Academic literature on the topic 'Image structure representation'
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Journal articles on the topic "Image structure representation"
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Chen, Yuhao, Alexander Wong, Yuan Fang, Yifan Wu, and Linlin Xu. "Deep Residual Transform for Multi-scale Image Decomposition." Journal of Computational Vision and Imaging Systems 6, no. 1 (January 15, 2021): 1–5. http://dx.doi.org/10.15353/jcvis.v6i1.3537.
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Multi-scale image decomposition (MID) is a fundamental task in computer vision and image processing that involves the transformation of an image into a hierarchical representation comprising of different levels of visual granularity from coarse structures to fine details. A well-engineered MID disentangles the image signal into meaningful components which can be used in a variety of applications such as image denoising, image compression, and object classification. Traditional MID approaches such as wavelet transforms tackle the problem through carefully designed basis functions under rigid decomposition structure assumptions. However, as the information distribution varies from one type of image content to another, rigid decomposition assumptions lead to inefficiently representation, i.e., some scales can contain little to no information. To address this issue, we present Deep Residual Transform (DRT), a data-driven MID strategy where the input signal is transformed into a hierarchy of non-linear representations at different scales, with each representation being independently learned as the representational residual of previous scales at a user-controlled detail level. As such, the proposed DRT progressively disentangles scale information from the original signal by sequentially learning residual representations. The decomposition flexibility of this approach allows for highly tailored representations cater to specific types of image content, and results in greater representational efficiency and compactness. In this study, we realize the proposed transform by leveraging a hierarchy of sequentially trained autoencoders. To explore the efficacy of the proposed DRT, we leverage two datasets comprising of very different types of image content: 1) CelebFaces and 2) Cityscapes. Experimental results show that the proposed DRT achieved highly efficient information decomposition on both datasets amid their very different visual granularity characteristics.
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RIZO-RODRÍGUEZ, DAYRON, HEYDI MÉNDEZ-VAZQUEZ, and EDEL GARCÍA-REYES. "ILLUMINATION INVARIANT FACE RECOGNITION IN QUATERNION DOMAIN." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 03 (May 2013): 1360004. http://dx.doi.org/10.1142/s0218001413600045.
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The performance of face recognition systems tends to decrease when images are affected by illumination. Feature extraction is one of the main steps of a face recognition process, where it is possible to alleviate the illumination effects on face images. In order to increase the accuracy of recognition tasks, different methods for obtaining illumination invariant features have been developed. The aim of this work is to compare two different ways to represent face image descriptions in terms of their illumination invariant properties for face recognition. The first representation is constructed following the structure of complex numbers and the second one is based on quaternion numbers. Using four different face description approaches both representations are constructed, transformed into frequency domain and expressed in polar coordinates. The most illumination invariant component of each frequency domain representation is determined and used as the representative information of the face image. Verification and identification experiments are then performed in order to compare the discriminative power of the selected components. Representative component of the quaternion representation overcame the complex one.
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Fu, Y., Y. Ye, G. Liu, B. Zhang, and R. Zhang. "ROBUST MULTIMODAL IMAGE MATCHING BASED ON MAIN STRUCTURE FEATURE REPRESENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 583–89. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-583-2020.
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Abstract. Image matching is a crucial procedure for multimodal remote sensing image processing. However, the performance of conventional methods is often degraded in matching multimodal images due to significant nonlinear intensity differences. To address this problem, this letter proposes a novel image feature representation named Main Structure with Histogram of Orientated Phase Congruency (M-HOPC). M-HOPC is able to precisely capture similar structure properties between multimodal images by reinforcing the main structure information for the construction of the phase congruency feature description. Specifically, each pixel of an image is assigned an independent weight for feature descriptor according to the main structure such as large contours and edges. Then M-HOPC is integrated as the similarity measure for correspondence detection by a template matching scheme. Three pairs of multimodal images including optical, LiDAR, and SAR data have been used to evaluate the proposed method. The results show that M-HOPC is robust to nonlinear intensity differences and achieves the superior matching performance compared with other state-of-the-art methods.
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WANG, ZHIYONG, ZHERU CHI, DAGAN FENG, and AH CHUNG TSOI. "CONTENT-BASED IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK USING ADAPTIVE PROCESSING OF TREE-STRUCTURE IMAGE REPRESENTATION." International Journal of Image and Graphics 03, no. 01 (January 2003): 119–43. http://dx.doi.org/10.1142/s0219467803000944.
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Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested.
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Yu, Siquan, Jiaxin Liu, Zhi Han, Yong Li, Yandong Tang, and Chengdong Wu. "Representation Learning Based on Autoencoder and Deep Adaptive Clustering for Image Clustering." Mathematical Problems in Engineering 2021 (January 9, 2021): 1–11. http://dx.doi.org/10.1155/2021/3742536.
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Image clustering is a complex procedure, which is significantly affected by the choice of image representation. Most of the existing image clustering methods treat representation learning and clustering separately, which usually bring two problems. On the one hand, image representations are difficult to select and the learned representations are not suitable for clustering. On the other hand, they inevitably involve some clustering step, which may bring some error and hurt the clustering results. To tackle these problems, we present a new clustering method that efficiently builds an image representation and precisely discovers cluster assignments. For this purpose, the image clustering task is regarded as a binary pairwise classification problem with local structure preservation. Specifically, we propose here such an approach for image clustering based on a fully convolutional autoencoder and deep adaptive clustering (DAC). To extract the essential representation and maintain the local structure, a fully convolutional autoencoder is applied. To manipulate feature to clustering space and obtain a suitable image representation, the DAC algorithm participates in the training of autoencoder. Our method can learn an image representation that is suitable for clustering and discover the precise clustering label for each image. A series of real-world image clustering experiments verify the effectiveness of the proposed algorithm.
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CHEN, XIAOWU, BIN ZHOU, FANG XU, and QINPING ZHAO. "AUTOMATIC IMAGE COMPLETION WITH STRUCTURE PROPAGATION AND TEXTURE SYNTHESIS." International Journal of Software Engineering and Knowledge Engineering 20, no. 08 (December 2010): 1097–117. http://dx.doi.org/10.1142/s0218194010005055.
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In this paper, we present a novel automatic image completion solution in a greedy manner inspired by a primal sketch representation model. Firstly, an image is divided into structure (sketchable) components and texture (non-sketchable) components, and the missing structures, such as curves and corners, are predicted by tensor voting. Secondly, the textures along structural sketches are synthesized with the sampled patches of some known structure components. Then, using the texture completion priorities decided by the confidence term, data term and distance term, the similar image patches of some known texture components are found by selecting a point with the maximum priority on the boundary of hole region. Finally, these image patches inpaint the missing textures of hole region seamlessly through graph cuts. The characteristics of this solution include: (1) introducing the primal sketch representation model to guide completion for visual consistency; (2) achieving fully automatic completion. The experiments on natural images illustrate satisfying image completion results.
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Li, Wei, Yuxiang Zhang, Na Liu, Qian Du, and Ran Tao. "Structure-Aware Collaborative Representation for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing 57, no. 9 (September 2019): 7246–61. http://dx.doi.org/10.1109/tgrs.2019.2912507.
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Li, Zhao, Le Wang, Tao Yu, and Bing Liang Hu. "Image Super-Resolution via Low-Rank Representation." Applied Mechanics and Materials 568-570 (June 2014): 652–55. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.652.
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This paper presents a novel method for solving single-image super-resolution problems, based upon low-rank representation (LRR). Given a set of a low-resolution image patches, LRR seeks the lowest-rank representation among all the candidates that represent all patches as the linear combination of the patches in a low-resolution dictionary. By jointly training two dictionaries for the low-resolution and high-resolution images, we can enforce the similarity of LLRs between the low-resolution and high-resolution image pair with respect to their own dictionaries. Therefore, the LRR of a low-resolution image can be applied with the high-resolution dictionary to generate a high-resolution image. Unlike the well-known sparse representation, which computes the sparsest representation of each image patch individually, LRR aims at finding the lowest-rank representation of a collection of patches jointly. LRR better captures the global structure of image. Experiments show that our method gives good results both visually and quantitatively.
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Dong, Bin, Songlei Jian, and Kai Lu. "Learning Multimodal Representations by Symmetrically Transferring Local Structures." Symmetry 12, no. 9 (September 13, 2020): 1504. http://dx.doi.org/10.3390/sym12091504.
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Multimodal representations play an important role in multimodal learning tasks, including cross-modal retrieval and intra-modal clustering. However, existing multimodal representation learning approaches focus on building one common space by aligning different modalities and ignore the complementary information across the modalities, such as the intra-modal local structures. In other words, they only focus on the object-level alignment and ignore structure-level alignment. To tackle the problem, we propose a novel symmetric multimodal representation learning framework by transferring local structures across different modalities, namely MTLS. A customized soft metric learning strategy and an iterative parameter learning process are designed to symmetrically transfer local structures and enhance the cluster structures in intra-modal representations. The bidirectional retrieval loss based on multi-layer neural networks is utilized to align two modalities. MTLS is instantiated with image and text data and shows its superior performance on image-text retrieval and image clustering. MTLS outperforms the state-of-the-art multimodal learning methods by up to 32% in terms of R@1 on text-image retrieval and 16.4% in terms of AMI onclustering.
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Berg, A. P., and W. B. Mikhael. "An efficient structure and algorithm for image representation using nonorthogonal basis images." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 44, no. 10 (1997): 818–28. http://dx.doi.org/10.1109/82.633439.
Full textDissertations / Theses on the topic "Image structure representation"
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Noble, Julia Alison. "Descriptions of image surfaces." Thesis, University of Oxford, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238117.
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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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Jeong, Ki Tai. "A Common Representation Format for Multimedia Documents." Thesis, University of North Texas, 2002. https://digital.library.unt.edu/ark:/67531/metadc3336/.
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Multimedia documents are composed of multiple file format combinations, such as image and text, image and sound, or image, text and sound. The type of multimedia document determines the form of analysis for knowledge architecture design and retrieval methods. Over the last few decades, theories of text analysis have been proposed and applied effectively. In recent years, theories of image and sound analysis have been proposed to work with text retrieval systems and progressed quickly due in part to rapid progress in computer processing speed. Retrieval of multimedia documents formerly was divided into the categories of image and text, and image and sound. While standard retrieval process begins from text only, methods are developing that allow the retrieval process to be accomplished simultaneously using text and image. Although image processing for feature extraction and text processing for term extractions are well understood, there are no prior methods that can combine these two features into a single data structure. This dissertation will introduce a common representation format for multimedia documents (CRFMD) composed of both images and text. For image and text analysis, two techniques are used: the Lorenz Information Measurement and the Word Code. A new process named Jeong's Transform is demonstrated for extraction of text and image features, combining the two previous measurements to form a single data structure. Finally, this single data measurements to form a single data structure. Finally, this single data structure is analyzed by using multi-dimensional scaling. This allows multimedia objects to be represented on a two-dimensional graph as vectors. The distance between vectors represents the magnitude of the difference between multimedia documents. This study shows that image classification on a given test set is dramatically improved when text features are encoded together with image features. This effect appears to hold true even when the available text is diffused and is not uniform with the image features. This retrieval system works by representing a multimedia document as a single data structure. CRFMD is applicable to other areas of multimedia document retrieval and processing, such as medical image retrieval, World Wide Web searching, and museum collection retrieval.
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Kershaw, Helen Elizabeth. "Reconstruction of mechanical properties from surface-based motion data for Digital Image Elasto-Tomography using an implicit surface representation of breast tissue structure." Thesis, University of Canterbury. Mechanical Engineering, 2012. http://hdl.handle.net/10092/7271.
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There has been great interest in recent times in the use of elastography for the characterization of human tissue. Digital Image Elasto-Tomography is a novel breast cancer pre-screening technique under development at the University of Canterbury, which aims to identify and locate stiff areas within the breast that require further investigation using images of the surface motion alone. A calibrated array of five digital cameras is used to capture surface motion of the breast under harmonic actuation. The forward problem, that is the resulting motion for a given mechanical property distribution, is calculated using the Finite Element Method. The inverse problem is to find the mechanical properties which reproduce the measured surface motion through numerical simulation. A reconstruction algorithm is developed using a shape based description to reduce the number of parameters in the inverse problem. A parallel Genetic Algorithm is developed for parameter optimization. A geometric method termed Fitness Function Analysis is shown to improve the inclusion location optimization problem. The ensemble of solutions generated using the Genetic Algorithm is used to produce an optimal and a credible region for inclusion location. Successful single frequency phantom reconstructions are presented. An effective way of combining information from multi-frequency phantom data by examining the characteristics of the measured surface motion using data quality metrics is developed and used to produce improved reconstructions. Results from numerical simulation datasets and a two inclusion phantom used to test the optimization of multiple and ellipsoidal inclusions indicate that although two inclusions can be successfully reconstructed, the single inclusions assumption may suffice even in irregular, heterogeneous cases. This assumption was used to successfully locate the stiffest inclusion in a phantom containing multiple inclusions of differing stiffness based on three multi-frequency datasets. The methods developed in phantoms are applied to three in vivo cases for both single and multi-frequency data with limited success.
This thesis builds on previous work undertaken at the University of Canterbury. The original contributions in this work are as follows. A new reconstruction algorithm combining a genetic algorithm with fitness function analysis is developed. The most realistic tissue mimicking phantoms to date are used. An ellipsoidal shape-based description is presented, and applied to the first multi-inclusion reconstructions in DIET. This work presents the first reconstruction using meshes created directly from data using a meshing algorithm developed by Jonas Biehler. A multi-frequency cost function is developed to produce the first multi-frequency and in vivo reconstructions using DIET data.
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Elliott, Desmond. "Structured representation of images for language generation and image retrieval." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10524.
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A photograph typically depicts an aspect of the real world, such as an outdoor landscape, a portrait, or an event. The task of creating abstract digital representations of images has received a great deal of attention in the computer vision literature because it is rarely useful to work directly with the raw pixel data. The challenge of working with raw pixel data is that small changes in lighting can result in different digital images, which is not typically useful for downstream tasks such as object detection. One approach to representing an image is automatically extracting and quantising visual features to create a bag-of-terms vector. The bag-of-terms vector helps overcome the problems with raw pixel data but this unstructured representation discards potentially useful information about the spatial and semantic relationships between the parts of the image. The central argument of this thesis is that capturing and encoding the relationships between parts of an image will improve the performance of extrinsic tasks, such as image description or search. We explore this claim in the restricted domain of images representing events, such as riding a bicycle or using a computer. The first major contribution of this thesis is the Visual Dependency Representation: a novel structured representation that captures the prominent region–region relationships in an image. The key idea is that images depicting the same events are likely to have similar spatial relationships between the regions contributing to the event. This representation is inspired by dependency syntax for natural language, which directly captures the relationships between the words in a sentence. We also contribute a data set of images annotated with multiple human-written descriptions, labelled image regions, and gold-standard Visual Dependency Representations, and explain how the gold-standard representations can be constructed by trained human annotators. The second major contribution of this thesis is an approach to automatically predicting Visual Dependency Representations using a graph-based statistical dependency parser. A dependency parser is typically used in Natural Language Processing to automatically predict the dependency structure of a sentence. In this thesis we use a dependency parser to predict the Visual Dependency Representation of an image because we are working with a discrete image representation – that of image regions. Our approach can exploit features from the region annotations and the description to predict the relationships between objects in an image. In a series of experiments using gold-standard region annotations, we report significant improvements in labelled and unlabelled directed attachment accuracy over a baseline that assumes there are no relationships between objects in an image. Finally, we find significant improvements in two extrinsic tasks when we represent images as Visual Dependency Representations predicted from gold-standard region annotations. In an image description task, we show significant improvements in automatic evaluation measures and human judgements compared to state-of-the-art models that use either external text corpora or region proximity to guide the generation process. In the query-by-example image retrieval task, we show a significant improvement in Mean Average Precision and the precision of the top 10 images compared to a bag-of-terms approach. We also perform a correlation analysis of human judgements against automatic evaluation measures for the image description task. The automatic measures are standard measures adopted from the machine translation and summarization literature. The main finding of the analysis is that unigram BLEU is less correlated with human judgements than Smoothed BLEU, Meteor, or skip-bigram ROUGE.
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Gay, Joanna. "Structural representation models for multi-modal image registration in biomedical applications." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-410820.
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In clinical applications it is often beneficial to use multiple imaging technologies to obtain information about different biomedical aspects of the subject under investigation, and to make best use of such sets of images they need to first be registered or aligned. Registration of multi-modal images is a challenging task and is currently the topic of much research, with new methods being published frequently. Structural representation models extract underlying features such as edges from images, distilling them into a common format that can be easily compared across different image modalities. This study compares the performance of two recent structural representation models on the task of aligning multi-modal biomedical images, specifically Second Harmonic Generation and Two Photon Excitation Fluorescence Microscopy images collected from skin samples. Performance is also evaluated on Brightfield Microscopy images. The two models evaluated here are PCANet-based Structural Representations (PSR, Zhu et al. (2018)) and Discriminative Local Derivative Patterns (dLDP, Jiang et al. (2017)). Mutual Information is used to provide a baseline for comparison. Although dLDP in particular gave promising results, worthy of further investigation, neither method outperformed the classic Mutual Information approach, as demonstrated in a series of experiments to register these particularly diverse modalities.
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Kemp, Jamie L. "Score and structure in ritual representation : meanings of the notational form in Sarum processional images." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/32456.
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This research project examines an intriguing type of depictions which can be found within Sarum processional manuscripts, a genre of liturgical books which were produced between the thirteenth and fifteenth centuries. The central focus is a specific example from Norwich which was produced between the late fourteenth and early fifteenth
centuries. I propose that their flat, ordered, and geometrically arranged mode of representation can be best understood when considered in relation to the semantic characteristics of notational systems. Their visual form signals that they are not representations of idiosyncratic events which have happened in the past, but are instead authoritative prescriptive layouts. They illustrate what important objects are required for
the performance of a ritual and the number, status and position of the participants that will need to be in attendance. Thus, I argue that the viewer is not intended to be a passive witness to a scene taking place in the image, but is instead a presumed participant in a future performance of a specific character.
Three arguments are introduced to lend support to the thesis. The first presents historical
evidence which illustrates the authoritative role given to these books. The text discusses their widespread use and argues that this authoritative role may have been the result of a deliberate strategy on the part of the individuals seeking to increase the circulation of the books associated with the Sarum Use. The second argument is based on the examination of the relationships between the images and the texts found within the books. It states that the images do not present sufficient information to be considered pictorial instructions,
but instead, can convey other meanings. The final argument is that the pictorial images
have the semantic characteristics of a notational system. I argue that they are related to
one specific system—the musical scores which interleave the images and intermingle
with them in the pictorial frame.Arts, Faculty of
Art History, Visual Art and Theory, Department of
Graduate
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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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Nehme, Raphaela. "The Lens of the Other: Instagram as a Tool to Counter the Unsafe Images of Countries and the Case of Lebanon." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41040.
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The emergence of new media tools and social media platforms over the last ten years has created new means of intercultural engagement. On Instagram, there is a growing trend of travel pages and travel bloggers whose aim is to introduce and share with other users the highlights of the destinations they travel to. This also applies to locals in these destinations who wish to portray their country positively and promote it as a tourist destination, particularly in certain countries of the Middle East where there is the added challenge of an ‘unsafe’ image to combat. This research focuses on Lebanon to find out to what extent Instagram can be considered a tool to combat the ‘unsafe’ image of the country, and if users who come across depictions of Lebanon on Instagram perceive the country as a potential tourist destination. Using a mixed methods approach, this thesis combines surveys and semi-structured interviews with Canadian participants to reach its findings. Its theoretical framework makes use of Edward Said’s conception of the ‘other’ (1978), Stuart Hall’s system of representations (1980), Jan Neverdeen Pieterse’s hybridization paradigm (1996), and Eli Pariser’s (2011) echo chamber to analyze its findings. Broadly, findings show that while Instagram can effectively be considered a tool to counter the ‘unsafe’ image of Lebanon, and while the country may be branded as a potential tourist destination to users who come across such depictions of it, algorithm restrictions limit the potential for such depictions to fulfill their potential since they don’t always reach users who perceive Lebanon to be an ‘unsafe’ place.
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Riba, Fiérrez Pau. "Distilling Structure from Imagery: Graph-based Models for the Interpretation of Document Images." Doctoral thesis, Universitat Autònoma de Barcelona, 2020. http://hdl.handle.net/10803/670774.
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Des del seu inici, la comunitat investigadora sobre reconeixement de patrons i visió per computador ha reconegut la importància d’aprofitar la informació estructural de les imatges. Els grafs s’han seleccionat com el marc adequat per representar aquest tipus d’informació a causa de la seva flexibilitat i poder de representació capaç de codificar, tant els components, objectes i entitats com les seves relacions. Tot i que els grafs s’han aplicat amb èxit a una gran varietat de tasques -com a resultat de la seva naturalesa simbòlica i relacional- sempre han patit d’algunes limitacions comparats amb mètodes estadístics. Això es deu al fet que algunes operacions matemàtiques trivials no tenen una equivalència en el domini dels grafs. Per exemple, en la base de moltes aplicacions de reconeixement de patrons hi ha la necessitat de comparar objectes. No obstant això, aquesta operació trivial no està degudament definida per grafs quan considerem vectors de característiques.
Al llarg d’aquesta recerca, el principal domini d’aplicació està basat en el tema de l’Anàlisi i Reconeixement d’Imatges de Documents. Aquest és un subcamp de la Visió per Computador que té com a objectiu compendre imatges de documents. En aquest context, l’estructura -particularment la representació en forma de graf- proporciona una dimensió complementària al contingut de la imatge.
En Visió per Computador la primera dificultat que ens trobem recau en construir una representació significativa de grafs capaç de codificar les característiques rellevants d’una imatge donada. Això es deu al fet que és un procés que ha de trobar un equilibri entre la simplicitat de la representació i la flexibilitat, per tal de representar les diferents deformacions que apareixen en cada domini d’aplicació. Hem estudiat aquest tema en l’aplicació de la recerca de paraules, dividint els diferents traços en grafemes –les unitats més petites d’un alfabet manuscrit&-.
També, hem investigat diferents metodologies per accelerar el procés de comparació entre grafs perquè la recerca de paraules o, inclús, de forma més general, l’aplicació en la recerca de grafs, pugui incloure grans col·leccions de documents. Aquestes metodologies han estat principalment dues: (a) un sistema d’indexació de grafs combinat amb un sistema de votació en l’àmbit de nodes capaç d’eliminar resultats improbables i (b) usant representacions jeràrquiques de grafs que duen a terme la majoria de les comparacions en una versió reduïda del graf original, mitjançant comparatives entre els nivells més abstractes i els més detallats. A més a més, la representació jeràrquica també ha demostrat obtenir una representació més robusta que el graf original, lidiant amb el soroll i les deformacions de manera elegant. Per tant, proposem explotar aquesta informació en forma de codificació jeràrquica del graf que permeti utilitzar tècniques estadístiques clàssiques.
Els nous avenços en aprenentatge profund geomètric han aparegut com una generalització de les metodologies d’aprenentatge profund aplicades a dominis no Euclidians –com grafs i varietats–, i han promogut un gran interès en la comunitat científica per aquests esquemes de representació. Així doncs, proposem una distància de grafs capaç d’obtenir resultats comparables a l’estat de l’art en diferents tasques aprofitant aquests nous desenvolupaments, però considerant les metodologies tradicionals com a base.
També hem realitzat una col·laboració industrial amb la finalitat d’extreure informació automàtica de les factures de l’empresa (amb dades anònimes). El resultat ha estat el desenvolupament d’un sistema de detecció de taules en documents administratius. D’aquesta manera les xarxes neuronals basades en grafs han demostrat ser aptes per detectar patrons repetitius, els quals, després d’un procés d’agregació, constitueixen una taula.La comunidad que investiga el reconocimiento de patrones y la visión por computador ha reconocido la importancia de aprovechar la información estructural de las imágenes. Los grafos se han seleccionado como el marco adecuado para representar este tipo de información a causa de su flexibilidad y poder de representación capaz de codificar los componentes, los objetos, las entidades y sus relaciones. Aunque los grafos se han aplicado con éxito a una gran variedad de tareas –como resultado de su naturaleza simbólica y relacional–, siempre han sufrido algunas limitaciones comparados con los métodos estadísticos. Esto se debe al hecho que algunas operaciones matemáticas triviales no tienen una equivalencia en el dominio de los grafos. Por ejemplo, en la base de la mayoría de aplicaciones de reconocimiento de patrones hay la necesidad de comparar objetos. No obstante, esta operación trivial no está debidamente definida por grafos cuando consideramos vectores de características. Durante la investigación, el principal dominio de aplicación se basa en el Análisis y Reconocimiento de Imágenes de Documentos. Este es un subcampo de la Visión por Computador que tiene como objetivo comprender imágenes de documentos. En este contexto la estructura -particularmente la representación en forma de grafo- proporciona una dimensión complementaria al contenido de la imágen. En Visión por Computador la primera dificultad que nos encontramos se basa en construir una representación significativa de grafos que sea capaz de codificar las características relevantes de una imagen. Esto se debe a que es un proceso que tiene que encontrar un equilibrio entre la simplicidad de la representación y la flexibilidad, para representar las diferentes deformaciones que aparecen en cada dominio de la aplicación. Hemos estudiado este tema en la aplicación de la búsqueda de palabras, dividiendo los diferentes trazos en grafemas –las unidades más pequeñas de un alfabeto manuscrito–. Tambien, hemos investigado diferentes metodologías para acelerar el proceso de comparación entre grafos para que la búsqueda de palabras o, incluso, de forma más general, la aplicación de búsqueda de grafos, pueda incluir grandes colecciones de documentos. Estas metodologías han estado principalmente dos: (a) un sistema de indexación de grafos combinado con un sistema de votación en el ámbito de los nodos capaces de eliminar resultados improbables y (b) usando representaciones jerárquicas de grafos que llevan a término la mayoría de las comparaciones en una versión reducida del grafo original mediante comparativas entre los niveles más abstractos y los más detallados. Asimismo, la representación jerárquica también ha demostrado obtener una representación más robusta que el grafo original, además de lidiar con el ruido y las deformaciones de manera elegante. Así pues, proponemos explotar esta información en forma de codificación jerárquica del grafo que permita utilizar técnicas estadísticas clásicas. Los nuevos avances en el aprendizaje profundo geométrico han aparecido como una generalización de las metodologías de aprendizaje profundo aplicadas a dominios no Euclidianos –como grafos y variedades– y han promovido un gran interés en la comunidad científica por estos esquemas de representación. Proponemos una distancia de grafos capaz de obtener resultados comparables al estado del arte en diferentes tareas aprovechando estos nuevos desarrollos, pero considerando las metodologías tradicionales como base. También hemos realizado una colaboración industrial con la finalidad de extraer información automática de las facturas de la empresa (con datos anónimos). El resultado ha sido el desarrollo de un sistema de detección de tablas en documentos administrativos. Así pues, las redes neuronales basadas en grafos han demostrado ser aptas para detectar patrones repetitivos, los cuales, después de un proceso de agregación, constituyen una tabla.
From its early stages, the community of Pattern Recognition and Computer Vision has considered the importance on leveraging the structural information when understanding images. Usually, graphs have been selected as the adequate framework to represent this kind of information due to their flexibility and representational power able to codify both, the components, objects or entities and their pairwise relationship. Even though graphs have been successfully applied to a huge variety of tasks, as a result of their symbolic and relational nature, graphs have always suffered from some limitations compared to statistical approaches. Indeed, some trivial mathematical operations do not have an equivalence in the graph domain. For instance, in the core of many pattern recognition application, there is the need to compare two objects. This operation, which is trivial when considering feature vectors, is not properly defined for graphs. Along this dissertation the main application domain has been on the topic of Document Image Analysis and Recognition. It is a subfield of Computer Vision aiming at understanding images of documents. In this context, the structure and in particular graph representations, provides a complementary dimension to the raw image contents. In computer vision, the first challenge we face is how to build a meaningful graph representation that is able to encode the relevant characteristics of a given image. This representation should find a trade off between the simplicity of the representation and its flexibility to represent the deformations appearing on each application domain. We applied our proposal to the word spotting application where strokes are divided into graphemes which are the smaller units of a handwritten alphabet. We have investigated different approaches to speed-up the graph comparison in order that word spotting, or more generally, a retrieval application is able to handle large collections of documents. On the one hand, a graph indexing framework combined with a votation scheme at node level is able to quickly prune unlikely results. On the other hand, making use of graph hierarchical representations, we are able to perform a coarse-to-fine matching scheme which performs most of the comparisons in a reduced graph representation. Besides, the hierarchical graph representation demonstrated to be drivers of a more robust scheme than the original graph. This new information is able to deal with noise and deformations in an elegant fashion. Therefore, we propose to exploit this information in a hierarchical graph embedding which allows the use of classical statistical techniques. Recently, the new advances on geometric deep learning, which has emerged as a generalization of deep learning methods to non-Euclidean domains such as graphs and manifolds, has raised again the attention to these representation schemes. Taking advantage of these new developments but considering traditional methodologies as a guideline, we proposed a graph metric learning framework able to obtain state-of-the-art results on different tasks. Finally, the contributions of this thesis have been validated in real industrial use case scenarios. For instance, an industrial collaboration has resulted in the development of a table detection framework in annonymized administrative documents containing sensitive data. In particular, the interest of the company is the automatic information extraction from invoices. In this scenario, graph neural networks have proved to be able to detect repetitive patterns which, after an aggregation process, constitute a table.
Books on the topic "Image structure representation"
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Cervel, M. Sandra Peña. Topology and cognition: What image-schemas reveal about the metaphorical languages of emotions. Muenchen: Lincom Europa, 2003.
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Neretina, Tat'yana, and Tat'yana Orehova. Formation at students of pedagogical profile "image of the parent" in the process of professional training at the University. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1043103.
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In modern conditions of development of mankind, when, for various reasons endangered is the institution of the family, especially actual is a problem of formation of the growing person in the period of schooling parental position as an essential part not only of development but also the survival of humanity as a species. The solution to this problem in terms of the organization of Russian society goes along with the family on a school teacher. Hence the need to prepare future teachers for performing this task.
In the present monograph presents one approach to solving this problem through the formation of future teachers of "the way I parent," a deep awareness and understanding of the essence and structure of process of formation of own "image of the parent", the content of this phenomenon relevant content, development of representations about itself as about the parent, about other people and the world in General.
Intended for University students, primary school teachers, specialists in educational work, as well as for lecturers reading a course of lectures on subjects connected with pedagogy, psychology and ethic of family education.
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Chung, Simone Shu-Yeng, and Mike Douglass, eds. The Hard State, Soft City of Singapore. NL Amsterdam: Amsterdam University Press, 2020. http://dx.doi.org/10.5117/9789463729505.
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With Singapore serving as the subject of exploration, The Hard State, Soft City of Singapore explores the purview of imaginative representations of the city. Alongside the physical structures and associated practices that make up our lived environment, and conceptualized space engineered into material form by bureaucrats, experts and commercial interests, a perceptual layer of space is conjured out of people’s everyday life experiences. While such imaginative projections may not be as tangible as its functional designations, they are nonetheless equally vital and palpable. The richness of its inhabitants’ memories, aspirations and meaningful interpretations challenges the reduction of Singapore as a Generic City. Taking the imaginative field as the point of departure, the forms and modes of intellectual and creative articulations of Singapore’s urban condition probe the resilience of cities and the people who reside in them, through the images they convey or evoke as a means for collective expressions of human agency in placemaking.
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Grenander, Ulf, and Michael I. Miller. Pattern Theory. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780198505709.001.0001.
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Pattern Theory provides a comprehensive and accessible overview of the modern challenges in signal, data, and pattern analysis in speech recognition, computational linguistics, image analysis and computer vision. Aimed at graduate students in biomedical engineering, mathematics, computer science, and electrical engineering with a good background in mathematics and probability, the text includes numerous exercises and an extensive bibliography. Additional resources including extended proofs, selected solutions and examples are available on a companion website. The book commences with a short overview of pattern theory and the basics of statistics and estimation theory. Chapters 3-6 discuss the role of representation of patterns via condition structure. Chapters 7 and 8 examine the second central component of pattern theory: groups of geometric transformation applied to the representation of geometric objects. Chapter 9 moves into probabilistic structures in the continuum, studying random processes and random fields indexed over subsets of Rn. Chapters 10 and 11 continue with transformations and patterns indexed over the continuum. Chapters 12-14 extend from the pure representations of shapes to the Bayes estimation of shapes and their parametric representation. Chapters 15 and 16 study the estimation of infinite dimensional shape in the newly emergent field of Computational Anatomy. Finally, Chapters 17 and 18 look at inference, exploring random sampling approaches for estimation of model order and parametric representing of shapes.
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Kuppers, Petra. Dancing Disabled. Edited by Rebekah J. Kowal, Gerald Siegmund, and Randy Martin. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199928187.013.55.
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This chapter provides ways of linking phenomenology, feminist analysis, embodiment in dance, and corporeal representational politics. It engages Iris Marion Young’s argument about “Throwing Like a Girl,” addressing the pervasive structure at the heart of the meaning of femininity: the “disabling” object/subject bind that throws woman out of agency, and into the image. Using Young, Simone de Beauvoir, and Maurice Merleau Ponty as historical touchstones, the chapter shows how this agency/object bracket is at work in disability representation, and how examples of contemporary dance practice can fruitfully destabilize this scene. Dancers discussed include Gerda Koenig, a German dance artist and choreographer of DIN A 13, and Bill Shannon, a US dance artist.
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Asada, Minoru. Proprioception and body schema. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0018.
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Proprioception is our ability to sense the position of our own limbs and other body parts in space, and body schema is a body representation that allows both biological and artificial agents to execute their actions based on proprioception. The proprioceptive information used by current artificial agents (robots) is mainly related to posture (and its change) and consists of joint angles (joint velocities) given a linked structure. However, the counterpart in biological agents (humans and other animals) includes more complicated components with associated controversies concerning the relationship between the body schema and the body image. A new trend of constructive approaches has been attacking this topic using computational models and robots. This chapter provides an overview of the biology of proprioception and body representation, summarizes the classical use of body schema in robotics, and describes a series of constructive approaches that address some of the mysteries of body representation.
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de Vignemont, Frédérique. Taxonomies of Body Representations. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198735885.003.0009.
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This chapter considers the relationship between body representations, action, and bodily experiences. It first clarifies the conceptual landscape of body representations and stresses the conceptual and empirical difficulties that the current body schema/body image taxonomy faces, difficulties that can be explained by their constant interaction but not only. There is indeed a lack of precise understanding of the functional role of the body schema as opposed to the body image. Instead of these unclear notions, the chapter proposes distinguishing different types of body representations on the basis of their direction of fit and of their spatial organization. On the one hand, there is a purely descriptive body map that represents well-segmented categorical body parts, in which one can localize one’s sensations. On the other hand, there is a body map that is both descriptive and directive (i.e. pushmi-pullyu representation), and that encodes structural bodily affordances for action planning and control.
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Rothermel, Dennis. Becoming-Animal Cinema Narrative. Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474422734.003.0014.
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This chapter connects distinctive animal territories to specific uses of film language through a series of case studies, most notably Robert Bresson’s Au hasard Balthazar (1966), Michelangelo Frammartino’s Le Quattro Volte (2011), Bela Tarr’s The Turin Horse (2011), and Ang Lee’s Life of Pi (2012). Significantly, becoming-animal cannot be represented by conventional point-of-view and shot-reverse-shot editing (the structural mainstay of filmic suture), because it ties the animal to the conventional (and thus delimiting) human vectorial space of Deleuze’s action-image. Instead, inspired by Pier Paolo Pasolini’s seminal essay, ‘The “Cinema of Poetry”’, the chapter notes that all four filmmakers resort to a form of free-indirect discourse, whereby animality fills up the film from the inside as formative of the representation rather than rendering the subject within the structure of representation. Not unlike T.S. Eliot’s objective correlative, where the character’s subjectivity is presented objectively in and through the mise-en-scène as well as individual focalisation (in this case the character is also on-screen), animal perception is able to be expressed by a form of camera self-consciousness, what Deleuze calls ‘cinema a special kind of cinema where the camera makes itself felt.
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Clüver, Claus. Ekphrasis and Adaptation. Edited by Thomas Leitch. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199331000.013.26.
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In discussing word-and-image interactions, ekphrasis and adaptation are frequently cited as major instances of intermedial transposition. Ekphrasis, redefined as “the verbal representation of real or fictive configurations composed in a non-kinetic visual medium,” can occur in literary and non-literary texts and represent two- and three-dimensional images. Some ekphrastic texts can be read as fully developed intermedial translations; others may render readers’ encounters with visual images that the text does not actually transpose at all. Ekphrasis is a descriptive monomedial mode of intermedial reference. In contrast, adaptations incorporate transmedial elements of the source texts transposed into a new medium. Verbal texts are most frequently adapted to plurimedial media, but also to such mixed-media forms as the comic book. Novelizations of films or videogames exemplify adaptation to the verbal medium. More common is the adaptation to literary texts of structural devices employed in other media, as in the musicalization of fiction.
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Birtwistle, Andy. Meaning and Musicality. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190469894.003.0009.
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The chapter critically reappraises the work of the British experimental filmmaker John Smith, drawing on analyses of key films and interview material to explore his use of sound, music and voice. Smith’s films often engage self-reflexively with how sound creates or accepts meaning within an audiovisual context. Influenced by structural film practice of the 1960s and 1970s, and underpinned by a Brechtian concern with the politics of representation, Smith’s often humorous work both foregrounds and deconstructs the sound-image relations at work in dominant modes of cinematic representation. This analysis of Smith’s work identifies the political dynamic of the filmmaker’s use of sound, and addresses what is at stake—for both Smith and his audience—in the self-reflexive concern with audiovisual modes of representation. Examined within this context are Smith’s creative focus on the production of meaning and how this relates to aspects of musicality and abstraction in his work.
Book chapters on the topic "Image structure representation"
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Wang, Zhiyong, Zheru Chi, Dagan Feng, and S. Y. Cho. "Adaptive Processing of Tree-Structure Image Representation." In Advances in Multimedia Information Processing — PCM 2001, 989–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45453-5_133.
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Cai, Yu, Jinshan Pan, and Zhixun Su. "Blind Image Deblurring via Salient Structure Detection and Sparse Representation." In Image and Video Technology, 283–99. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92753-4_23.
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De Floriani, Leila. "A Triangle Based Data Structure For Multiresolution Surface Representation." In Image Analysis and Processing II, 277–85. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1007-5_30.
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Radstake, Niels, Peter J. F. Lucas, Marina Velikova, and Maurice Samulski. "Critiquing Knowledge Representation in Medical Image Interpretation Using Structure Learning." In Knowledge Representation for Health-Care, 56–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18050-7_5.
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Rocca, Luigi, and Enrico Puppo. "A Virtually Continuous Representation of the Deep Structure of Scale-Space." In Image Analysis and Processing – ICIAP 2013, 522–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41184-7_53.
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Wang, Yong, Xiaohui Zhao, Xiuling Mo, and Yuqing Wang. "Image Quality Assessment Based on Complex Representation of Structure Information." In Electrical, Information Engineering and Mechatronics 2011, 769–74. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2467-2_91.
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Islam, Mobarakol, Lalithkumar Seenivasan, Lim Chwee Ming, and Hongliang Ren. "Learning and Reasoning with the Graph Structure Representation in Robotic Surgery." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 627–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59716-0_60.
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Al-Dujaili, Abdullah, François Merciol, and Sébastien Lefèvre. "GraphBPT: An Efficient Hierarchical Data Structure for Image Representation and Probabilistic Inference." In Lecture Notes in Computer Science, 301–12. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18720-4_26.
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Qiao, Gang, Shangwei Liu, Qun Wei, Luting Wei, and Yingjie Wang. "Research on Block Segmentation and Assembly Technology of 3D Printing Structure." In Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology, 31–39. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3867-4_4.
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Kaur, Barleen, Paul Lemaître, Raghav Mehta, Nazanin Mohammadi Sepahvand, Doina Precup, Douglas Arnold, and Tal Arbel. "Improving Pathological Structure Segmentation via Transfer Learning Across Diseases." In Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data, 90–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33391-1_11.
Full textConference papers on the topic "Image structure representation"
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Hu, Junjie, and Terumasa Aoki. "NON-rigid structure from motion via sparse self-expressive representation." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8297141.
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Janssen, H. "Image representation in hypercolumnar structure by means of associative memory." In Close-Range Photogrammetry Meets Machine Vision. SPIE, 1990. http://dx.doi.org/10.1117/12.2294377.
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Zhang, Deming, Chang Lu, Xiaobo Lu, and Han Xue. "A Local Adaptive Structure Sparse Representation Algorithm for Image Reconstruction." In 2018 37th Chinese Control Conference (CCC). IEEE, 2018. http://dx.doi.org/10.23919/chicc.2018.8484007.
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Bennstrom, C. F., and J. R. Casas. "Object representation using colour, shape and structure criteria in a binary partition tree." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530599.
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Liu, Yang, Haixu Liu, Chenyu Liu, and Xueming Li. "Structure-constrained low-rank and partial sparse representation for image classification." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7026057.
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Quan, Siwen, Jie Ma, Fangyu Hu, Bin Fang, and Tao Ma. "Local voxelized structure for 3D local shape description: A binary representation." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296793.
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Zhang, Min, Yifan Li, and Yu Chen. "Completely Blind Image Quality Assessment Using Latent Quality Factor from Image Local Structure Representation." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682159.
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Zhang, M. C., and S. Chen. "A Binary Image Representation Scheme Using Irredundant Translation Invariant Data Structure." In 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, edited by William A. Pearlman. SPIE, 1989. http://dx.doi.org/10.1117/12.970044.
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Han, Ping, Xiaohong Yu, Xiaoguang Lu, and Hai Li. "PolSAR image speckle reduction based on sparse representation and structure characteristics." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6855188.
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Liu, Fan, Jinhui Tang, Yan Song, Xinguang Xiang, and Zhenmin Tang. "Local structure based sparse representation for face recognition with single sample per person." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025143.
Full textReports on the topic "Image structure representation"
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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.
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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-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.