC associations and 5 park special types; 18 forest and woodland, 7 shrubland, 17 herbaceous, and 2 sparse vegetation types were identified. The final GUIS map consists of 38 map classes. Land cover classes include four types: non-vegetated barren land / borrow pit, developed open space, developed low – high intensity, and water/ocean. Of the 34 vegetation map classes, 26 represent a single USNVC association/park special, six map classes contain two USNVC associations/park specials, and two map classes contain three USNVC associations/park specials. Forest and woodland associations had an abundance of sand pine (Pinus clausa), slash pine (Pinus elliottii), sand live oak (Quercus geminata), yaupon (Ilex vomitoria), wax myrtle (Morella cerifera), and saw palmetto (Serenoa repens). Shrubland associations supported dominant species such as eastern baccharis (Baccharis halimifolia), yaupon (Ilex vomitoria), wax myrtle (Morella cerifera), saw palmetto (Serenoa repens), and sand live oak (Quercus geminata). Herbaceous associations commonly included camphorweed (Heterotheca subaxillaris), needlegrass rush (Juncus roemerianus), bitter seabeach grass (Panicum amarum var. amarum), gulf bluestem (Schizachyrium maritimum), saltmeadow cordgrass (Spartina patens), and sea oats (Uniola paniculata). The final GUIS vegetation map consists of 1,268 polygons totaling 35,769.0 hectares (ha) or 88,387.2 acres (ac). Mean polygon size excluding water is 3.6 ha (8.9 ac). The most abundant land cover class is open water/ocean which accounts for approximately 31,437.7 ha (77,684.2 ac) or 87.9% of the total mapped area. Natural and ruderal vegetation consists of 4,176.8 ha (10,321.1 ac) or 11.6% of the total area. Within the natural and ruderal vegetation types, herbaceous types are the most extensive with 1945.1 ha (4,806.4 ac) or 46.5%, followed by forest and woodland types with 804.9 ha (1,989.0 ac) or 19.3%, sparse vegetation types with 726.9 ha (1,796.1 ac) or 17.4%, and shrubland types with 699.9 ha (1,729.5 ac) or 16.8%. Developed open space, which can include a matrix of roads, parking lots, park-like areas and campgrounds account for 153.8 ha (380.0 ac) or 0.43% of the total mapped area. Artificially non-vegetated barren land is rare and only accounts for 0.74 ha (1.82 ac) or 0.002% of the total area. We collected 701 AA samples to evaluate the thematic accuracy of the vegetation map. Final thematic accuracy, as a simple proportion of correct versus incorrect field calls, is 93.0%. Overall weighted map class accuracy is 93.6%, where the area of each map class was weighted in proportion to the percentage of total park area. This method provides more weight to larger map classes in the park. Each map class had an individual thematic accuracy goal of at least 80%. The hurricane impact area map class was the only class that fell below this target with an accuracy of 73.5%. The vegetation communities impacted by the hurricane are highly dynamic and regenerated quickly following the disturbance event, contributing to map class disagreement during the accuracy assessment phase. No other map classes fell below the 80% accuracy threshold. In addition to the vegetation polygon database and map, several products to support park resource management are provided including the vegetation classification, field key to the associations, local association descriptions, photographic database, project geodatabase, ArcGIS .mxd files for map posters, and aerial imagery acquired for the project. The project geodatabase links the spatial vegetation data layer to vegetation classification, plot photos, project boundary extent, AA points, and the PLOTS database. The geodatabase includes USNVC hierarchy tables allowing for spatial queries of data associated with a vegetation polygon or sample point. All geospatial products are projected using North American Datum 1983 (NAD83) in Universal Transverse Mercator (UTM) Zone 16 N. The final report includes methods and results, contingency tables showing AA results, field forms, species list, and a guide to imagery interpretation. These products provide useful information to assist with management of park resources and inform future management decisions. Use of standard national vegetation classification and mapping protocols facilitates effective resource stewardship by ensuring the compatibility and widespread use throughout the NPS as well as other federal and state agencies. Products support a wide variety of resource assessments, park management and planning needs. Associated information provides a structure for framing and answering critical scientific questions about vegetation communities and their relationship to environmental processes across the landscape.
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Zerla, Pauline. Trauma, Violence Prevention, and Reintegration: Learning from Youth Conflict Narratives in the Central African Republic. RESOLVE Network, February 2024. http://dx.doi.org/10.37805/lpbi2024.1.
Full textAbstract:
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 underscores the implications of these insights for local and international efforts to establish peace and security through disarmament, demobilization, and reintegration (DDR) programs and community violence reduction (CVR) initiatives. In addition to standard data collection methods such as interviews and focus group discussions, data collection undertaken for this report utilized a trauma-informed method called body mapping. The use of body maps—life size images of a human body with visual representations of experiences— in research can offer a means for individuals to reflect on potentially difficult experiences through a non-verbal process. Given the potential relevance of this tool in future studies examining the nexus between conflict, reintegration, mental health, and trauma, this report also includes discussion of the implementation of this method with considerations for others hoping to adapt it for their own use.
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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, May 2021. http://dx.doi.org/10.17760/d20410114.
Full textAbstract:
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-consuming and expensive. The main objective of this study is to develop methods and algorithms to automate the post-processing of the visual sensor data towards the extraction of three main categories of information: 1) object information such as object identity, shapes, and spatial relationships - a novel heuristic-based method is proposed to automate the detection and recognition of main structural elements of steel girder bridges in both terrestrial and unmanned aerial vehicle (UAV)-based laser scanning data. Domain knowledge on the geometric and topological constraints of the structural elements is modeled and utilized as heuristics to guide the search as well as to reject erroneous detection results. 2) structural damage information, such as damage locations and quantities - to support the assessment of damage associated with small deformations, an advanced crack assessment method is proposed to enable automated detection and quantification of concrete cracks in critical structural elements based on UAV-based visual sensor data. In terms of damage associated with large deformations, based on the surface normal-based method proposed in Guldur et al. (2014), a new algorithm is developed to enhance the robustness of damage assessment for structural elements with curved surfaces. 3) three-dimensional volumetric models - the object information extracted from the laser scanning data is exploited to create a complete geometric representation for each structural element. In addition, mesh generation algorithms are developed to automatically convert the geometric representations into conformal all-hexahedron finite element meshes, which can be finally assembled to create a finite element model of the entire bridge. To validate the effectiveness of the developed methods and algorithms, several field data collections have been conducted to collect both the visual sensor data and the physical measurements from experimental specimens and in-service bridges. The data were collected using both terrestrial laser scanners combined with images, and laser scanners and cameras mounted to unmanned aerial vehicles.