Relevant bibliographies by topics / Spatio-temporal sequences

Academic literature on the topic 'Spatio-temporal sequences'

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Published: 11 March 2023

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Journal articles on the topic "Spatio-temporal sequences"

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Caspi, Y., and M. Irani. "Spatio-temporal alignment of sequences." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 11 (November 2002): 1409–24. http://dx.doi.org/10.1109/tpami.2002.1046148.

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Horn, D., G. Dror, and B. Quenet. "Dynamic Proximity of Spatio-Temporal Sequences." IEEE Transactions on Neural Networks 15, no. 5 (September 2004): 1002–8. http://dx.doi.org/10.1109/tnn.2004.832809.

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Diego, Ferran, Joan Serrat, and Antonio M. Lopez. "Joint Spatio-Temporal Alignment of Sequences." IEEE Transactions on Multimedia 15, no. 6 (October 2013): 1377–87. http://dx.doi.org/10.1109/tmm.2013.2247390.

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Azzabou, Noura, and Nikos Paragios. "Spatio-temporal speckle reduction in ultrasound sequences." Inverse Problems & Imaging 4, no. 2 (2010): 211–22. http://dx.doi.org/10.3934/ipi.2010.4.211.

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Hach, Thomas, and Tamara Seybold. "Spatio-Temporal Denoising for Depth Map Sequences." International Journal of Multimedia Data Engineering and Management 7, no. 2 (April 2016): 21–35. http://dx.doi.org/10.4018/ijmdem.2016040102.

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This paper proposes a novel strategy for depth video denoising in RGBD camera systems. Depth map sequences obtained by state-of-the-art Time-of-Flight sensors suffer from high temporal noise. Hence, all high-level RGB video renderings based on the accompanied depth maps' 3D geometry like augmented reality applications will have severe temporal flickering artifacts. The authors approached this limitation by decoupling depth map upscaling from the temporal denoising step. Thereby, denoising is processed on raw pixels including uncorrelated pixel-wise noise distributions. The authors' denoising methodology utilizes joint sparse 3D transform-domain collaborative filtering. Therein, they extract RGB texture information to yield a more stable and accurate highly sparse 3D depth block representation for the consecutive shrinkage operation. They show the effectiveness of our method on real RGBD camera data and on a publicly available synthetic data set. The evaluation reveals that the authors' method is superior to state-of-the-art methods. Their method delivers flicker-free depth video streams for future applications.
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Ahn, J. H., and J. K. Kim. "Spatio-temporal visibility function for image sequences." Electronics Letters 27, no. 7 (1991): 585. http://dx.doi.org/10.1049/el:19910369.

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Oliveira, Francisco P. M., Andreia Sousa, Rubim Santos, and João Manuel R. S. Tavares. "Spatio-temporal alignment of pedobarographic image sequences." Medical & Biological Engineering & Computing 49, no. 7 (April 8, 2011): 843–50. http://dx.doi.org/10.1007/s11517-011-0771-x.

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Pavlovskaya, Marina, and Shaul Hochstein. "Explicit Ensemble Perception of Temporal and Spatio-temporal Element Sequences." Journal of Vision 21, no. 9 (September 27, 2021): 2570. http://dx.doi.org/10.1167/jov.21.9.2570.

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Koseoglu, Baran, Erdem Kaya, Selim Balcisoy, and Burcin Bozkaya. "ST Sequence Miner: visualization and mining of spatio-temporal event sequences." Visual Computer 36, no. 10-12 (July 16, 2020): 2369–81. http://dx.doi.org/10.1007/s00371-020-01894-6.

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Addesso, Paolo, Maurizio Longo, Rocco Restaino, and Gemine Vivone. "Spatio-temporal resolution enhancement for cloudy thermal sequences." European Journal of Remote Sensing 52, sup1 (October 11, 2018): 2–14. http://dx.doi.org/10.1080/22797254.2018.1526045.

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Dissertations / Theses on the topic "Spatio-temporal sequences"

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Luo, Ying. "Statistical semantic analysis of spatio-temporal image sequences /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/5884.

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Ogden, Samuel R. "Automatic Content-Based Temporal Alignment of Image Sequences with Varying Spatio-Temporal Resolution." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3303.

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Many applications use multiple cameras to simultaneously capture imagery of a scene from different vantage points on a rigid, moving camera system over time. Multiple cameras often provide unique viewing angles but also additional levels of detail of a scene at different spatio-temporal resolutions. However, in order to benefit from this added information the sources must be temporally aligned. As a result of cost and physical limitations it is often impractical to synchronize these sources via an external clock device. Most methods attempt synchronization through the recovery of a constant scale factor and offset with respect to time. This limits the generality of such alignment solutions. We present an unsupervised method that utilizes a content-based clustering mechanism in order to temporally align multiple non-synchronized image sequences of different and varying spatio-temporal resolutions. We show that the use of temporal constraints and dynamic programming adds robustness to changes in capture rates, field of view, and resolution.
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Spiegel, Rainer. "Human and machine learning of spatio-temporal sequences : an experimental and computational investigation." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619820.

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Pinto, Rafael Coimbra. "Online incremental one-shot learning of temporal sequences." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/49063.

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Este trabalho introduz novos algoritmos de redes neurais para o processamento online de padrões espaço-temporais, estendendo o algoritmo Incremental Gaussian Mixture Network (IGMN). O algoritmo IGMN é uma rede neural online incremental que aprende a partir de uma única passada através de dados por meio de uma versão incremental do algoritmo Expectation-Maximization (EM) combinado com regressão localmente ponderada (Locally Weighted Regression, LWR). Quatro abordagens diferentes são usadas para dar capacidade de processamento temporal para o algoritmo IGMN: linhas de atraso (Time-Delay IGMN), uma camada de reservoir (Echo-State IGMN), média móvel exponencial do vetor de entrada reconstruído (Merge IGMN) e auto-referência (Recursive IGMN). Isso resulta em algoritmos que são online, incrementais, agressivos e têm capacidades temporais e, portanto, são adequados para tarefas com memória ou estados internos desconhecidos, caracterizados por fluxo contínuo ininterrupto de dados, e que exigem operação perpétua provendo previsões sem etapas separadas para aprendizado e execução. Os algoritmos propostos são comparados a outras redes neurais espaço-temporais em 8 tarefas de previsão de séries temporais. Dois deles mostram desempenhos satisfatórios, em geral, superando as abordagens existentes. Uma melhoria geral para o algoritmo IGMN também é descrita, eliminando um dos parâmetros ajustáveis manualmente e provendo melhores resultados.
This work introduces novel neural networks algorithms for online spatio-temporal pattern processing by extending the Incremental Gaussian Mixture Network (IGMN). The IGMN algorithm is an online incremental neural network that learns from a single scan through data by means of an incremental version of the Expectation-Maximization (EM) algorithm combined with locally weighted regression (LWR). Four different approaches are used to give temporal processing capabilities to the IGMN algorithm: time-delay lines (Time-Delay IGMN), a reservoir layer (Echo-State IGMN), exponential moving average of reconstructed input vector (Merge IGMN) and self-referencing (Recursive IGMN). This results in algorithms that are online, incremental, aggressive and have temporal capabilities, and therefore are suitable for tasks with memory or unknown internal states, characterized by continuous non-stopping data-flows, and that require life-long learning while operating and giving predictions without separated stages. The proposed algorithms are compared to other spatio-temporal neural networks in 8 time-series prediction tasks. Two of them show satisfactory performances, generally improving upon existing approaches. A general enhancement for the IGMN algorithm is also described, eliminating one of the algorithm’s manually tunable parameters and giving better results.
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Cheng, Hai-Ling Margaret. "3D spatio-temporal interpolation of of digital image sequences using low-order 3D IIR filters." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq20866.pdf.

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Umakanthan, Sabanadesan. "Human action recognition from video sequences." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/93749/1/Sabanadesan_Umakanthan_Thesis.pdf.

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This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.
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Stéphanou, Angélique. "The Spatio-temporal dynamics of cell membrane deformations and cell migration : a characterization from image sequences and theoretical modelling." Université Joseph Fourier (Grenoble), 2002. http://www.theses.fr/2002GRE19002.

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This thesis concerns the study of cell deformations and is interested in two complementary approaches, an experimental one and another theoretical one. The experimental approach is motivated by the demonstration from previous works of the existence of a certain auto-organization of the deformation patterns. This auto-organization consists of the appearance of recurring protrusive patterns in space and time. This has been shown, in particular, for round-shaped cells (leukocytes or keratinocytes) which present a relatively simple organization of theactin cytoskeleton. We have chosen to study murin fibroblasts (L929 line). The fibroblasts exhibit long membrane extensions such as filopods. This time, this type of protrusion is related to a more complex organization of the actin cytoskeleton, where the filaments tend to form bundles. Our aim has been to determine if there exists a similar self-organized componentof these fibroblasts membrane deformations. Experimental characterization has been performed from image sequences where the cells were observed by phase contrast videomicroscopy. The morphodynamical data of the cells have been extracted from the images with two different methods:(i) a classical segmentation of the cell boundaries for the individual study of each protrusive zone of the cell and (ii) an optical flow method for a global characterizationof the movement of the whole cell. The results obtained show that the cells exhibit mainly symmetrical morphologies with 2 to 4 protrusions. The 4-protrusion state (cross morphology), observed for the most isolated cells, is dynamically characterized by a synchronized pulsating movement between the two perpendicular protrusive directions, where the extension in one direction is accompanied by the simultaneous retraction in the other direction (. . . ) In conclusion, we defined how the work realized experimentally allows us to propose the possibility to use the morphodynamical parameters obtained from the characterization as criteria to identify the cell phenotypes. We also discuss how theoretical modelling can orientate the choice of new experimental protocols.
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De, Groeve Johannes. "A wildlife journey in space and time: methodological advancements in the assessment and analysis of spatio-temporal patterns of animal movement across European landscapes." Doctoral thesis, country:BE, 2018. http://hdl.handle.net/10449/52251.

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Movement is one of the most fundamental processes for living entities on earth at the core of scientific disciplines such as ecology and geography. In animal ecology, ongoing progress in tracking and remote sensing technologies has spurred an explosion of movement and environmental data collected at high spatial and temporal resolution, at a large scale, so that the interaction between animal movement and habitat features can now be investigated in much more detail. As a result, in recent years the field of animal ecology has produced a growing body of studies on movement-based patterns leading to habitat use and selection. In this regard, GIScience has contributed with several visual analytical approaches to study animals in relation to their environment and habitat. However, the pat - terns behind the sequential use of different habitat classes have remained largely unexplored. Sequential habitat use is defined as the consecutive use of habitat features along the trajectory of an animal, extracted from the context of its spatial movement. By account - ing for the sequence of use, it is possible to distinguish fundamentally different behavioural habitat use strategies that are important for the survival and fitness of an animal, such as habitat alternation versus random sequential use. Such distinctions would remain undetected by only considering the proportion of use. Sequential habitat use patterns occur in a spatial context, meaning sequential patterns are affected by what is actually available to the animal. In this dissertation we merge knowledge from different fields to present an innovative method to study the relation between animals and their environment by accounting for the sequential use of habitats, and animal movement rules. We developed a visually effective method to analyse and visualise sequential habitat use patterns of animals at multiple spatio- temporal scales by combining real and simulated sequences of habitat use. To study sequential habitat use patterns we use Sequence Analysis Methods (SAM), an approach widely applied in molecular biology, as well as many applications in different fields, to measure dissimilarity between sequences of characters. In brief, we use dissimilarity algorithms to measure the distance between all pairs of sequences, and then apply a cluster - ing algorithm to investigate how these sequences group together, which are visualised as dissimilarity trees. We propose a procedure consisting of three steps, including explo- ration, simulation and classification. In the exploration phase, we build exploratory trees, which visualise real sequential habitat use patterns. Second, by applying animal movement models we simulate expected sequential habitat use patterns, and assess how spatial context, and especially habitat availability, affects the clustering of sequential patterns. Third, we combine real and simulated sequences to identify which simulated pattern is most parsimonious with the real sequences. The research progress has been presented in three main chapters. In Chapter 3 we present seminal methodological development where SAM was applied to animal movement data. In Chapter 4 we introduce further methodological advancements to extend the applicability of SAM to animal ecology. In Chapter 5 we present a large-scale multi-population ecological application. All research was performed using GPS movement data of roe deer and environmental data provided by the Euroungulates database project. Chapter 3 presents the first application of SAM to identify ecologically relevant sequential patterns in animal habitat use. We exemplify the method using ecological data consisting of simulated and real trajectories from a roe deer population (Capreolus capreolus) in the Italian Alps, expressed as ordered sequences of four habitat use classes, i.e. high/open, high/closed, low/open, low/closed. In essence, the SAM framework identifies relevant sequential patterns in real trajectories by measuring their similarity to spatially-explicit simulated trajectories with known sequential patterns. Simulation trajectories were generated in arenas resembling the landscape structure of the roe deer population. Chapter 4 extends SAM to an individual-based approach (i.e. IM-SAM, Individual Movement – Sequence Analysis Methods), that is applicable over multiple populations. Specifically, instead of performing simulations in landscape-like arenas, we use real individual home ranges, thus accounting for individual spatial context, and landscape composition and structure. To assess usability of our advanced framework we investigate the sequential use of open and forest habitats for nine roe deer populations ranging in landscapes with different geographic contexts and anthropogenic disturbance. We also discuss implications for conservation and management. Chapter 5 addresses the functional role of landscapes throughout seasons by identifying both population level and individual level variability in the sequential habitat use patterns of roe deer, identified in the former nine roe deer populations. We show how identified sequential habitat use patterns can be treated as variables, and analysed with standard and well-accepted statistical methods. While the (IM-)SAM framework was developed for studying sequential habitat use in specific, we highlight that its methodological steps and study design can easily be gener- alised. Indeed, its dissimilarity and clustering algorithms, temporal resolution, sampling units, and number of classes for which sequential patterns are investigated can all be customised for the specific research questions in mind. (IM-)SAM is easily applicable to different types of sequential data that describe aspects of an animal's internal (e.g. heart rate) or external state (e.g. temperature). Through improvements in technology, including the growing number of information that can be collected through sensors (GPS trackers, biologgers and satellites), improving database infrastructures and the instant availability of advanced R packages dedicated to animal movement, (IM-)SAM could be easily integrated in a wide range of both local and broad-scaled behavioural spatio-temporal studies.
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Muraleedharan, Nair Jayakrishnan. "Signature Verification Model: A Long Term Memory Approach." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1427210243.

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Ziaeetabar, Fatemeh [Verfasser], Florentin [Akademischer Betreuer] Wörgötter, Florentin [Gutachter] Wörgötter, Ricarda I. [Gutachter] Schubotz, Dieter [Gutachter] Hogrefe, Marcus [Gutachter] Baum, Carsten [Gutachter] Damm, and Wolfgang [Gutachter] May. "Spatio-temporal reasoning for semantic scene understanding and its application in recognition and prediction of manipulation actions in image sequences / Fatemeh Ziaeetabar ; Gutachter: Florentin Wörgötter, Ricarda I. Schubotz, Dieter Hogrefe, Marcus Baum, Carsten Damm, Wolfgang May ; Betreuer: Florentin Wörgötter." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2020. http://d-nb.info/1208918494/34.

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Books on the topic "Spatio-temporal sequences"

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Cruse, Holk, and Malte Schilling. Pattern generation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0024.

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The faculty to generate patterns is a basic feature of living systems. This chapter concentrates on patterns used in the context of control of behavior. Spatio-temporal patterns appear as quasi-rhythmic patterns mainly in the domain of locomotion (e.g. swimming, flying, walking). Such patterns may be rooted directly in the nervous system itself, or may emerge in interaction with the environment. The examples given show simulation of the corresponding behaviors that in most cases are applied to robots (e.g. walking in an unpredictable environment). In addition, non-rhythmic patterns will be explained which are linked to internal states and are required to select specific behaviors and control behavioral sequences. Such states may be relevant for top-down attention and may or may not be accompanied with subjective experiences, then called mind patterns. Specific cases concern the application of an internal body model, as well as states characterized as cognitive or as conscious.
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Luc, Heres, ed. Time in GIS: Issues in spatio-temporal modelling. Nederlandse Commissie voor Geodesie, 2000. http://dx.doi.org/10.54419/v5m55p.

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Most Geographic Information Systems started as a substitute for loose paper maps. These paper maps did not have a built-in time dimension and could only represent history indirectly as a sequence of physically separate images. This was in fact imitated by these first generation systems. The time dimension could only be represented by means of separate files. A minority of Geographic Information Systems however, started their life as a substitute for ordered lists and tables with a link to paper maps. In these lists, the inclusion of a time com-ponent in the form of a data field was quite usual. This method too was copied by the systems that replaced these paper tables. The current trend in the development of Geographic Information Systems is towards the inte-gration of the classical map-oriented concepts with the table-oriented concepts. This often leads to the explicit embedding of the time component in the GIS environment. The Subcommission Geo-Information Models of the Netherlands Geodetic Commission has organized a workshop to discuss the theory and practice of time and history in GIS on 18 May 2000. This publication contains 6 articles prepared for the workshop. The first paper, written by Donna Peuquet, gives a bird’s-eye view of the current state of the art in spatio-temporal database technology and methodology. She is a well-known expert in the field of spatio-temporal information systems and the author of many articles in this field. The second article is written by Monica Wachowicz. She describes what you can do with a GIS once it contains a historical dimension and how you can detect changes in geographic phenomena. Furthermore, her article suggests how geographic visualisation and knowledge discovery techniques can be integrated in a spatio-temporal database. How to record the time dimension in a database is one thing, how to show this dimension to users is another one. In his contribution, Menno-Jan Kraak first tells about the techniques, which were used in the age of paper maps and the limitations these methods had. He goes on to explain what kind of cartographic techniques have been developed since the mass introduc-tion of the computer. Finally he describes the powerful animation methods which currently exist and can be used on CD-ROM and Internet applications. Peter van Oosterom describes how the time dimension is represented in the information sys-tems of the Cadastre and how this is used to publish updates. The Cadastre has a very long tradition in incorporating the time component, which has always been an inherent component of the cadastral registration. In former times this was translated in very precise procedures about how to update the paper maps and registers. Today it is translated in spatio-temporal database design. The article of Luc Heres tells about the time component in the National Road Database, origi-nally designed for traffic accident registration. This is one of the systems with ''table'' roots and with quite a long tradition in handling the time dimension. He elucidates first the core objects in the conceptual model and how time is added. Next, how this model is translated in a logical design and finally how this is technically implemented. Geologists and geophysicians also have a respectable tradition in handling the time dimension in the data they collect. This is illustrated in the last paper, which is written by Ipo Ritsema. He outlines how time is handled in geological and geophysical databases maintained by TNO. By means of some practical cases he illustrates which problems can be encountered and how these can be solved.
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Book chapters on the topic "Spatio-temporal sequences"

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Patanè, Luca, Roland Strauss, and Paolo Arena. "Learning Spatio-Temporal Behavioural Sequences." In Nonlinear Circuits and Systems for Neuro-inspired Robot Control, 65–85. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73347-0_5.

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Azzabou, Noura, and Nikos Paragios. "Spatio-temporal Speckle Reduction in Ultrasound Sequences." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, 951–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85988-8_113.

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Kaempchen, Nico, Markus Zocholl, and Klaus C. J. Dietmayer. "Spatio–temporal Segmentation Using Laserscanner and Video Sequences." In Lecture Notes in Computer Science, 367–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28649-3_45.

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Hirel, Julien, Philippe Gaussier, and Mathias Quoy. "Model of the Hippocampal Learning of Spatio-temporal Sequences." In Artificial Neural Networks – ICANN 2010, 345–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15825-4_46.

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Zhou, Yihao, and Yan Qiu Chen. "Feature-Assisted Dense Spatio-temporal Reconstruction from Binocular Sequences." In Computer Vision – ACCV 2010, 435–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19282-1_35.

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Brandt, Einar, Lars Wigström, and Bengt Wranne. "Segmentation of Echocardiographic Image Sequences Using Spatio-temporal Information." In Medical Image Computing and Computer-Assisted Intervention – MICCAI’99, 410–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/10704282_45.

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Perperidis, Dimitrios, Raad Mohiaddin, and Daniel Rueckert. "Spatio-Temporal Free-Form Registration of Cardiac MR Image Sequences." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004, 911–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30135-6_111.

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Perperidis, Dimitrios, Raad Mohiaddin, and Daniel Rueckert. "Fast Spatio-temporal Free-Form Registration of Cardiac MR Image Sequences." In Functional Imaging and Modeling of the Heart, 414–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494621_41.

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Sioutis, Michael, Jean-François Condotta, Yakoub Salhi, Bertrand Mazure, and David A. Randell. "Ordering Spatio-Temporal Sequences to Meet Transition Constraints: Complexity and Framework." In IFIP Advances in Information and Communication Technology, 130–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23868-5_10.

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Duchateau, Nicolas, Mathieu De Craene, Xavier Pennec, Beatriz Merino, Marta Sitges, and Bart Bijnens. "Which Reorientation Framework for the Atlas-Based Comparison of Motion from Cardiac Image Sequences?" In Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, 25–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33555-6_3.

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Conference papers on the topic "Spatio-temporal sequences"

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Sawada, Katsutoshi, and Masatoshi Asada. "Spatio-Temporal Scalable Coding of Interlaced Video Sequences." In SMPTE HDTV Workshop. IEEE, 1996. http://dx.doi.org/10.5594/m001249.

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Zafeiriou, Lazaros, Epameinondas Antonakos, Stefanos Zafeiriou, and Maja Pantic. "Joint Unsupervised Deformable Spatio-Temporal Alignment of Sequences." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.368.

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"REFA3D: ROBUST SPATIO-TEMPORAL ANALYSIS OF VIDEO SEQUENCES." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003857203520357.

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Verhein, Florian. "k-STARs: Sequences of Spatio-Temporal Association Rules." In 2006 6th IEEE International Conference on Data Mining Workshops. IEEE, 2006. http://dx.doi.org/10.1109/icdmw.2006.102.

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Chen, Yueguo, Shouxu Jiang, Beng Chin Ooi, and Anthony K. H. Tung. "Querying Complex Spatio-Temporal Sequences in Human Motion Databases." In 2008 IEEE 24th International Conference on Data Engineering (ICDE 2008). IEEE, 2008. http://dx.doi.org/10.1109/icde.2008.4497417.

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Katsaggelos, A. K., J. N. Driessen, S. N. Efstratiadis, and R. L. Lagendijk. "Spatio-Temporal Motion Compensated Noise Filtering Of Image Sequences." In 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, edited by William A. Pearlman. SPIE, 1989. http://dx.doi.org/10.1117/12.970019.

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Bersvendsen, Jørn, Matthew Toews, Adriyana Danudibroto, William M. Wells, Stig Urheim, Raúl San José Estépar, and Eigil Samset. "Robust spatio-temporal registration of 4D cardiac ultrasound sequences." In SPIE Medical Imaging, edited by Neb Duric and Brecht Heyde. SPIE, 2016. http://dx.doi.org/10.1117/12.2217005.

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Chan, C. L., and B. J. Sullivan. "Nonlinear model-based spatio-temporal filtering of image sequences." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.151031.

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Ogden, Samuel R., and Bryan S. Morse. "Automatic content-based temporal alignment of image sequences with varying spatio-temporal resolution." In 2013 IEEE Workshop on Applications of Computer Vision (WACV). IEEE, 2013. http://dx.doi.org/10.1109/wacv.2013.6475027.

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10

Yi Sun and Lijun Yin. "3D Spatio-Temporal face recognition using dynamic range model sequences." In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2008. http://dx.doi.org/10.1109/cvprw.2008.4563125.

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