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Letteratura scientifica selezionata sul tema "Convolutional dictionary"

Autore: Grafiati

Pubblicato: 22 febbraio 2025

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Articoli di riviste sul tema "Convolutional dictionary"

Song, Andrew, Bahareh Tolooshams e Demba Ba. "Gaussian Process Convolutional Dictionary Learning". IEEE Signal Processing Letters 29 (2022): 95–99. http://dx.doi.org/10.1109/lsp.2021.3127471.

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Gao, Fangyuan, Xin Deng, Mai Xu, Jingyi Xu e Pier Luigi Dragotti. "Multi-Modal Convolutional Dictionary Learning". IEEE Transactions on Image Processing 31 (2022): 1325–39. http://dx.doi.org/10.1109/tip.2022.3141251.

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Yoon, Jongsu, e Yoonsik Choe. "Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding". Applied Sciences 10, n. 12 (26 giugno 2020): 4395. http://dx.doi.org/10.3390/app10124395.

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Retinex theory represents the human visual system by showing the relative reflectance of an object under various illumination conditions. A feature of this human visual system is color constancy, and the Retinex theory is designed in consideration of this feature. The Retinex algorithms have been popularly used to effectively decompose the illumination and reflectance of an object. The main aim of this paper is to study image enhancement using convolution sparse coding and sparse representations of the reflectance component in the Retinex model over a learned dictionary. To realize this, we use the convolutional sparse coding model to represent the reflectance component in detail. In addition, we propose that the reflectance component can be reconstructed using a trained general dictionary by using convolutional sparse coding from a large dataset. We use singular value decomposition in limited memory to construct a best reflectance dictionary. This allows the reflectance component to provide improved visual quality over conventional methods, as shown in the experimental results. Consequently, we can reduce the difference in perception between humans and machines through the proposed Retinex-based image enhancement.

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Chun, Il Yong, e Jeffrey A. Fessler. "Convolutional Dictionary Learning: Acceleration and Convergence". IEEE Transactions on Image Processing 27, n. 4 (aprile 2018): 1697–712. http://dx.doi.org/10.1109/tip.2017.2761545.

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Song, Andrew H., Francisco J. Flores e Demba Ba. "Convolutional Dictionary Learning With Grid Refinement". IEEE Transactions on Signal Processing 68 (2020): 2558–73. http://dx.doi.org/10.1109/tsp.2020.2986897.

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Li, Jing, Xiao Wei, Fengpin Wang e Jinjia Wang. "IPGM: Inertial Proximal Gradient Method for Convolutional Dictionary Learning". Electronics 10, n. 23 (3 dicembre 2021): 3021. http://dx.doi.org/10.3390/electronics10233021.

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Inspired by the recent success of the proximal gradient method (PGM) and recent efforts to develop an inertial algorithm, we propose an inertial PGM (IPGM) for convolutional dictionary learning (CDL) by jointly optimizing both an ℓ2-norm data fidelity term and a sparsity term that enforces an ℓ1 penalty. Contrary to other CDL methods, in the proposed approach, the dictionary and needles are updated with an inertial force by the PGM. We obtain a novel derivative formula for the needles and dictionary with respect to the data fidelity term. At the same time, a gradient descent step is designed to add an inertial term. The proximal operation uses the thresholding operation for needles and projects the dictionary to a unit-norm sphere. We prove the convergence property of the proposed IPGM algorithm in a backtracking case. Simulation results show that the proposed IPGM achieves better performance than the PGM and slice-based methods that possess the same structure and are optimized using the alternating-direction method of multipliers (ADMM).

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Molla, Md Rony, e Ma Jian Fen. "Convolutional Sparse Coding Multiple Instance Learning for Whole Slide Image Classification". International Journal of Advanced Engineering Research and Science 10, n. 12 (2023): 096–104. http://dx.doi.org/10.22161/ijaers.1012.10.

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Multiple Instance Learning (MIL) is commonly utilized in weakly supervised whole slide image (WSI) classification. MIL techniques typically involve a feature embedding step using a pretrained feature extractor, then an aggregator that aggregates the embedded instances into predictions. Current efforts aim to enhance these sections by refining feature embeddings through self-supervised pretraining and modeling correlations between instances. In this paper, we propose a convolutional sparsely coded MIL (CSCMIL) that utilizes convolutional sparse dictionary learning to simultaneously address these two aspects. Sparse dictionary learning consists of filters or kernels that are applied with convolutional operations and utilizes an overly comprehensive dictionary to represent instances as sparse linear combinations of atoms, thereby capturing their similarities. Straightforwardly built into existing MIL frameworks, the suggested CSC module has an affordable computation cost. Experiments on various datasets showed that the suggested CSC module improved performance by 3.85% in AUC and 4.50% in accuracy, equivalent to the SimCLR pretraining (4.21% and 4.98%) significantly of current MIL approaches.

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Martin-del-Campo, Sergio, Fredrik Sandin e Stephan Schnabel. "Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning". Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 235, n. 4 (5 gennaio 2021): 660–75. http://dx.doi.org/10.1177/1748006x20984260.

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We analyze vibration signals from wind turbines with dictionary learning and investigate the relation between dictionary distances and faults occurring in a wind turbine output shaft rolling element bearing and gearbox under different data and compute constraints. Dictionary learning is an unsupervised machine learning method for signal processing, which permits learning a set of signal-specific features that have been used to monitor the condition of rotating machines, including wind turbines. Dictionary distance is one such feature, and its effectiveness depends on an adequate selection of the dictionary learning hyperparameters and the data availability, which typically is constrained in condition monitoring systems for remotely located wind farms. Here we evaluate the characteristics of the dictionary distance feature under healthy and faulty conditions of the wind turbines using different options for the selection of the pretrained dictionary, the sparsity of the signal model which determines the compute requirements, and the interval between data samples. Furthermore, we compare the dictionary distance feature to the typical time-domain features used in condition monitoring. We find that the dictionary distance based feature of a faulty wind turbine deviates by a factor of two or more from the population distribution several weeks before the gearbox bearing fault was reported, using a data sampling interval as long as 24 h and a model sparsity as low as 2.5%.

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Cheng, Ruihong, Huajun Wang e Ping Luo. "Remote sensing image super-resolution using multi-scale convolutional sparse coding network". PLOS ONE 17, n. 10 (26 ottobre 2022): e0276648. http://dx.doi.org/10.1371/journal.pone.0276648.

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With the development of convolutional neural networks, impressive success has been achieved in remote sensing image super-resolution. However, the performance of super-resolution reconstruction is unsatisfactory due to the lack of details in remote sensing images when compared to natural images. Therefore, this paper presents a novel multiscale convolutional sparse coding network (MCSCN) to carry out the remote sensing images SR reconstruction with rich details. The MCSCN, which consists of a multiscale convolutional sparse coding module (MCSCM) with dictionary convolution units, can improve the extraction of high frequency features. We can obtain more plentiful feature information by combining multiple sizes of sparse features. Finally, a layer based on sub-pixel convolution that combines global and local features takes as the reconstruction block. The experimental results show that the MCSCN gains an advantage over several existing state-of-the-art methods in terms of peak signal-to-noise ratio and structural similarity.

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Xu, Yulong, Yingying Zhao, Hao Rong, Fangfang Liu, Yali Lv e Honglei Zhu. "Semantic Analysis of Public Health Medical Issues Based on Convolution Neural Networks". Mobile Information Systems 2022 (30 agosto 2022): 1–9. http://dx.doi.org/10.1155/2022/2392207.

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Text mining and semantic analysis of medical public health issues are the main points for intelligent medical interaction, but less relevant research has been done on them. This article conceives a convolutional neural network for the semantic classification of public health medical issues. The dual convolution layer is used to further reduce the dimension of the data, extract more in-depth information from the data, and map the features. Each convolution layer includes several convolution nuclei to extract semantic characteristics, and then, the complete connection layer is input to the classifier to obtain the results of the classification. To check the classification effect, the dictionary artificial construction and the double hidden-layers neuronal network are used for semantic classification, and the three methods are compared and tested on the six real datasets. The experimental results show that when the quality of the dataset is high, the convolution neural network method proposed in this paper exceeds the last two methods. The proposed method is higher than the construction of the artificial dictionary and the double hidden-layers neural network in the recall rate: 0.153 and 0.037, and greater than 0.07 and 0.01 for the F1 measure rate, respectively. When the quality of the dataset is general, the models of the three methods do not give good classification results. Finally, it is concluded that the convolutional neural network method conceived has a good semantic recognition performance in public health medical issues.

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Più fonti

Tesi sul tema "Convolutional dictionary"

Quesada, Pacora Jorge Gerardo. "Separable dictionary learning for convolutional sparse coding via split updates". Master's thesis, Pontificia Universidad Católica del Perú, 2019. http://hdl.handle.net/20.500.12404/14209.

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The increasing ubiquity of Convolutional Sparse Representation techniques for several image processing tasks (such as object recognition and classification, as well as image denoising) has recently sparked interest in the use of separable 2D dictionary filter banks (as alternatives to standard nonseparable dictionaries) for efficient Convolutional Sparse Coding (CSC) implementations. However, existing methods approximate a set of K non-separable filters via a linear combination of R (R << K) separable filters, which puts an upper bound on the latter’s quality. Furthermore, this implies the need to learn first the whole set of non-separable filters, and only then compute the separable set, which is not optimal from a computational perspective. In this context, the purpose of the present work is to propose a method to directly learn a set of K separable dictionary filters from a given image training set by drawing ideas from standard Convolutional Dictionary Learning (CDL) methods. We show that the separable filters obtained by the proposed method match the performance of an equivalent number of non-separable filters. Furthermore, the computational performance of this learning method is shown to be substantially faster than a state-of-the-art non-separable CDL method when either the image training set or the filter set are large. The method and results presented here have been published [1] at the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018). Furthermore, a preliminary approach (mentioned at the end of Chapter 2) was also published at ICASSP 2017 [2]. The structure of the document is organized as follows. Chapter 1 introduces the problem of interest and outlines the scope of this work. Chapter 2 provides the reader with a brief summary of the relevant literature in optimization, CDL and previous use of separable filters. Chapter 3 presents the details of the proposed method and some implementation highlights. Chapter 4 reports the attained computational results through several simulations. Chapter 5 summarizes the attained results and draws some final conclusions.
Tesis

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Silva, Obregón Gustavo Manuel. "Efficient algorithms for convolutional dictionary learning via accelerated proximal gradient". Master's thesis, Pontificia Universidad Católica del Perú, 2019. http://hdl.handle.net/20.500.12404/13903.

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Convolutional sparse representations and convolutional dictionary learning are mathematical models that consist in representing a whole signal or image as a sum of convolutions between dictionary filters and coefficient maps. Unlike the patch-based counterparts, these convolutional forms are receiving an increase attention in multiple image processing tasks, since they do not present the usual patchwise drawbacks such as redundancy, multi-evaluations and non-translational invariant. Particularly, the convolutional dictionary learning (CDL) problem is addressed as an alternating minimization between coefficient update and dictionary update stages. A wide number of different algorithms based on FISTA (Fast Iterative Shrinkage-Thresholding Algorithm), ADMM (Alternating Direction Method of Multipliers) and ADMM consensus frameworks have been proposed to efficiently solve the most expensive steps of the CDL problem in the frequency domain. However, the use of the existing methods on large sets of images is computationally restricted by the dictionary update stage. The present thesis report is strategically organized in three parts. On the first part, we introduce the general topic of the CDL problem and the state-of-the-art methods used to deal with each stage. On the second part, we propose our first computationally efficient method to solve the entire CDL problem using the Accelerated Proximal Gradient (APG) framework in both updates. Additionally, a novel update model reminiscent of the Block Gauss-Seidel (BGS) method is incorporated to reduce the number of estimated components during the coefficient update. On the final part, we propose another alternative method to address the dictionary update stage based on APG consensus approach. This last method considers particular strategies of theADMMconsensus and our first APG framework to develop a less complex solution decoupled across the training images. In general, due to the lower number of operations, our first approach is a better serial option while our last approach has as advantage its independent and highly parallelizable structure. Finally, in our first set of experimental results, which is composed of serial implementations, we show that our first APG approach provides significant speedup with respect to the standard methods by a factor of 1:6 5:3. A complementary improvement by a factor of 2 is achieved by using the reminiscent BGS model. On the other hand, we also report that the second APG approach is the fastest method compared to the state-of-the-art consensus algorithm implemented in serial and parallel. Both proposed methods maintain comparable performance as the other ones in terms of reconstruction metrics, such as PSNR, SSIM and sparsity, in denoising and inpainting tasks.
Tesis

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Allain, Cédric. "Temporal point processes and scalable convolutional dictionary learning : a unified framework for m/eeg signal analysis in neuroscience". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG008.

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Dans le domaine de l'imagerie cérébrale non invasive, la magnéto- et l'électroencéphalographie (M/EEG) offrent un précieux aperçu des activités neuronales. Les données enregistrées consistent en des séries temporelles multivariées qui fournissent des informations sur les processus cognitifs et sont souvent complétées par des détails auxiliaires liés au paradigme expérimental, tels que l'horodatage des stimuli externes ou des actions entreprises par les sujets. En outre, l'ensemble des données peut inclure des enregistrements de plusieurs sujets, ce qui facilite les analyses en population.Cette thèse de doctorat présente un nouveau cadre pour l'analyse des signaux M/EEG qui synergise l'Apprentissage Convolutif de Dictionnaire (CDL) et les Processus Ponctuels Temporels (TPP). Ce travail est divisé en deux composantes principales : les avancées en modélisation temporelle et le passage à l'échelle computationnelle. En matière de modélisation temporelle, deux nouveaux modèles de processus ponctuels sont introduits, accompagnés de méthodes d'inférence efficaces pour capturer les activités neuronales liées aux tâches. La méthode proposée d'Inférence Discrétisée Rapide pour les Processus de Hawkes (FaDIn) a également des implications pour des applications plus larges. De plus, ce travail aborde les défis computationnels de l'analyse des données M/EEG à grande échelle basée sur le CDL, en introduisant un nouvel algorithme robuste de CDL avec fenêtrage stochastique. Cet algorithme permet de traiter efficacement les signaux entachés d'artefacts ainsi que les études de population à grande échelle. Le CDL populationnelle a ensuite été utilisée sur le grand ensemble de données en libre accès Cam-CAN, révélant des aspects de l'activité neuronale liée à l'âge
In the field of non-invasive brain imaging, Magnetoencephalography and Electroencephalography (M/EEG) offer invaluable insights into neural activities. The recorded data consist of multivariate time series that provide information about cognitive processes and are often complemented by auxiliary details related to the experimental paradigm, such as timestamps of external stimuli or actions undertaken by the subjects. Additionally, the dataset may include recordings from multiple subjects, facilitating population- level analyses.This doctoral research presents a novel framework for M/EEG signal analysis that synergizes Convolutional Dictionary Learning (CDL) and Temporal Point Processes (TPPs). The work is segmented into two primary components: temporal modeling advancements and computational scalability. For temporal modeling, two novel point process models are introduced with efficient inference methods to capture task-specific neural activities. The proposed Fast Discretized Inference for Hawkes Processes (FaDIn) method also has implications for broader applications. Additionally, this work addresses the computational challenges of large-scale M/EEG data CDL-based analysis, by introducing a novel Stochastic Robust Windowing CDL algorithm. This algorithm allows to process efficiently artifact-ridden signals as well as large population studies. Population CDL was then used on the large open-access dataset Cam-CAN, shedding light on age-related neural activity

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Moreau, Thomas. "Représentations Convolutives Parcimonieuses -- application aux signaux physiologiques et interpétabilité de l'apprentissage profond". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLN054/document.

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Les représentations convolutives extraient des motifs récurrents qui aident à comprendre la structure locale dans un jeu de signaux. Elles sont adaptées pour l’analyse des signaux physiologiques, qui nécessite des visualisations mettant en avant les informations pertinentes. Ces représentations sont aussi liées aux modèles d’apprentissage profond. Dans ce manuscrit, nous décrivons des avancées algorithmiques et théoriques autour de ces modèles. Nous montrons d’abord que l’Analyse du Spectre Singulier permet de calculer efficacement une représentation convolutive. Cette représentation est dense et nous décrivons une procédure automatisée pour la rendre plus interprétable. Nous proposons ensuite un algorithme asynchrone, pour accélérer le codage parcimonieux convolutif. Notre algorithme présente une accélération super-linéaire. Dans une seconde partie, nous analysons les liens entre représentations et réseaux de neurones. Nous proposons une étape d’apprentissage supplémentaire, appelée post-entraînement, qui permet d’améliorer les performances du réseau entraîné, en s’assurant que la dernière couche soit optimale. Puis nous étudions les mécanismes qui rendent possible l’accélération du codage parcimonieux avec des réseaux de neurones. Nous montrons que cela est lié à une factorisation de la matrice de Gram du dictionnaire. Finalement, nous illustrons l’intérêt de l’utilisation des représentations convolutives pour les signaux physiologiques. L’apprentissage de dictionnaire convolutif est utilisé pour résumer des signaux de marche et le mouvement du regard est soustrait de signaux oculométriques avec l’Analyse du Spectre Singulier
Convolutional representations extract recurrent patterns which lead to the discovery of local structures in a set of signals. They are well suited to analyze physiological signals which requires interpretable representations in order to understand the relevant information. Moreover, these representations can be linked to deep learning models, as a way to bring interpretability intheir internal representations. In this disserta tion, we describe recent advances on both computational and theoretical aspects of these models.First, we show that the Singular Spectrum Analysis can be used to compute convolutional representations. This representation is dense and we describe an automatized procedure to improve its interpretability. Also, we propose an asynchronous algorithm, called DICOD, based on greedy coordinate descent, to solve convolutional sparse coding for long signals. Our algorithm has super-linear acceleration.In a second part, we focus on the link between representations and neural networks. An extra training step for deep learning, called post-training, is introduced to boost the performances of the trained network by making sure the last layer is optimal. Then, we study the mechanisms which allow to accelerate sparse coding algorithms with neural networks. We show that it is linked to afactorization of the Gram matrix of the dictionary.Finally, we illustrate the relevance of convolutional representations for physiological signals. Convolutional dictionary learning is used to summarize human walk signals and Singular Spectrum Analysis is used to remove the gaze movement in young infant’s oculometric recordings

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Bussy, Victor. "Integration of a priori data to optimise industrial X-ray tomographic reconstruction". Electronic Thesis or Diss., Lyon, INSA, 2024. http://www.theses.fr/2024ISAL0116.

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Cette thèse explore des sujets de recherche dans le domaine du contrôle non destructif industriel par rayons X (CND). L’application de la tomographie CT s’est considérablement étendue et son utilisation s'est intensifiée dans de nombreux secteurs industriels. En raison des exigences croissantes et des contraintes sur les processus de contrôle, la CT se doit de constamment évoluer et s'adapter. Que ce soit en termes de qualité de reconstruction ou en temps d’inspection, la tomographie par rayons X est en constante progression, notamment dans ce qu’on appelle la stratégie de vues éparses. Cette stratégie consiste à reconstruire un objet en utilisant le minimum possible de projections radiologiques tout en maintenant une qualité de reconstruction satisfaisante. Cette approche réduit les temps d'acquisition et les coûts associés. La reconstruction en vues éparses constitue un véritable défi car le problème tomographique est mal conditionné, on le dit mal posé. De nombreuses techniques ont été développées pour surmonter cet obstacle, dont plusieurs sont basées sur l'utilisation d'informations a priori lors du processus de reconstruction. En exploitant les données et les connaissances disponibles avant l'expérience, il est possible d'améliorer le résultat de la reconstruction malgré le nombre réduit de projections. Dans notre contexte industriel, par exemple, le modèle de conception assistée par ordinateur (CAO) de l’objet est souvent disponible, ce qui représente une information précieuse sur la géométrie de l’objet étudié. Néanmoins, il est important de noter que le modèle CAO ne fournit qu’une représentation approximative de l'objet. En CND ou en métrologie, ce sont précisément les différences entre un objet et son modèle CAO qui sont d'intérêt. Par conséquent, l'intégration d'informations a priori est complexe car ces informations sont souvent "approximatives" et ne peuvent pas être utilisées telles quelles. Nous proposons plutôt d’utiliser judicieusement les informations géométriques disponibles à partir du modèle CAO à chaque étape du processus. Nous ne proposons donc pas une méthode, mais une méthodologie pour l'intégration des informations géométriques a priori lors la reconstruction tomographique par rayons X
This thesis explores research topics in the field of industrial non-destructive testing (NDT) using X-rays. The application of CT tomography has significantly expanded, and its use has intensified across many industrial sectors. Due to increasing demands and constraints on inspection processes, CT must continually evolve and adapt. Whether in terms of reconstruction quality or inspection time, X-ray tomography is constantly progressing, particularly in the so-called sparse-view strategy. This strategy involves reconstructing an object using the minimum possible number of radiographic projections while maintaining satisfactory reconstruction quality. This approach reduces acquisition times and associated costs. Sparse-view reconstruction poses a significant challenge as the tomographic problem is ill-conditioned, or, as it is often described, ill-posed. Numerous techniques have been developed to overcome this obstacle, many of which rely on leveraging prior information during the reconstruction process. By exploiting data and knowledge available before the experiment, it is possible to improve reconstruction results despite the reduced number of projections. In our industrial context, for example, the computer-aided design (CAD) model of the object is often available, which provides valuable information about the geometry of the object under study. However, it is important to note that the CAD model only offers an approximate representation of the object. In NDT or metrology, it is precisely the differences between an object and its CAD model that are of interest. Therefore, integrating prior information is complex, as this information is often "approximate" and cannot be used as is. Instead, we propose to judiciously use the geometric information available from the CAD model at each step of the process. We do not propose a single method but rather a methodology for integrating prior geometric information during X-ray tomographic reconstruction

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Lee, Yen-De, e 李彥德. "Visual Center Preprocessing and Cloud Dictionary Correction Technique Applied to Text Recognition with Convolutional Neural Networks". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/k9tf7e.

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碩士
國立中正大學
電機工程研究所
105
Text is the evolution of humanity after many centuries and the symbol of communication between people. As the image recognition technology matures, the recognition rate of the text in the natural scene is accurate. In many technologies, the deep learning is the best. Convolutional neural network in deep learning has been widely used in text detection and recognition in recent years. However, convolutional neural networks are computationally complex and time-consuming. Our research goals are reduce time-consuming by image preprocessing and word correction by using cloud dictionary. First, searching text areas and significant text priority are used to search visual centers. Visual centers are used to reduce the time-consuming of character recognition with convolution neural networks. The text detection uses MSERs to avoid time-consuming of the sliding window method. Second, the previous people are usually to obtain correct word by searching their own dictionaries. We use cloud dictionary for more efficient recognition and greater fault tolerance.

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Capitoli di libri sul tema "Convolutional dictionary"

Zheng, Yanze, e Jian Dong. "Convolutional Dictionary Super-Resolution Network for Removing CT Metal Artifacts". In Signals and Communication Technology, 3–19. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-78131-5_1.

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Sandilya, Mrinmoy, e S. R. Nirmala. "Compressed Sensing MRI Reconstruction Using Convolutional Dictionary Learning and Laplacian Prior". In IOT with Smart Systems, 661–69. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-3945-6_65.

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Mao, Gui-Han, Jian-Cong Fan e Yi-Ming Zhang. "Convolutional Neural Network Combined with Emotional Dictionary Apply in Chinese Text Emotional Classification". In Advances in Smart Vehicular Technology, Transportation, Communication and Applications, 85–96. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1209-1_9.

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Zhang, Chengfang, e Xingchun Yang. "Image Fusion Based on Masked Online Convolutional Dictionary Learning with Surrogate Function Approach". In Advances in Intelligent Systems and Computing, 70–74. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5887-0_10.

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Zhang, Chengfang, Ziliang Feng, Chao Zhang e Kai Yi. "An Efficient Medical Image Fusion via Online Convolutional Sparse Coding with Sample-Dependent Dictionary". In Lecture Notes in Computer Science, 3–13. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46317-4_1.

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Ayllón-Gavilán, Rafael, David Guijo-Rubio, Pedro Antonio Gutiérrez e César Hervás-Martínez. "O-Hydra: A Hybrid Convolutional and Dictionary-Based Approach to Time Series Ordinal Classification". In Advances in Artificial Intelligence, 50–60. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62799-6_6.

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Zhang, Chengfang, e Xingchun Yang. "Visible and Infrared Image Fusion Based on Online Convolutional Dictionary Learning with Sparse Matrix Computation". In Advances in Wireless Communications and Applications, 123–28. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5697-5_15.

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Zhang, Chengfang, e Xingchun Yang. "Visible and Infrared Image Fusion Based on Masked Online Convolutional Dictionary Learning with Frequency Domain Computation". In New Developments of IT, IoT and ICT Applied to Agriculture, 177–82. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5073-7_18.

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Dong, Qunxi, Jie Zhang, Qingyang Li, Pau M. Thompson, Richard J. Caselli, Jieping Ye e Yalin Wang. "Multi-task Dictionary Learning Based on Convolutional Neural Networks for Longitudinal Clinical Score Predictions in Alzheimer’s Disease". In Human Brain and Artificial Intelligence, 21–35. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1398-5_2.

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Elbasiony, Reda, Walid Gomaa e Tetsuya Ogata. "Deep 3D Pose Dictionary: 3D Human Pose Estimation from Single RGB Image Using Deep Convolutional Neural Network". In Artificial Neural Networks and Machine Learning – ICANN 2018, 310–20. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01424-7_31.

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Atti di convegni sul tema "Convolutional dictionary"

Yoshida, Takehiro, Ibuki Muta e Yoshimitsu Kuroki. "Classification with Dictionary Filters in Convolutional Sparse Representation". In 2024 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 1–4. IEEE, 2024. https://doi.org/10.1109/ispacs62486.2024.10869135.

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Kobayashi, Takuma, Kanata Hayashi e Yoshimitsu Kuroki. "Efficient Convolutional Dictionary Learning When The Number Of Classes In The Dataset Increases". In 2024 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 1–4. IEEE, 2024. https://doi.org/10.1109/ispacs62486.2024.10869089.

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Gondo, Yudai, Hiroto Take e Yoshimitsu Kuroki. "Improving sparsity of Convolutional Sparse Representation using a combination of a few dictionary filters". In 2024 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 1–3. IEEE, 2024. https://doi.org/10.1109/ispacs62486.2024.10868219.

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Liu, Jialin, Cristina Garcia-Cardona, Brendt Wohlberg e Wotao Yin. "Online convolutional dictionary learning". In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296573.

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Papyan, Vardan, Yaniv Romano, Michael Elad e Jeremias Sulam. "Convolutional Dictionary Learning via Local Processing". In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017. http://dx.doi.org/10.1109/iccv.2017.566.

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Venkatakrishnan, S. V., e Brendt Wohlberg. "Convolutional Dictionary Regularizers for Tomographic Inversion". In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682637.

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Zazo, Javier, Bahareh Tolooshams, Demba Ba e Harvard John A. Paulson. "Convolutional Dictionary Learning in Hierarchical Networks". In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2019. http://dx.doi.org/10.1109/camsap45676.2019.9022440.

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Garcia-Cardona, Cristina, e Brendt Wohlberg. "Subproblem coupling in convolutional dictionary learning". In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296571.

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Xu, Lijun, Ying Wang e Yijia Zhou. "Dictionary learning in convolutional sparse representation". In Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), a cura di Shi Jin e Wanyang Dai. SPIE, 2023. http://dx.doi.org/10.1117/12.2672444.

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Garcia-Cardona, Cristina, e Brendt Wohlberg. "Convolutional Dictionary Learning for Multi-Channel Signals". In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645108.

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