Relevant bibliographies by topics / Information extraction and fusion

Academic literature on the topic 'Information extraction and fusion'

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Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Information extraction and fusion"

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Yang, Zhongguo, Mingzhu Zhang, Zhongmei Zhang, Han Li, Chen Liu, and Sikandar Ali. "Lecture Information Service Based on Multiple Features Fusion." International Journal of Software Engineering and Knowledge Engineering 31, no. 04 (April 2021): 545–62. http://dx.doi.org/10.1142/s0218194021400076.

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Information service is always a hot topic especially when the Web is accessible anywhere. In university, lecture information is very important for students and teachers who want to take part in academic meetings. Therefore, lecture news extraction is an important and imperative task. Many open information extraction methods have been proposed, but due to the high heterogeneity of websites, this task is still a challenge. In this paper, we propose a method based on fusing multiple features to locate lecture news on the university website. These features include the linked relationship between parent webpage and child webpages, the visual similarity, and the semantics of webpages. Additionally, this paper provides an information service based on a main content extraction algorithm for extracting the lecture information. Stable and invariant features enable the proposed method to adapt to various kinds of campus websites. The experiments conducted on 50 websites show the effectiveness and efficiency of the provided service.
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Zhang, Xin, Li Yang, and Yan Zhang. "Multi-Source Information Fusion Based on Data Driven." Applied Mechanics and Materials 40-41 (November 2010): 121–26. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.121.

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Take data driven method as theoretical basis, study multi-source information fusion technology. Using online and off-line data of the fusion system, does not rely on system's mathematical model, has avoided question about system modeling by mechanism. Uses principal component analysis method, rough set theory, Support Vector Machine(SVM) and so on, three method fusions and supplementary, through information processing and feature extraction to system's data-in, catches the most important information to lower dimensional space, realizes knowledge reduction. From data level, characteristic level, decision-making three levels realize information fusion. The example indicated that reduced computational complexity, reduced information loss in the fusion process, and enhanced the fusion accuracy.
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Liu, Xia, Zhijing Xu, and Kan Huang. "Multimodal Emotion Recognition Based on Cascaded Multichannel and Hierarchical Fusion." Computational Intelligence and Neuroscience 2023 (January 5, 2023): 1–18. http://dx.doi.org/10.1155/2023/9645611.

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Humans express their emotions in a variety of ways, which inspires research on multimodal fusion-based emotion recognition that utilizes different modalities to achieve information complementation. However, extracting deep emotional features from different modalities and fusing them remain a challenging task. It is essential to exploit the advantages of different extraction and fusion approaches to capture the emotional information contained within and across modalities. In this paper, we present a novel multimodal emotion recognition framework called multimodal emotion recognition based on cascaded multichannel and hierarchical fusion (CMC-HF), where visual, speech, and text signals are simultaneously utilized as multimodal inputs. First, three cascaded channels based on deep learning technology perform feature extraction for the three modalities separately to enhance deeper information extraction ability within each modality and improve recognition performance. Second, an improved hierarchical fusion module is introduced to promote intermodality interactions of three modalities and further improve recognition and classification accuracy. Finally, to validate the effectiveness of the designed CMC-HF model, some experiments are conducted to evaluate two benchmark datasets, IEMOCAP and CMU-MOSI. The results show that we achieved an almost 2%∼3.2% increase in accuracy of the four classes for the IEMOCAP dataset as well as an improvement of 0.9%∼2.5% in the average class accuracy for the CMU-MOSI dataset when compared to the existing state-of-the-art methods. The ablation experimental results indicate that the cascaded feature extraction method and the hierarchical fusion method make a significant contribution to multimodal emotion recognition, suggesting that the three modalities contain deeper information interactions of both intermodality and intramodality. Hence, the proposed model has better overall performance and achieves higher recognition efficiency and better robustness.
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Deren, LI, and SHAO Juliang. "HOUSE EXTRACTION WITH MULTIRESOLUTION ANALYSIS AND INFORMATION FUSION." Geo-spatial Information Science 1, no. 1 (October 1998): 6–12. http://dx.doi.org/10.1080/10095020.1998.10553277.

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Liu, Tingting, Jian Yin, and Qingfeng Qin. "MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding." Applied Sciences 12, no. 16 (August 17, 2022): 8218. http://dx.doi.org/10.3390/app12168218.

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Depending on the type of information network, information network embedding is classified into homogeneous information network embedding and heterogeneous information network (HIN) embedding. Compared with the homogeneous network, HIN composition is more complex and contains richer semantics. At present, the research on homogeneous information network embedding is relatively mature. However, if the homogeneous information network model is directly applied to HIN, it will cause incomplete information extraction. It is necessary to build a specialized embedding model for HIN. Learning information network embedding based on the meta-path is an effective approach to extracting semantic information. Nevertheless, extracting HIN embedding only from a single view will cause information loss. To solve these problems, we propose a multi-view fusion-based HIN embedding model, called MFHE. MFHE includes four parts: node feature space transformation, subview information extraction, multi-view information fusion, and training. MFHE divides HIN into different subviews based on meta-paths, models the local information accurately in the subviews based on the multi-head attention mechanism, and then fuses subview information through a spatial matrix. In this paper, we consider the relationship between subviews; thus, the MFHE is applicable to complex HIN embedding. Experiments are conducted on ACM and DBLP datasets. Compared with baselines, the experimental results demonstrate that the effectiveness of MFHE and HIN embedding has been improved.
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Han, Yan Bin, Geng Shi Zhang, and Jin Ping Li. "A Feature Extraction Strategy Based on Multiple Color Information." Advanced Materials Research 433-440 (January 2012): 6175–81. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6175.

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In this paper, a feature extraction strategy based on multiple color information fusion was proposed. Firstly this method started with analyzing the transform formula of color space, which transform was mainly thinking about RGB color space to other color spaces. Secondly by analyzing the characteristic of every color space in describing the actual color information, the advantages and disadvantages of every color space were showed. Thirdly through above conclusion, the algorithm which extracted the target feature only using single color information was defective, and then the strategy based on multiple color information fusion was proposed. Lastly the detail fusion strategy was given, which fused the probability distributed information of multiple color into the last probability distributed information as the target feature. The feature extraction strategy in this paper is verified by the camshift algorithm. The results show that the multiple color information fusion can improve the tracking performance of moving target.
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Wang, Wenya, and Sinno Jialin Pan. "Integrating Deep Learning with Logic Fusion for Information Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9225–32. http://dx.doi.org/10.1609/aaai.v34i05.6460.

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Information extraction (IE) aims to produce structured information from an input text, e.g., Named Entity Recognition and Relation Extraction. Various attempts have been proposed for IE via feature engineering or deep learning. However, most of them fail to associate the complex relationships inherent in the task itself, which has proven to be especially crucial. For example, the relation between 2 entities is highly dependent on their entity types. These dependencies can be regarded as complex constraints that can be efficiently expressed as logical rules. To combine such logic reasoning capabilities with learning capabilities of deep neural networks, we propose to integrate logical knowledge in the form of first-order logic into a deep learning system, which can be trained jointly in an end-to-end manner. The integrated framework is able to enhance neural outputs with knowledge regularization via logic rules, and at the same time update the weights of logic rules to comply with the characteristics of the training data. We demonstrate the effectiveness and generalization of the proposed model on multiple IE tasks.
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Zhao, Jiang, Jiao Wang, and Meng Shang. "Fault Diagnosis Method of Time Domain and Time-Frequency Domain Based on Information Fusion." Applied Mechanics and Materials 300-301 (February 2013): 635–39. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.635.

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On account of the problem that traditional pipe leakage diagnosis method is not highly accuracy .this paper come up with a method that based on pipe leakage diagnosis method of neural network information fusion. Giving the stress wave time domain feature extraction index data algorithm and wavelet packet extraction each the frequency band energy algorithm, by comparing with these results of the pressure wave time domain feature index data, time-frequency extraction energy values and fault diagnosis of both information fusion ,which show the neural network information fusion method that is used for pipe leakage diagnosis that is feasible and effective.
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Yan, Zhiqiang, Hongyuan Wang, Qianhao Ning, and Yinxi Lu. "Robust Image Matching Based on Image Feature and Depth Information Fusion." Machines 10, no. 6 (June 8, 2022): 456. http://dx.doi.org/10.3390/machines10060456.

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In this paper, we propose a robust image feature extraction and fusion method to effectively fuse image feature and depth information and improve the registration accuracy of RGB-D images. The proposed method directly splices the image feature point descriptors with the corresponding point cloud feature descriptors to obtain the fusion descriptor of the feature points. The fusion feature descriptor is constructed based on the SIFT, SURF, and ORB feature descriptors and the PFH and FPFH point cloud feature descriptors. Furthermore, the registration performance based on fusion features is tested through the RGB-D datasets of YCB and KITTI. ORBPFH reduces the false-matching rate by 4.66~16.66%, and ORBFPFH reduces the false-matching rate by 9~20%. The experimental results show that the RGB-D robust feature extraction and fusion method proposed in this paper is suitable for the fusion of ORB with PFH and FPFH, which can improve feature representation and registration, representing a novel approach for RGB-D image matching.
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Zhu, Danyao, Luhe Wan, and Wei Gao. "Fusion Method Evaluation and Classification Suitability Study of Wetland Satellite Imagery." Earth Sciences Research Journal 23, no. 4 (October 1, 2019): 339–46. http://dx.doi.org/10.15446/esrj.v23n4.84350.

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Based on HJ-1A HSI data and Landsat-8 OLI data, RS image fusion experiments were carried out using three fusion methods: principal component (PC) transform, Gram Schimdt (GS) transform and nearest neighbor diffusion (NND) algorithm. Four evaluation indexes, namely mean, standard deviation, information entropy and average gradient, were selected to evaluate the fusion results from the aspects of image brightness, clarity and information content. Wetland vegetation was classified by spectral angle mapping (SAM) to find a suitable fusion method for wetland vegetation information extraction. The results show that PC fusion image contains the largest amount of information, GS fusion image has certain advantages in brightness and clarity maintenance, and NND fusion method can retain the spectral characteristics of the image to the maximum extent; Among the three fusion methods, PC transform is the most suitable for wetland information extraction. It can retain more spectral information while improving spatial resolution, with classification accuracy of 89.24% and Kappa coefficient of 0.86.
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Dissertations / Theses on the topic "Information extraction and fusion"

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Ahmad, Muhammad Imran. "Feature extraction and information fusion in face and palmprint multimodal biometrics." Thesis, University of Newcastle upon Tyne, 2013. http://hdl.handle.net/10443/2128.

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Multimodal biometric systems that integrate the biometric traits from several modalities are able to overcome the limitations of single modal biometrics. Fusing the information at an earlier level by consolidating the features given by different traits can give a better result due to the richness of information at this stage. In this thesis, three novel methods are derived and implemented on face and palmprint modalities, taking advantage of the multimodal biometric fusion at feature level. The benefits of the proposed method are the enhanced capabilities in discriminating information in the fused features and capturing all of the information required to improve the classification performance. Multimodal biometric proposed here consists of several stages such as feature extraction, fusion, recognition and classification. Feature extraction gathers all important information from the raw images. A new local feature extraction method has been designed to extract information from the face and palmprint images in the form of sub block windows. Multiresolution analysis using Gabor transform and DCT is computed for each sub block window to produce compact local features for the face and palmprint images. Multiresolution Gabor analysis captures important information in the texture of the images while DCT represents the information in different frequency components. Important features with high discrimination power are then preserved by selecting several low frequency coefficients in order to estimate the model parameters. The local features extracted are fused in a new matrix interleaved method. The new fused feature vector is higher in dimensionality compared to the original feature vectors from both modalities, thus it carries high discriminating power and contains rich statistical information. The fused feature vector also has larger data points in the feature space which is advantageous for the training process using statistical methods. The underlying statistical information in the fused feature vectors is captured using GMM where several numbers of modal parameters are estimated from the distribution of fused feature vector. Maximum likelihood score is used to measure a degree of certainty to perform recognition while maximum likelihood score normalization is used for classification process. The use of likelihood score normalization is found to be able to suppress an imposter likelihood score when the background model parameters are estimated from a pool of users which include statistical information of an imposter. The present method achieved the highest recognition accuracy 97% and 99.7% when tested using FERET-PolyU dataset and ORL-PolyU dataset respectively.
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Jin, Xiaoying. "Automatic extraction of man-made objects from high-resolution satellite imagery by information fusion." Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/5816.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2005.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (November 15, 2006) Vita. Includes bibliographical references.
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Arif-Uz-Zaman, Kazi. "Failure and maintenance information extraction methodology using multiple databases from industry: A new data fusion approach." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/116354/1/Kazi_Arif-Uz-Zaman_Thesis.pdf.

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This study develops a new method to identify a vital input, i.e. failure times of an asset, to reliability models from multiple but commonly-available industrial maintenance databases. A text mining approach is employed to extract useful features from unstructured free texts of different maintenance work records. The proposed method is further developed using Active Learning algorithms to improve the robustness of the results. The outcomes of this study can be used to develop advanced and applicable reliability models from historical maintenance databases, which were not effectively utilised before. Two industry case studies were conducted to justify the method.
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Thuillier, Etienne. "Extraction of mobility information through heterogeneous data fusion : a multi-source, multi-scale, and multi-modal problem." Thesis, Bourgogne Franche-Comté, 2017. http://www.theses.fr/2017UBFCA019.

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Aujourd'hui c'est un fait, nous vivons dans un monde où les enjeux écologiques, économiques et sociétaux sont de plus en plus pressants. Au croisement des différentes lignes directrices envisagées pour répondre à ces problèmes, une vision plus précise de la mobilité humaine est un axe central et majeur, qui a des répercussions sur tous les domaines associés tels que le transport, les sciences sociales, l'urbanisme, les politiques d'aménagement, l'écologie, etc. C'est par ailleurs dans un contexte de contraintes budgétaires fortes que les principaux acteurs de la mobilité sur les territoires cherchent à rationaliser les services de transport, et les déplacements des individus. La mobilité humaine est donc un enjeu stratégique aussi bien pour les collectivités locales que pour les usagers, qu'il faut savoir observer, comprendre, et anticiper.Cette étude de la mobilité passe avant tout par une observation précise des déplacements des usagers sur les territoires. Aujourd'hui les acteurs de la mobilité se tournent principalement vers l'utilisation massive des données utilisateurs. L'utilisation simultanée de données multi-sources, multi-modales, et multi-échelles permet d'entrevoir de nombreuses possibilités, mais cette dernière présente des défis technologiques et scientifiques majeurs. Les modèles de mobilité présentés dans la littérature sont ainsi trop souvent axés sur des zones d'expérimentation limitées, en utilisant des données calibrées, etc. et leur application dans des contextes réels, et à plus large échelle est donc discutable. Nous identifions ainsi deux problématiques majeures qui permettent de répondre à ce besoin d'une meilleure connaissance de la mobilité humaine, mais également à une meilleure application de cette connaissance. La première problématique concerne l'extraction d'informations de mobilité à partir de la fusion de données hétérogènes. La seconde problématique concerne la pertinence de cette fusion dans un contexte réel, et à plus large échelle. Nous apportons différents éléments de réponses à ces problématiques dans cette thèse. Tout d'abord en présentant deux modèles de fusion de données, qui permettent une extraction d'informations pertinentes. Puis, en analysant l'application de ces deux modèles au sein du projet ANR Norm-Atis.Dans cette thèse, nous suivons finalement le développement de toute une chaine de processus. En commençant par une étude de la mobilité humaine, puis des modèles de mobilité, nous présentons deux modèles de fusion de données, et nous analysons leur pertinence dans un cas concret. Le premier modèle que nous proposons permet d'extraire 12 comportements types de mobilité. Il est basé sur un apprentissage non-supervisé de données issues de la téléphonie mobile. Nous validons nos résultats en utilisant des données officielles de l'INSEE, et nous déduisons de nos résultats, des comportements dynamiques qui ne peuvent pas être observés par les données de mobilité traditionnelles. Ce qui est une forte valeur-ajoutée de notre modèle. Le second modèle que nous proposons permet une désagrégation des flux de mobilité en six motifs de mobilité. Il se base sur un apprentissage supervisé des données issues d'enquêtes de déplacements ainsi que des données statiques de description du sursol. Ce modèle est appliqué par la suite aux données agrégés au sein du projet Norm-Atis. Les temps de calculs sont suffisamment performants pour permettre une application de ce modèle dans un contexte temps-réel
Today it is a fact that we live in a world where ecological, economic and societal issues are increasingly pressing. At the crossroads of the various guidelines envisaged to address these problems, a more accurate vision of human mobility is a central and major axis, which has repercussions on all related fields such as transport, social sciences, urban planning, management policies, ecology, etc. It is also in the context of strong budgetary constraints that the main actors of mobility on the territories seek to rationalize the transport services and the movements of individuals. Human mobility is therefore a strategic challenge both for local communities and for users, which must be observed, understood and anticipated.This study of mobility is based above all on a precise observation of the movements of users on the territories. Nowadays mobility operators are mainly focusing on the massive use of user data. The simultaneous use of multi-source, multi-modal, and multi-scale data opens many possibilities, but the latter presents major technological and scientific challenges. The mobility models presented in the literature are too often focused on limited experimental areas, using calibrated data, etc., and their application in real contexts and on a larger scale is therefore questionable. We thus identify two major issues that enable us to meet this need for a better knowledge of human mobility, but also to a better application of this knowledge. The first issue concerns the extraction of mobility information from heterogeneous data fusion. The second problem concerns the relevance of this fusion in a real context, and on a larger scale. These issues are addressed in this dissertation: the first, through two data fusion models that allow the extraction of mobility information, the second through the application of these fusion models within the ANR Norm-Atis project.In this thesis, we finally follow the development of a whole chain of processes. Starting with a study of human mobility, and then mobility models, we present two data fusion models, and we analyze their relevance in a concrete case. The first model we propose allows to extract 12 types of mobility behaviors. It is based on an unsupervised learning of mobile phone data. We validate our results using official data from the INSEE, and we infer from our results, dynamic behaviors that can not be observed through traditional mobility data. This is a strong added-value of our model. The second model operates a mobility flows decompositoin into six mobility purposes. It is based on a supervised learning of mobility surveys data and static data from the land use. This model is then applied to the aggregated data within the Norm-Atis project. The computing times are sufficiently powerful to allow an application of this model in a real-time context
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Foucard, Rémi. "Fusion multi-niveaux par boosting pour le tagging automatique." Thesis, Paris, ENST, 2013. http://www.theses.fr/2013ENST0093/document.

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Les tags constituent un outil très utile pour indexer des documents multimédias. Cette thèse de doctorat s’intéresse au tagging automatique, c’est à dire l’association automatique par un algorithme d’un ensemble de tags à chaque morceau. Nous utilisons des techniques de boosting pour réaliser un apprentissage prenant mieux en compte la richesse de l’information exprimée par la musique. Un algorithme de boosting est proposé, afin d’utiliser conjointement des descriptions de morceaux associées à des extraits de différentes durées. Nous utilisons cet algorithme pour fusionner de nouvelles descriptions, appartenant à différents niveaux d’abstraction. Enfin, un nouveau cadre d’apprentissage est proposé pour le tagging automatique, qui prend mieux en compte les subtilités des associations entre les tags et les morceaux
Tags constitute a very useful tool for multimedia document indexing. This PhD thesis deals with automatic tagging, which consists in associating a set of tags to each song automatically, using an algorithm. We use boosting techniques to design a learning which better considers the complexity of the information expressed by music. A boosting algorithm is proposed, which can jointly use song descriptions associated to excerpts of different durations. This algorithm is used to fuse new descriptions, which belong to different abstraction levels. Finally, a new learning framework is proposed for automatic tagging, which better leverages the subtlety ofthe information expressed by music
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Gulen, Elvan. "Fusing Semantic Information Extracted From Visual, Auditory And Textual Data Of Videos." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614582/index.pdf.

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In recent years, due to the increasing usage of videos, manual information extraction is becoming insufficient to users. Therefore, extracting semantic information automatically turns out to be a serious requirement. Today, there exists some systems that extract semantic information automatically by using visual, auditory and textual data separately but the number of studies that uses more than one data source is very limited. As some studies on this topic have already shown, using multimodal video data for automatic information extraction ensures getting better results by guaranteeing increase in the accuracy of semantic information that is retrieved from visual, auditory and textual sources. In this thesis, a complete system which fuses the semantic information that is obtained from visual, auditory and textual video data is introduced. The fusion system carries out the following procedures
analyzing and uniting the semantic information that is extracted from multimodal data by utilizing concept interactions and consequently generating a semantic dataset which is ready to be stored in a database. Besides, experiments are conducted to compare results obtained from the proposed multimodal fusion operation with results obtained as an outcome of semantic information extraction from just one modality and other fusion methods. The results indicate that fusing all available information along with concept relations yields better results than any unimodal approaches and other traditional fusion methods in overall.
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Muhammad, Hanif Shehzad. "Feature selection and classifier combination: Application to the extraction of textual information in scene images." Paris 6, 2009. http://www.theses.fr/2009PA066521.

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Dans cette thèse, nous avons traité le problème de la détection et de la localisation dans les images de scène. Notre système est composé de deux parties : le Détecteur de texte et le Localiseur de texte. Le détecteur de texte (une cascade de classifieurs boostés) emploie la méthode de dopage qui sélectionne et combine des descripteurs et des classifieurs faibles pertinents. Plus précisément, nous avons proposé une version régularisée de l’algorithme AdaBoost qui intègre la complexité (liée à la charge de calcul) des descripteurs et des classifieurs faibles dans la phase de sélection. Nous avons proposé des descripteurs hétérogènes pour coder l’information textuelle dans les images. Nos règles de classification appartiennent des différentes classes de classifieurs : discriminant, linéaire et non-linéaire, paramétrique et non-paramétrique. Le détecteur génère des régions candidates de texte qui servent d’entrées au localiseur de texte dont l’objectif est de trouver des rectangles englobants, autour des mots ou des lignes de texte dans l’image. Les résultats sur deux bases d’images difficiles montrent l’efficacité de notre approche.
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Skibinski, Sebastian [Verfasser], Heinrich [Akademischer Betreuer] Müller, and Uwe [Gutachter] Schwiegelshohn. "Extraction, localization, and fusion of collective vehicle data / Sebastian Skibinski ; Gutachter: Uwe Schwiegelshohn ; Betreuer: Heinrich Müller." Dortmund : Universitätsbibliothek Dortmund, 2019. http://d-nb.info/1191990192/34.

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Skibinski, Sebastian [Verfasser], Heinrich Akademischer Betreuer] Müller, and Uwe [Gutachter] [Schwiegelshohn. "Extraction, localization, and fusion of collective vehicle data / Sebastian Skibinski ; Gutachter: Uwe Schwiegelshohn ; Betreuer: Heinrich Müller." Dortmund : Universitätsbibliothek Dortmund, 2019. http://d-nb.info/1191990192/34.

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Apatean, Anca Ioana. "Contributions à la fusion des informations : application à la reconnaissance des obstacles dans les images visible et infrarouge." Phd thesis, INSA de Rouen, 2010. http://tel.archives-ouvertes.fr/tel-00621202.

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Afin de poursuivre et d'améliorer la tâche de détection qui est en cours à l'INSA, nous nous sommes concentrés sur la fusion des informations visibles et infrarouges du point de vue de reconnaissance des obstacles, ainsi distinguer entre les véhicules, les piétons, les cyclistes et les obstacles de fond. Les systèmes bimodaux ont été proposées pour fusionner l'information à différents niveaux: des caractéristiques, des noyaux SVM, ou de scores SVM. Ils ont été pondérés selon l'importance relative des capteurs modalité pour assurer l'adaptation (fixe ou dynamique) du système aux conditions environnementales. Pour évaluer la pertinence des caractéristiques, différentes méthodes de sélection ont été testés par un PPV, qui fut plus tard remplacée par un SVM. Une opération de recherche de modèle, réalisée par 10 fois validation croisée, fournit le noyau optimisé pour SVM. Les résultats ont prouvé que tous les systèmes bimodaux VIS-IR sont meilleurs que leurs correspondants monomodaux.
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Books on the topic "Information extraction and fusion"

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Martin, Golz, Kuh Anthony, Obradovic Dragan, Tanaka Toshihisa, and SpringerLink (Online service), eds. Signal Processing Techniques for Knowledge Extraction and Information Fusion. Boston, MA: Springer Science+Business Media, LLC, 2008.

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Mandic, Danilo, Martin Golz, Anthony Kuh, Dragan Obradovic, and Toshihisa Tanaka, eds. Signal Processing Techniques for Knowledge Extraction and Information Fusion. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74367-7.

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Pazienza, Maria Teresa, ed. Information Extraction. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7.

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Li, Jinxing, Bob Zhang, and David Zhang. Information Fusion. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8976-5.

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Maybury, Mark T., ed. Multimedia Information Extraction. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118219546.

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Paolo, Coletti, ed. Information extraction in finance. Southampton: WIT Press, 2008.

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Popovich, Vasily V., Christophe Claramunt, Manfred Schrenk, and Kyrill V. Korolenko, eds. Information Fusion and Geographic Information Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00304-2.

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Popovich, Vasily V., Christophe Claramunt, Thomas Devogele, Manfred Schrenk, and Kyrill Korolenko, eds. Information Fusion and Geographic Information Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19766-6.

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Popovich, Vasily V., Manfred Schrenk, and Kyrill V. Korolenko, eds. Information Fusion and Geographic Information Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37629-3.

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M, Jordan John, ed. Human-centered information fusion. Boston: Artech House, 2010.

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Book chapters on the topic "Information extraction and fusion"

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Baki Ermis, Erhan, Venkatesh Saligrama, and Pierre-marc Jodoin. "Information Fusion and Anomaly Detection with Uncalibrated Cameras in Video Surveillance." In Multimedia Information Extraction, 201–16. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118219546.ch13.

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Zhao, Haitao, Zhihui Lai, Henry Leung, and Xianyi Zhang. "Latent Semantic Feature Extraction." In Information Fusion and Data Science, 13–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40794-0_2.

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Zhao, Haitao, Zhihui Lai, Henry Leung, and Xianyi Zhang. "Manifold-Learning-Based Feature Extraction." In Information Fusion and Data Science, 53–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40794-0_4.

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Xuejun, Wang, Zhao Linlin, and Wang Shuang. "A Fusion Scheme of Video Object Extraction." In Recent Advances in Computer Science and Information Engineering, 251–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25792-6_38.

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Mandic, Danilo, George Souretis, Wai Yie Leong, David Looney, Marc M. Van Hulle, and Toshihisa Tanaka. "Complex Empirical Mode Decomposition for Multichannel Information Fusion." In Signal Processing Techniques for Knowledge Extraction and Information Fusion, 243–60. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74367-7_13.

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Obradovic, Dragan, Henning Lenz, Markus Schupfner, and Kai Heesche. "Multimodal Fusion for Car Navigation Systems." In Signal Processing Techniques for Knowledge Extraction and Information Fusion, 141–58. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74367-7_8.

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Calhoun, Vince D., and Tülay Adali. "ICA for Fusion of Brain Imaging Data." In Signal Processing Techniques for Knowledge Extraction and Information Fusion, 221–40. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74367-7_12.

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Chen, Zuocong. "Method for Extraction and Fusion Based on KL Measure." In Communications in Computer and Information Science, 42–51. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0118-0_4.

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Jelfs, Beth, Phebe Vayanos, Soroush Javidi, Vanessa Su Lee Goh, and Danilo Mandic. "Collaborative Adaptive Filters for Online Knowledge Extraction and Information Fusion." In Signal Processing Techniques for Knowledge Extraction and Information Fusion, 3–21. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74367-7_1.

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Rutkowski, Tomasz M., Andrzej Cichocki, and Danilo Mandic. "Information Fusion for Perceptual Feedback: A Brain Activity Sonification Approach." In Signal Processing Techniques for Knowledge Extraction and Information Fusion, 261–73. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74367-7_14.

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Conference papers on the topic "Information extraction and fusion"

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Forsling, Robin, Zoran Sjanic, Fredrik Gustafsson, and Gustaf Hendeby. "Communication Efficient Decentralized Track Fusion Using Selective Information Extraction." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190575.

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Liu, Tsa Chun, Ratnasingham Tharmarasa, Simon Halle, Mihai Florea, Mike McDonald, and Thia Kirubarajan. "Anomaly Detection with Pattern of Life Extraction for GMTI Tracking." In 2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019. http://dx.doi.org/10.23919/fusion43075.2019.9011442.

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Xiaoxi Yan, Chongzhao Han, and Jing Liu. "State extraction of probability hypothesis density filter based on Dirichlet distribution." In 2010 13th International Conference on Information Fusion (FUSION 2010). IEEE, 2010. http://dx.doi.org/10.1109/icif.2010.5711955.

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Datta, S., B. R. Choudhuri, and A. Ganguli. "Text extraction system." In Proceedings of the Sixth International Conference of Information Fusion. IEEE, 2003. http://dx.doi.org/10.1109/icif.2003.177409.

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Lilienthal, Jannis, and Waltenegus Dargie. "Extraction of Motion Artifacts from the Measurements of a Wireless Electrocardiogram using Tensor Decomposition." In 2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019. http://dx.doi.org/10.23919/fusion43075.2019.9011290.

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SUWANDI, ADANG, and CATHERINE OLIVIA. "Knowledge Extraction for Infomation Fusion." In Fourth International Conference on Advances in Information Processing and Communication Technology - IPCT 2016. Institute of Research Engineers and Doctors, 2016. http://dx.doi.org/10.15224/978-1-63248-099-6-32.

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Ulmke, M., and W. Koch. "Road Map Extraction using GMTI Tracking." In 2006 9th International Conference on Information Fusion. IEEE, 2006. http://dx.doi.org/10.1109/icif.2006.301564.

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SUWANDI, ADANG, and ARWIN DATUMAYA. "Information Fusion as Knowledge Extraction in an Information Processing System." In Fourth International Conference on Advances in Computing, Electronics and Communication - ACEC 2016. Institute of Research Engineers and Doctors, 2016. http://dx.doi.org/10.15224/978-1-63248-113-9-05.

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Zuobin, Wu, Mao Kezhi, and Gee-Wah Ng. "Feature Regrouping for CCA - Based Feature Fusion and Extraction Through Normalized Cut." In 2018 21st International Conference on Information Fusion (FUSION 2018). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455397.

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Dou, Weibei, Qingmin Liao, Su Ruan, Daniel Bloyet, Jean-Mac Constans, and Yanping Chen. "Automatic brain tumor extraction using fuzzy information fusion." In Second International Conference on Image and Graphics, edited by Wei Sui. SPIE, 2002. http://dx.doi.org/10.1117/12.477203.

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Reports on the topic "Information extraction and fusion"

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Etzioni, Oren. Open Information Extraction. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada538482.

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Cohen, Eric, and Evelyne Tzoukermann. Phrase-based Multimedia Information Extraction. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada456800.

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White, Michael, Tanya Korelsky, Claire Cardie, Vincent Ng, David Pierce, and Kiri Wagstaff. Multidocument Summarization via Information Extraction. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada457772.

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Onyshkevych, Boyan. Template Design for Information Extraction. Fort Belvoir, VA: Defense Technical Information Center, July 1993. http://dx.doi.org/10.21236/ada635849.

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Srihari, Rohini, and Wei Li. Information Extraction Supported Question Answering. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada460042.

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Shinyama, Yusuke, and Satoshi Sekine. Paraphrase Acquisition for Information Extraction. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada460236.

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Irwin, N. H., S. M. DeLand, and S. V. Crowder. Extraction of information from unstructured text. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/148697.

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Nurre, Joseph H. Automate Information Extraction from Scan Data. Fort Belvoir, VA: Defense Technical Information Center, November 1998. http://dx.doi.org/10.21236/ada362095.

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Bray, O. H. Information integration for data fusion. Office of Scientific and Technical Information (OSTI), January 1997. http://dx.doi.org/10.2172/444047.

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Corkill, Daniel D. Collaborative Software for Information Fusion. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada437538.

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