Relevant bibliographies by topics / Fusion distances

Contents

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

Academic literature on the topic 'Fusion distances'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Fusion distances"

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Wang, Xiu Fang, Xue Ming Li, Qi Yang, and De Xin Qin. "Pipeline Network Leakage Diagnosis Based on Distance Data Fusion." Applied Mechanics and Materials 303-306 (February 2013): 918–21. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.918.

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The novel scheme of data fusion is a kind of evidence theories based on distance. We calculate the weight coefficients according to the structure of network and distances matrix between points and sensors. The greater the distance is, the smaller the coefficient will be. Experimental results show that, the proposed algorithm works well with high conflicts and fits characteristics of network.
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Reiner, C. S., T. F. Hany, J. Fornaro, G. K. von Schulthess, B. Marincek, D. Weishaupt, and O. F. Donati. "18F-FDG-PET and MRI in patients with malignancies of the liver and pancreas." Nuklearmedizin 49, no. 03 (2010): 106–14. http://dx.doi.org/10.3413/nukmed-0263.

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Summary Purpose: To evaluate the accuracy of retrospective rigid image registration and fusion between F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) and magnetic resonance imaging (MRI) of the upper abdomen. Patients, material, methods: Image fusion of PET and MRI was performed in 30 patients with suspected malignancy of the liver or pancreas. Using a commercially available image fusion tool capable of rigid manual point-based registration, PET-Images were retrospectively registered and fused by matching eight homologous points in the 3D spoiled gradient echo (GRE) MRI sequences acquired in portal venous phase and in the CT-component of PET/CT. Two separate observers (R1, R2) assessed accuracy of image registration by determining the distances in the x-, y- and z-axis as well as the absolute distance between anatomical landmarks which differed from the landmarks chosen for registration. Quality of fusion was graded using a three point grading scale (1 poorly fused; 2 satisfactory fused; 3 correctly fused) and compared to hybrid PET/CT fusion. Results: Mean time of registration per patient was less than 2 minutes. Objective registration assessment showed errors between 2.4–6.3 mm in x-axis: mean 3.6 mm (R1); 4.6 mm (R2), 2.3–9.3 mm in y-axis (mean 5.1 mm; 5.5 mm) and 3.3–12.0 mm in z-axis (mean 5.9 mm; 5.9 mm.) The mean error in absolute distance between points was 6.0–16.8 mm (mean 9.9 mm; 10.6 mm). In visual assessment, most fusions were graded to be satisfactory or correctly fused: R1, R2: grade 3, 11/30 (36.7%), 22/30 (73.3%); grade 2, 13/30 (43.3%), 8/30 (26.7%); grade 1, 6/30 (20%), 0/30 (0%). Fusions were mostly comparable to hybrid PET/ CT fusions. All of the fusions were defined as diagnostically relevant by both observers. Conclusion: Retrospective rigid image fusion of FDGPET and MRI of the upper abdomen using the CT-component of PET/CT for registration is feasible without adaptation in image acquisition protocols and shows sub-centimeter registration errors in most cases.
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Wang, Tao, Xiaoran Wang, and Mingyu Hong. "Gas Leak Location Detection Based on Data Fusion with Time Difference of Arrival and Energy Decay Using an Ultrasonic Sensor Array." Sensors 18, no. 9 (September 7, 2018): 2985. http://dx.doi.org/10.3390/s18092985.

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Ultrasonic gas leak location technology is based on the detection of ultrasonic waves generated by the ejection of pressured gas from leak holes in sealed containers or pipes. To obtain more accurate leak location information and determine the locations of leak holes in three-dimensional space, this paper proposes an ultrasonic leak location approach based on multi-algorithm data fusion. With the help of a planar ultrasonic sensor array, the eigenvectors of two individual algorithms, i.e., the arrival distance difference, as determined from the time difference of arrival (TDOA) location algorithm, and the ratio of arrival distances from the energy decay (ED) location algorithm, are extracted and fused to calculate the three-dimensional coordinates of leak holes. The fusion is based on an extended Kalman filter, in which the results of the individual algorithms are seen as observation values. The final system state matrix is composed of distances between the measured leak hole and the sensors. Our experiments show that, under the condition in which the pressure in the measured container is 100 kPa, and the leak hole–sensor distance is 800 mm, the maximum error of the calculated results based on the data fusion location algorithm is less than 20 mm, and the combined accuracy is better than those of the individual location algorithms.
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Kumar, G. Ajay, Jin Hee Lee, Jongrak Hwang, Jaehyeong Park, Sung Hoon Youn, and Soon Kwon. "LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles." Symmetry 12, no. 2 (February 24, 2020): 324. http://dx.doi.org/10.3390/sym12020324.

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The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation, and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the detection of objects at short and long distances. As both the sensors are capable of capturing the different attributes of the environment simultaneously, the integration of those attributes with an efficient fusion approach greatly benefits the reliable and consistent perception of the environment. This paper presents a method to estimate the distance (depth) between a self-driving car and other vehicles, objects, and signboards on its path using the accurate fusion approach. Based on the geometrical transformation and projection, low-level sensor fusion was performed between a camera and LiDAR using a 3D marker. Further, the fusion information is utilized to estimate the distance of objects detected by the RefineDet detector. Finally, the accuracy and performance of the sensor fusion and distance estimation approach were evaluated in terms of quantitative and qualitative analysis by considering real road and simulation environment scenarios. Thus the proposed low-level sensor fusion, based on the computational geometric transformation and projection for object distance estimation proves to be a promising solution for enabling reliable and consistent environment perception ability for autonomous vehicles.
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Ji, Linna, Fengbao Yang, and Xiaoming Guo. "Image Fusion Algorithm Selection Based on Fusion Validity Distribution Combination of Difference Features." Electronics 10, no. 15 (July 21, 2021): 1752. http://dx.doi.org/10.3390/electronics10151752.

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Aiming at addressing the problem whereby existing image fusion models cannot reflect the demand of diverse attributes (e.g., type or amplitude) of difference features on algorithms, leading to poor or invalid fusion effect, this paper puts forward the construction and combination of difference features fusion validity distribution based on intuition-possible sets to deal with the selection of algorithms with better fusion effect in dual mode infrared images. Firstly, the distances of the amplitudes of difference features between fused images and source images are calculated. The distances can be divided into three levels according to the fusion result of each algorithm, which are regarded as intuition-possible sets of fusion validity of difference features, and a novel construction method of fusion validity distribution based on intuition-possible sets is proposed. Secondly, in view of multiple amplitude intervals of each difference feature, this paper proposes a distribution combination method based on intuition-possible set ordering. Difference feature score results are aggregated by a fuzzy operator. Joint drop shadows of difference feature score results are obtained. Finally, the experimental results indicate that our proposed method can achieve optimal selection of algorithms that has relatively better effect on the fusion of difference features according to the varied feature amplitudes.
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Predebon, John. "Convergence Responses to Monocularly Viewed Objects: Implications for Distance Perception." Perception 23, no. 3 (March 1994): 303–19. http://dx.doi.org/10.1068/p230303.

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In four experiments the role of dark vergence and the implied distance from the familiar-size and suggested-size cues to distance on the convergence response was investigated. A nonius-alignment technique was used to measure the convergence response in total darkness (dark vergence) and the fusion-free convergence response to monocularly viewed objects presented at a distance of 75 cm under otherwise reduced stimulus conditions. Observers also estimated the size and distance of the objects. The results indicated a significant association between individuals' dark-vergence distances and the convergence distances to the objects. Furthermore, the convergence response was influenced by the implied distance from the familiar-size cue but not by the implied distance from size suggestions. Both the familiar-size and the suggested-size cues influenced reports of distance. The implications of these findings for distance perception are discussed with particular reference to the familiar-size cue to distance.
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Grothe, Tobias, Julia Nowak, Reinhard Jahn, and Peter Jomo Walla. "Selected tools to visualize membrane interactions." European Biophysics Journal 50, no. 2 (March 2021): 211–22. http://dx.doi.org/10.1007/s00249-021-01516-6.

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AbstractIn the past decade, we developed various fluorescence-based methods for monitoring membrane fusion, membrane docking, distances between membranes, and membrane curvature. These tools were mainly developed using liposomes as model systems, which allows for the dissection of specific interactions mediated by, for example, fusion proteins. Here, we provide an overview of these methods, including two-photon fluorescence cross-correlation spectroscopy and intramembrane Förster energy transfer, with asymmetric labelling of inner and outer membrane leaflets and the calibrated use of transmembrane energy transfer to determine membrane distances below 10 nm. We discuss their application range and their limitations using examples from our work on protein-mediated vesicle docking and fusion.
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Grove, Philip M., Alistair P. Mapp, and Hiroshi Ono. "The Bifixation Field as a Function of Viewing Distance." Journal of Ophthalmology 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/274803.

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Hering reported that the area over which he could bifixate a target was smaller at near convergence distances than far convergence distances and predicted that in extreme horizontal gaze positions, the temporally directed eye lags behind the nasally directed eye. We tested these predictions using a subjective index of eye position. Experiment 1 confirmed that the bifixation field was significantly smaller at near convergence distances. When bifixation broke down at the near distance, the nasally directed eye lagged behind the temporally directed eye for all observers. At the far distance, the nasally directed eye preceded the temporally directed eye for four of six observers. Experiment 2 also confirmed that the bifixation field was smaller at near convergence distances but the nasally directed eye always lagged behind the temporally directed eye at the limits of the bifixation field. We confirmed Hering’s first prediction that the bifixation field is smaller at near convergence distances than at far ones. However, the majority of our results indicate that the nasally directed eye lags behind the temporally directed eye at the limits of the bifixation field, contrary to Hering’s prediction. We conclude that the eyes drift toward their tonic state of vergence when fusion breaks.
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Shen, J., W. L. Han, J. Ge, L. B. Zhang, and H. Tan. "DIGITAL ELEVATION MODEL INTERPOLATION BY FUSION OF MORPHOLOGICAL RECONSTRUCTION AND DISTANCE TRANSFORMATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 881–85. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-881-2017.

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Interpolation methods have significant impacts on the accuracy of the digital elevation model (DEM) from contours which are one of frequently employed data sources. In this paper, an interpolation method is presented to build DEM from contour lines by fusion/integration of morphological reconstruction and distance transformation with obstacles. Particularly, morphological reconstruction is used to get the elevation values of the higher contour lines and the lower contour lines of any a spatial point between two contour lines, and distance transformation with obstacles is used to get the geodesic distances of the spatial point to the higher contour lines and the lower contour lines respectively. At last, linear interpolation along water flow line is used to get the elevation values of the pixels to be interpolated. The experiment demonstrates that feasibility of our proposed method.
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Kumar, N. B. Mahesh, and K. Premalatha. "Palmprint Authentication System Based on Local and Global Feature Fusion Using DOST." Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/918376.

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Palmprint is the region between wrist and fingers. In this paper, a palmprint personal identification system is proposed based on the local and global information fusion. The local and global information is critical for the image observation based on the results of the relationship between physical stimuli and perceptions. The local features of the enhanced palmprint are extracted using discrete orthonormal Stockwell transform. The global feature is obtained by reducing the scale of discrete orthonormal Stockwell transform to infinity. The local and global matching distances of the two palmprint images are fused to get the final matching distance of the proposed scheme. The results show that the fusion of local and global features outperforms the existing works on the available three datasets.
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Dissertations / Theses on the topic "Fusion distances"

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Devogele, Thomas. "Système d'information géographique temporelle maritime ; Des distances linéaires à l'analyse temps réel des trajectoires." Habilitation à diriger des recherches, Université de Bretagne occidentale - Brest, 2009. http://tel.archives-ouvertes.fr/tel-00441484.

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Les Systèmes d'Information Géographique (SIG) gèrent des informations complexes localisées dans un espace géographique. Durant la dernière décennie, ces systèmes ont vu l'émergence d'une part des SIG en trois dimensions et plus particulièrement des modèles numériques de terrain (MNT) et d'autre part de l'intégration de données temporelles. Les travaux de recherche en informatique présentés dans ce manuscrit s'attaquent à ces deux thématiques et sont appliqués au domaine maritime. En termes de MNT, ces recherches ont essentiellement porté sur l'intégration de MNT terrestre et maritime. Une méthode de fusion générant un MNT continu a été définie. Elle est basée sur l'appariement d'éléments caractéristiques linéaires (crêtes, talwegs, lignes de rupture de pente). Elle emploie des distances linéaires discrètes introduites par Fréchet. Ces distances fournissent des vecteurs d'appariement autorisant la fusion de MNT à l'aide d'une technique de déformation élastique. En ce qui concerne les SIG spatio-temporels, ces travaux concernent les déplacements d'objets mobiles et plus particulièrement les navires. Dans le cadre de la navigation maritime, les SIG ont une place prépondérante via les aides à la navigation, les systèmes de surveillance du trafic et les simulateurs de formation. Dans ce cadre, un nouveau modèle de représentation des déplacements, la vue relative, a été formalisé. Ce formalisme s'appuie sur la perception humaine de l'espace spatio-temporel et des objets mobiles. Cette représentation est complémentaire de la représentation absolue. Les déplacements relatifs vis-à-vis d'un objet référent sont ainsi plus facilement analysables. Les évolutions des distances et des vitesses relatives sont mieux perçues. Parallèlement, une simulation des activités de navires à l'aide de systèmes multi-agents a été conçue. Les déplacements simultanés d'un grand nombre de navires dans un environnement maritime sont ainsi simulés. Elle prend en compte les différentes activités concurrentes des objets mobiles et les contraintes géographiques. Qui plus est, elle intègre des raisonnements à base de patrons pour reproduire les raisonnements humains. Finalement, à partir des bases de données historiques des positions d'objets mobiles dans un environnement ouvert, des techniques d'extractions des trajectoires et de fouille de données spatio-temporelles ont été réalisées. Elles permettent pour chaque itinéraire et chaque type d'objets de définir des routes types spatio-temporelles et de détecter à la volée des comportements inhabituels. Ces travaux, bien qu'appliqués au domaine maritime, sont génériques. Les méthodes de fusion de MNT peuvent être appliquées à tous types de MNT ayant des zones de recouvrement. De même, les travaux de modélisation, d'analyses et de simulation sur les objets mobiles sont adaptables à tous les déplacements d'objets mobiles dans des espaces ouverts.
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Pereira, Sandra M. C. "Analysis of spatial point patterns using hierarchical clustering algorithms." University of Western Australia. School of Mathematics and Statistics, 2003. http://theses.library.uwa.edu.au/adt-WU2004.0056.

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[Formulae and special characters can only be approximated here. Please see the pdf version of the abstract for an accurate reproduction.] This thesis is a new proposal for analysing spatial point patterns in spatial statistics using the outputs of popular techniques of (classical, non-spatial, multivariate) cluster analysis. The outputs of a chosen hierarchical algorithm, named fusion distances, are applied to investigate important spatial characteristics of a given point pattern. The fusion distances may be regarded as a missing link between the fields of spatial statistics and multivariate cluster analysis. Up to now, these two fields have remained rather separate because of fundamental differences in approach. It is shown that fusion distances are very good at discriminating different types of spatial point patterns. A detailed study on the power of the Monte Carlo test under the null hypothesis of Complete Spatial Randomness (the benchmark of spatial statistics) against chosen alternative models is also conducted. For instance, the test (based on the fusion distance) is very powerful for some arbitrary values of the parameters of the alternative. A new general approach is developed for analysing a given point pattern using several graphical techniques for exploratory data analysis and inference. The new strategy is applied to univariate and multivariate point patterns. A new extension of a popular strategy in spatial statistics, named the analysis of the local configuration, is also developed. This new extension uses the fusion distances, and analyses a localised neighbourhood of a given point of the point pattern. New spatial summary function and statistics, named the fusion distance function H(t), area statistic A, statistic S, and spatial Rg index, are introduced, and proven to be useful tools for identifying relevant features of spatial point patterns. In conclusion, the new methodology using the outputs of hierarchical clustering algorithms can be considered as an essential complement to the existing approaches in spatial statistics literature.
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Guo, Bingchen. "Soft biometric fusion for subject recognition at a distance." Thesis, University of Southampton, 2018. https://eprints.soton.ac.uk/423611/.

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Biometric recognition is an advanced technology that employs physical features (such as fingerprint, iris and face capture) and behavioural features (such as gait, signature and voice) to identify people. Biometric features are reliable and valid ways to describe the unique properties of individuals, but there are often rigorous requirements on the position and characteristics of devices used for data acquisition. Since biometric features can be difficult to capture at a distance, soft biometric features, such as height, weight, skin colour and gender, have received much attention. Although the uniqueness of soft biometric features is not as intuitively obvious as traditional biometric features, numerous experiments have demonstrated that the desired recognition accuracy can be achieved by using different soft biometric features. This thesis will propose state-of-the-art multimodal biometric fusion techniques to improve recognition performance of soft biometrics. The first contribution of this thesis is to estimate fusion performance based on three types of soft biometrics - face, body and clothing. Feature level and score level fusion strategies will be employed to measure and analyse the influence of fusion on soft biometric recognition. The second key contribution of this research is that the analysis of the influence of distance on soft biometric traits and an exploration of the potency of recognition using fusion at varying distances have been performed. A new soft biometric database, containing images of the human face, body and clothing taken at three different distances, was created and used to obtain face, body and clothing attributes. First, this new database was constructed to explore the suitability of each modality at a distance: intuitively, the face is suitable for near field identification, and the body becomes optimal when the subject is further away. The new dataset is used to explore the potential of face, body and clothing for human recognition using fusion. In this section, some novel fusion techniques on different levels (feature, score and rank level) are proposed to improve soft biometric recognition performance. A Supervised Generalised Canonical Correlation (SG-CCA) methodology is proposed to fuse the soft biometric features. The proposed SG-CCA is numerically validated to be the best fusion method compared with other multi-modal fusion methods. An SVM-weighted Likelihood Ratio Test (SVM-LRT) method is proposed for score level fusion. The experimental results demonstrate that SVM-LRT-based fusion significantly outperforms the single-mode recognition. A novel joint density distribution-based rank-score fusion is also proposed to combine rank and score information. Analysis using the new soft biometric database demonstrates that recognition performance is significantly improved by using the new methods over single modalities at different distances.
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Luusua, Emil. "Vehicle Detection, at a Distance : Done Efficiently via Fusion of Short- and Long-Range Images." Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167073.

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Object detection is a classical computer vision task, encountered in many practical applications such as robotics and autonomous driving. The latter involves serious consequences of failure and a multitude of challenging demands, including high computational efficiency and detection accuracy. Distant objects are notably difficult to detect accurately due to their small scale in the image, consisting of only a few pixels. This is especially problematic in autonomous driving, as objects should be detected at the earliest possible stage to facilitate handling of hazardous situations. Previous work has addressed small objects via use of feature pyramids and super-resolution techniques, but the efficiency of such methods is limited as computational cost increases with image resolution. Therefore, a trade-off must be made between accuracy and cost. Opportunely though, a common characteristic of driving scenarios is the predominance of distant objects in the centre of the image. Thus, the full-frame image can be downsampled to reduce computational cost, and a crop can be extracted from the image centre to preserve resolution for distant vehicles. In this way, short- and long-range images are generated. This thesis investigates the fusion of such images in a convolutional neural network, particularly the fusion level, fusion operation, and spatial alignment. A novel framework — DetSLR — is proposed for the task and examined via the aforementioned aspects. Through adoption of the framework for the well-established SSD detector and MobileNetV2 feature extractor, it is shown that the framework significantly improves upon the original detector without incurring additional cost. The fusion level is shown to have great impact on the performance of the framework, favouring high-level fusion, while only insignificant differences exist between investigated fusion operations. Finally, spatial alignment of features is demonstrated to be a crucial component of the framework.
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Gunay, Melih. "Representation Of Covariance Matrices In Track Fusion Problems." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12609026/index.pdf.

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Covariance Matrix in target tracking algorithms has a critical role at multi- sensor track fusion systems. This matrix reveals the uncertainty of state es- timates that are obtained from diferent sensors. So, many subproblems of track fusion usually utilize this matrix to get more accurate results. That is why this matrix should be interchanged between the nodes of the multi-sensor tracking system. This thesis mainly deals with analysis of approximations of the covariance matrix that can best represent this matrix in order to efectively transmit this matrix to the demanding site. Kullback-Leibler (KL) Distance is exploited to derive some of the representations for Gaussian case. Also com- parison of these representations is another objective of this work and this is based on the fusion performance of the representations and the performance is measured for a system of a 2-radar track fusion system.
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Sadek, Muheeb. "Synthesis and Investigation of Nucleobase Functionalized β-Peptide as SNAREs Model System for Membranefusion." Doctoral thesis, Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2015. http://hdl.handle.net/11858/00-1735-0000-0022-605D-6.

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Serce, Fatma Cemile. "A Multi-agent Adaptive Learning System For Distance Education." Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609220/index.pdf.

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The adaptiveness provides uniquely identifying and monitoring the learner&rsquo
s learning activities according to his/her respective profile. The adaptive intelligent learning management systems (AILMS) help a wide range of students to achieve their learning goals effectively by delivering knowledge in an adaptive or individualized style through online learning settings. This study presents a multi-agent system, called MODA, developed to provide adaptiveness in learning management systems (LMS). A conceptual framework for adaptive learning systems is proposed for this purpose. The framework is based on the idea that adaptiveness is the best matching between the learner profile and the course content profile. The learning styles of learners and the content type of learning material are used to match the learner to the most suitable content. The thesis covers the pedagogical framework applied in MODA, the technical and multi-agent architectures of MODA, the TCP-IP based protocol providing communication between MODA and LMS, and a sample application of the system to an open source learning management system, OLAT. The study also discusses the possibilities of future interests.
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Yin, Bo Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Language identification with language and feature dependency." Awarded By:University of New South Wales. Electrical Engineering & Telecommunications, 2009. http://handle.unsw.edu.au/1959.4/44045.

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The purpose of Language Identification (LID) is to identify a specific language from a spoken utterance, automatically. Language-specific characteristics are always associated with different languages. Most existing LID approaches utilise a statistical modelling process with common acoustic/phonotactic features to model specific languages while avoiding any language-specific knowledge. Great successes have been achieved in this area over past decades. However, there is still a huge gap between these languageindependent methods and the actual language-specific patterns. It is extremely useful to address these specific acoustic or semantic construction patterns, without spending huge labour on annotation which requires language-specific knowledge. Inspired by this goal, this research focuses on the language-feature dependency. Several practical methods have been proposed. Various features and modelling techniques have been studied in this research. Some of them carry out additional language-specific information without manual labelling, such as a novel duration modelling method based on articulatory features, and a novel Frequency-Modulation (FM) based feature. The performance of each individual feature is studied for each of the language-pair combinations. The similarity between languages and the contribution in identifying a language by using a particular feature are defined for the first time, in a quantitative style. These distance measures and languagedependent contributions become the foundations of the later-presented frameworks ?? language-dependent weighting and hierarchical language identification. The latter particularly provides remarkable flexibility and enhancement when identifying a relatively large number of languages and accents, due to the fact that the most discriminative feature or feature-combination is used when separating each of the languages. The proposed systems are evaluated in various corpora and task contexts including NIST language recognition evaluation tasks. The performances have been improved in various degrees. The key techniques developed for this work have also been applied to solve a different problem other than LID ?? speech-based cognitive load monitoring.
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Lassoued, Khaoula. "Localisation de robots mobiles en coopération mutuelle par observation d'état distribuée." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2289/document.

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On étudie dans cette thèse des méthodes de localisation coopérative de robots mobiles sans utilisation de mesures extéroceptives relatives, comme des angles ou des distances entre robots. Les systèmes de localisation considérés sont basés sur des mesures de radionavigation sur des balises fixes ou des satellites. Pour ces systèmes, on observe en général un écart entre la position observée et la position réelle. Cet écart systématique (appelé biais) peut être dû à une mauvaise position de la balise ou à une différence entre la propagation réelles des ondes électromagnétiques par rapport aux conditions standard utilisées pour établir les modèles d’observation. L’influence de ce biais sur la localisation des robots est non négligeable. La coopération et l’échange de données entre les robots (estimations des biais, estimations des positions et données proprioceptives) est une approche qui permet de corriger ces erreurs systématiques. La localisation coopérative par échange des estimations est sujette aux problèmes de consanguinité des données qui peuvent engendrer des résultats erronés, en particulier trop confiants. Lorsque les estimations sont utilisées pour la navigation autonome à l’approche, on doit éviter tout risque de collision qui peut mettre en jeu la sécurité des robots et des personnes aux alentours. On doit donc avoir recours à un mécanisme d’intégrité vérifiant que l’erreur commise reste inférieure à une erreur maximale tolérable pour la mission. Dans un tel contexte, il est nécessaire de caractériser des domaines de confiance fiables contenant les positions des robots mobiles avec une forte probabilité. L’utilisation des méthodes ensemblistes à erreurs bornées est considérée alors comme une solution efficace. En effet, ce type d’approche résout naturellement le problème de consanguinité des données et fournit des domaines de confiance fiables. De surcroît, l’utilisation de modèles non-linéaires ne pose aucun problème de linéarisation. Après avoir modélisé un système coopératif de nr robots avec des mesures biaisées sur des balises, une étude d’observabilité est conduite. Deux cas sont considérés selon la nature des mesures brutes des observations. En outre, des conditions d’observabilité sont démontrées. Un algorithme ensembliste de localisation coopérative est ensuite présenté. Les méthodes considérées sont basées sur la propagation de contraintes sur des intervalles et l’inversion ensembliste. La coopération est effectuée grâce au partage des positions estimées, des biais estimés et des mesures proprioceptives.L’échange des estimations de biais permet de réduire les incertitudes sur les positions des robots. Dans un cadre d’étude simple, la faisabilité de l’algorithme est évaluée grâce à des simulations de mesures de distances sur balises en utilisant plusieurs robots. La coopération est comparée aux méthodes non coopératives. L’algorithme coopératif ensembliste est ensuite testé sur des données réelles en utilisant deux véhicules. Les performances de la méthode ensembliste coopérative sont enfin comparées avec deux méthodes Bayésiennes séquentielles, notamment une avec fusion par intersection de covariance. La comparaison est conduite en termes d’exactitude et d’incertitude
In this work, we study some cooperative localization issues for mobile robotic systems that interact with each other without using relative measurements (e.g. bearing and relative distances). The considered localization technologies are based on beacons or satellites that provide radio-navigation measurements. Such systems often lead to offsets between real and observed positions. These systematic offsets (i.e, biases) are often due to inaccurate beacon positions, or differences between the real electromagnetic waves propagation and the observation models. The impact of these biases on robots localization should not be neglected. Cooperation and data exchange (estimates of biases, estimates of positions and proprioceptive measurements) reduce significantly systematic errors. However, cooperative localization based on sharing estimates is subject to data incest problems (i.e, reuse of identical information in the fusion process) that often lead to over-convergence problems. When position information is used in a safety-critical context (e.g. close navigation of autonomous robots), one should check the consistency of the localization estimates. In this context, we aim at characterizing reliable confidence domains that contain robots positions with high reliability. Hence, set-membership methods are considered as efficient solutions. This kind of approach enables merging adequately the information even when it is reused several time. It also provides reliable domains. Moreover, the use of non-linear models does not require any linearization. The modeling of a cooperative system of nr robots with biased beacons measurements is firstly presented. Then, we perform an observability study. Two cases regarding the localization technology are considered. Observability conditions are identified and demonstrated. We then propose a set-membership method for cooperativelocalization. Cooperation is performed by sharing estimated positions, estimated biases and proprioceptive measurements. Sharing biases estimates allows to reduce the estimation error and the uncertainty of the robots positions. The algorithm feasibility is validated through simulation when the observations are beacons distance measurements with several robots. The cooperation provides better performance compared to a non-cooperative method. Afterwards, the cooperative algorithm based on set-membership method is tested using real data with two experimental vehicles. Finally, we compare the interval method performance with a sequential Bayesian approach based on covariance intersection. Experimental results indicate that the interval approach provides more accurate positions of the vehicles with smaller confidence domains that remain reliable. Indeed, the comparison is performed in terms of accuracy and uncertainty
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Lian, Chunfeng. "Information fusion and decision-making using belief functions : application to therapeutic monitoring of cancer." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2333/document.

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La radiothérapie est une des méthodes principales utilisée dans le traitement thérapeutique des tumeurs malignes. Pour améliorer son efficacité, deux problèmes essentiels doivent être soigneusement traités : la prédication fiable des résultats thérapeutiques et la segmentation précise des volumes tumoraux. La tomographie d’émission de positrons au traceur Fluoro- 18-déoxy-glucose (FDG-TEP) peut fournir de manière non invasive des informations significatives sur les activités fonctionnelles des cellules tumorales. Les objectifs de cette thèse sont de proposer: 1) des systèmes fiables pour prédire les résultats du traitement contre le cancer en utilisant principalement des caractéristiques extraites des images FDG-TEP; 2) des algorithmes automatiques pour la segmentation de tumeurs de manière précise en TEP et TEP-TDM. La théorie des fonctions de croyance est choisie dans notre étude pour modéliser et raisonner des connaissances incertaines et imprécises pour des images TEP qui sont bruitées et floues. Dans le cadre des fonctions de croyance, nous proposons une méthode de sélection de caractéristiques de manière parcimonieuse et une méthode d’apprentissage de métriques permettant de rendre les classes bien séparées dans l’espace caractéristique afin d’améliorer la précision de classification du classificateur EK-NN. Basées sur ces deux études théoriques, un système robuste de prédiction est proposé, dans lequel le problème d’apprentissage pour des données de petite taille et déséquilibrées est traité de manière efficace. Pour segmenter automatiquement les tumeurs en TEP, une méthode 3-D non supervisée basée sur le regroupement évidentiel (evidential clustering) et l’information spatiale est proposée. Cette méthode de segmentation mono-modalité est ensuite étendue à la co-segmentation dans des images TEP-TDM, en considérant que ces deux modalités distinctes contiennent des informations complémentaires pour améliorer la précision. Toutes les méthodes proposées ont été testées sur des données cliniques, montrant leurs meilleures performances par rapport aux méthodes de l’état de l’art
Radiation therapy is one of the most principal options used in the treatment of malignant tumors. To enhance its effectiveness, two critical issues should be carefully dealt with, i.e., reliably predicting therapy outcomes to adapt undergoing treatment planning for individual patients, and accurately segmenting tumor volumes to maximize radiation delivery in tumor tissues while minimize side effects in adjacent organs at risk. Positron emission tomography with radioactive tracer fluorine-18 fluorodeoxyglucose (FDG-PET) can noninvasively provide significant information of the functional activities of tumor cells. In this thesis, the goal of our study consists of two parts: 1) to propose reliable therapy outcome prediction system using primarily features extracted from FDG-PET images; 2) to propose automatic and accurate algorithms for tumor segmentation in PET and PET-CT images. The theory of belief functions is adopted in our study to model and reason with uncertain and imprecise knowledge quantified from noisy and blurring PET images. In the framework of belief functions, a sparse feature selection method and a low-rank metric learning method are proposed to improve the classification accuracy of the evidential K-nearest neighbor classifier learnt by high-dimensional data that contain unreliable features. Based on the above two theoretical studies, a robust prediction system is then proposed, in which the small-sized and imbalanced nature of clinical data is effectively tackled. To automatically delineate tumors in PET images, an unsupervised 3-D segmentation based on evidential clustering using the theory of belief functions and spatial information is proposed. This mono-modality segmentation method is then extended to co-segment tumor in PET-CT images, considering that these two distinct modalities contain complementary information to further improve the accuracy. All proposed methods have been performed on clinical data, giving better results comparing to the state of the art ones
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Books on the topic "Fusion distances"

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Wells, Christi Jay. Between Beats. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197559277.001.0001.

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Between Beats: The Jazz Tradition and Black Vernacular Dance explores the complex intersections between jazz music and popular dance over the last hundred-plus years. It aims to show how popular entertainment and cultures of social dancing were crucial to jazz music’s formation and development, but it also investigates the processes through which jazz music came to earn a reputation as a “legitimate” art form better suited for still, seated listening. Through the concept of “choreographies of listening,” the book explores amateur and professional jazz dancers’ relationships with jazz music and musicians as jazz’s soundscapes and choreoscapes were forged through close contact and mutual creative exchange. The book’s later chapters also critically unpack the aesthetic and political negotiations through which jazz music supposedly distanced itself from dancing bodies. As musicians and critics sought to secure institutional space for jazz within America’s body-averse academic and high-art cultures, an intentional severance from the dancing body proved crucial to jazz’s re-positioning as a form of autonomous, elite art. Fusing little-discussed material from diverse historical and contemporary sources with the author’s own years of experience as a social jazz dancer, this book seeks to advance participatory dance and embodied practice as central topics of analysis in jazz studies. As it explores the fascinating history of jazz as popular dance music, this book also exposes how American anxieties about bodies and a broad cultural privileging of the cerebral over the corporeal have shaped efforts to “elevate” expressive forms such as jazz to elite status.
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Book chapters on the topic "Fusion distances"

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Aguiló, Isabel, Tomasa Calvo Sánchez, Pilar Fuster-Parra, Javier Martín, Jaume Suñer, and Oscar Valero. "New Advances in the Aggregation of Asymmetric Distances. The Bounded Case." In Fuzzy Logic and Information Fusion, 101–21. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30421-2_8.

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Sheng, Y., X. Yang, P. Valin, and L. Sévigny. "Robust Multisensor Image Registration with Partial Distance Merits." In Multisensor Fusion, 593–609. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_28.

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Moneta, Carlo, Gianni Vernazza, and Rodolfo Zunino. "A Vectorial Definition of Conceptual Distance for Prototype Acquisition and Refinement." In Data Fusion Applications, 123–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-84990-9_13.

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Pang, Shumao, Zhentai Lu, Wei Yang, Yao Wu, Zixiao Lu, Liming Zhong, and Qianjin Feng. "Hippocampus Segmentation Through Distance Field Fusion." In Patch-Based Techniques in Medical Imaging, 104–11. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28194-0_13.

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Bhanu, Bir, and Ju Han. "Fusion of Color/Infrared Video for Human Detection." In Human Recognition at a Distance in Video, 95–114. London: Springer London, 2010. http://dx.doi.org/10.1007/978-0-85729-124-0_6.

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Bhanu, Bir, and Ju Han. "Feature Level Fusion of Face and Gait at a Distance." In Human Recognition at a Distance in Video, 209–32. London: Springer London, 2010. http://dx.doi.org/10.1007/978-0-85729-124-0_11.

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Bhanu, Bir, and Ju Han. "Match Score Level Fusion of Face and Gait at a Distance." In Human Recognition at a Distance in Video, 185–207. London: Springer London, 2010. http://dx.doi.org/10.1007/978-0-85729-124-0_10.

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Duarte, Marco, and Yu-Hen Hu. "Distance Based Decision Fusion in a Distributed Wireless Sensor Network." In Information Processing in Sensor Networks, 392–404. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36978-3_26.

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Damer, Naser, Wael Alkhatib, Andreas Braun, and Arjan Kuijper. "Neighbor Distance Ratios and Dynamic Weighting in Multi-biometric Fusion." In Pattern Recognition and Image Analysis, 491–500. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58838-4_54.

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Dong, Wei, Qiuyuan Wang, Xin Wang, and Hongbin Zha. "PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction." In Computer Vision – ECCV 2018, 714–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01240-3_43.

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Conference papers on the topic "Fusion distances"

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Ding, Jiankun, Deqiang Han, Jean Dezert, and Yi Yang. "Comparative study on BBA determination using different distances of interval numbers." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009756.

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Malof, Jordan M., Kenneth D. Morton, Leslie M. Collins, and Peter A. Torrione. "Fusion of forward looking infrared and ground penetrating radar for improved stopping distances in landmine detection." In SPIE Defense + Security, edited by Steven S. Bishop and Jason C. Isaacs. SPIE, 2014. http://dx.doi.org/10.1117/12.2051444.

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Ehaimir, Marwa E., Islem Jarraya, Wael Ouarda, and Adel M. Alimi. "Human gait identity recognition system based on gait pal and pal entropy (GPPE) and distances features fusion." In 2017 Sudan Conference on Computer Science and Information Technology (SCCSIT). IEEE, 2017. http://dx.doi.org/10.1109/sccsit.2017.8293061.

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Han, Qilong, Dan Lu, and Rui Chen. "Fine-Grained Air Quality Inference via Multi-Channel Attention Model." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/346.

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In this paper, we study the problem of fine-grained air quality inference that predicts the air quality level of any location from air quality readings of nearby monitoring stations. We point out the importance of explicitly modeling both static and dynamic spatial correlations, and consequently propose a novel multi-channel attention model (MCAM) that models static and dynamic spatial correlations as separate channels. The static channel combines the beauty of attention mechanisms and graph-based spatial modeling via an adapted bilateral filtering technique, which considers not only locations' Euclidean distances but also their similarity of geo-context features. The dynamic channel learns stations' time-dependent spatial influence on a target location at each time step via long short-term memory (LSTM) networks and attention mechanisms. In addition, we introduce two novel ideas, atmospheric dispersion theories and the hysteretic nature of air pollutant dispersion, to better model the dynamic spatial correlation. We also devise a multi-channel graph convolutional fusion network to effectively fuse the graph outputs, along with other features, from both channels. Our extensive experiments on real-world benchmark datasets demonstrate that MCAM significantly outperforms the state-of-the-art solutions.
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Dong, Jiaxue, Dunbiao Niu, and Enbin Song. "An approach based on Chernoff distance to sparse sensing for distributed detection." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009840.

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Saha, Gobinda C., A. Mateen, and Tahir I. Khan. "Tribological Performance Study of HVOF-Sprayed Microstructured and Nanostructured WC-17wt.%Co Coatings." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-40086.

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Abrasive and erosive wear of components and machinery is an ongoing challenge in the oil sands industry in northern Alberta, Canada. To improve the wear resistance by increasing surface hardness of steels, heat treatments and deposition of hard layers of metal alloys (such as stellite) by fusion welding techniques are traditionally used. However, these deposition techniques are not applicable to all shapes and add considerable weight to the final component. Thermal spraying techniques such as the use of high velocity oxy-fuel (HVOF) composite coatings based on WC-Co cermet system offer better wear resistance and greater flexibility in applications. This study presents work on two feedstock powders, namely nanocrystalline and microcrystalline WC-Co cermets, with identical matrix phase content: WC-17wt.%Co. The novelty of the research is that an engineered duplex Co coated WC-17wt.%Co cermet particle designed to withstand coating spalling under elevated loads as well as to limit abrasive debridement during wear is introduced for the first time to produce a more homogeneously-dispersed coating microstructure. The engineered particle has 6wt.% of the ductile phase material mixed into the core to insure that the reinforcement WC phase is discontinuous to limit the debridement during wear, while remainder (11wt.%) of the Co is applied as a coating on the particle to improve the ductility. The mechanical properties of the overall particle are further improved by controlling the size of the reinforcing phase (WC) in the matrix (Co). This resulted in a WC-17wt.%Co particle containing a characteristic WC grain in the order of 350 nm in the core with the Co outer coating of 1–2 μm thick, making the powder particle as nanocrystalline. HVOF deposited coatings of the nanocrystalline and microcrystalline powders were examined for microhardness, fracture toughness, sliding abrasion (ASTM G133-05) and dry-sand rubber wheel abrasion (ASTM G65-04) wear performance. The wear rate under various loads and sliding distances was studied. In both the coatings, it was found that the wear rate increased with increasing applied loads, while it decreased with increasing sliding distances. 3D surface analysis of the wear tracks using atomic force microscopy (AFM) revealed two distinctive mechanisms associated with the two coatings after abrasive wear. The improved wear resistance was attributed to the higher hardness value of the nanostructured WC-17wt.%Co coating. It was also found that the nanostructured WC-17wt.%Co coating has about twice the toughness of the conventional microstructured coating counterpart. The extent of the WC decarburization and the dissolution of Co in the coatings were also studied.
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Hanselmann, Anne, Oliver C. Schrempf, and Uwe D. Hanebeck. "Optimal parametric density estimation by minimizing an analytic distance measure." In 2007 10th International Conference on Information Fusion. IEEE, 2007. http://dx.doi.org/10.1109/icif.2007.4408100.

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Gregoire, Eric. "Extension of a distance-based fusion framework." In Defense and Security, edited by Belur V. Dasarathy. SPIE, 2004. http://dx.doi.org/10.1117/12.540820.

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Torres-Torriti, Miguel, and Andres Guesalaga. "Automatic ship positioning and radar biases correction using the hausdorff distance." In 2007 10th International Conference on Information Fusion. IEEE, 2007. http://dx.doi.org/10.1109/icif.2007.4408137.

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Xiangjie Yang, Yunlong Sheng, L. Sevigny, and P. Valin. "Robust multisensor image registration with partial distance merits." In Proceedings of the Third International Conference on Information Fusion. IEEE, 2000. http://dx.doi.org/10.1109/ific.2000.862645.

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Reports on the topic "Fusion distances"

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Varshney, Pramod K., and Wael Hashlamoun. ALGORITHMS FOR SENSOR FUSION: Applications of Distance Measures and Probability of Error Bounds to Distributed. Detection Systems. Volume 2. Fort Belvoir, VA: Defense Technical Information Center, December 1991. http://dx.doi.org/10.21236/ada254634.

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