Дисертації з теми "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.
Повний текст джерелаOgden, Samuel R. "Automatic Content-Based Temporal Alignment of Image Sequences with Varying Spatio-Temporal Resolution." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3303.
Повний текст джерелаSpiegel, 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.
Повний текст джерелаPinto, 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.
Повний текст джерелаThis 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.
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.
Повний текст джерелаUmakanthan, Sabanadesan. "Human action recognition from video sequences." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/93749/1/Sabanadesan_Umakanthan_Thesis.pdf.
Повний текст джерелаStéphanou, 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.
Повний текст джерелаDe, 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.
Повний текст джерелаMuraleedharan, Nair Jayakrishnan. "Signature Verification Model: A Long Term Memory Approach." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1427210243.
Повний текст джерелаZiaeetabar, 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.
Повний текст джерелаSingh, Aditya P. "Stochastic and spatio-temporal modeling in systems biology." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 189 p, 2007. http://proquest.umi.com/pqdlink?did=1251904591&Fmt=7&clientId=79356&RQT=309&VName=PQD.
Повний текст джерелаXiao, Ying. "Mining crop sequence patterns at a large regional scale : A case from mainland France." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0122/document.
Повний текст джерелаIn the context of changing agricultural policy, the development of agricultural production systems, increasing concern for agricultural sustainability and shifts in agricultural land management practice-related land-use change, the main objective of this thesis was to mine crop sequence patterns (CSP) and the relationships between CSP and the biophysical and socio-technical-economic conditions in mainland France from historical census data (e.g. land-cover survey, agricultural censuses, population census). Our study period 1992-2003 covers the implementation period of the 1992 European Union Common Agricultural Policy reform and Agenda 2000 in France. Both the classical statistical and data mining technique were applied in alone or combined ways in this thesis. First, we proposed an innovative approach to representing CSP within a given area and period at a large regional scale in a stationary way. The 2549 3-year crop sequences (CSs) were first identified as major CSs within all 430 agricultural districts (ADs) in mainland France during this period. Next, 21 clusters of ADs , four types of cropping systems, 90 representative ADs and three principal planting zones of cereals, oilseeds, and protein crops belonging to five clusters identified previously were further defined. We then explored CSP in a dynamic way by investigating CSP after grassland-to-cropland conversion, the temporal variability of CSP, and the evolution of the relationships between CSP and the external conditions over the study period. We conclude that the approaches developed here permit the representation of CSP at the large regional scale in both stationary and dynamic ways using time series land-cover data denoting specific agricultural cover types. The findings of this thesis contribute to improving the understanding of the process and pattern of human land management practices by agriculture affecting the terrestrial biosphere
Čížek, Tomáš. "Rozpoznávání událostí ve fotbalu z prostoročasových dat objektů ve hře." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385989.
Повний текст джерелаWei-Jung, Chien, and 錢威融. "The Study of Spatio-Temporal Segmentation for Image Sequences." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/97882048315485152839.
Повний текст джерела國立交通大學
電子工程系
89
In this thesis, we propose a novel approach of motion segmentation. Conventional motion segmentation approaches usually utilize motion estimation to acquire the temporal information of the image sequence. However, this approach basically cannot handle the aperture problem, distortion problem, and occlusion problem. In this thesis, we propose a different approach to avoid these problems. We translate the transform of an image sequence into the spatio-temporal domain. With this translation, the motion segmentation issue becomes a 3-D object segmentation issue in the spatio-temporal domain. In our approach, we first transform the image sequence from RGB color space to CIE L* a* b* color space to achieve the color presentation similar to the perception of human visual system. Then, we perform four directional Balance operators on the image to search for the change points of the color trend. According to these change points, the low contrast regions are merged and then the 2-D image segmentation is achieved. About 3-D object segmentation, we apply the proposed 2-D segmentation approach to three perpendicular planes in the spatio-temporal domain. After combining these 2-D segmentation results together and applying some post-processing, the 3-D object segmentation is completed. The simulation result indicates that the objects in the image sequence can be segmented correctly.
Wang, Shen-Zheng, and 王舜正. "Real-time License Plate Recognition based on Cascaded Rejection Mechanisms to Reduce Spatio-temporal Search Space in Video Sequences." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/12389523070181011363.
Повний текст джерела國立交通大學
資訊科學與工程研究所
96
In surveillance applications, search space reduction (SSR) is an essential element to efficient algorithms. In this study, spatial and temporal SSRs are integrated for license plate recognition (LPR) in video sequences. However, as more features are measured, the computational load may increase significantly. When regard to the fact that most input patterns are negatives, it is apparently efficient to reject a majority of negatives as soon as possible. Therefore, we propose a realtime LPR based on a cascaded rejection framework to reduce spatiotemporal search space rapidly, while ensuring that the performance is high. To extract plates accurately even in complicated situations, two representations, compact plate regions and repeated regions, are first presented. Compact plate regions, which bound the top and bottom of plate characters, could be extracted in the first stage to avoid the use of additional removal procedures. Our method started from spatial SSR by algorithms of one-pass compact plate extraction, bi-level plate character segmentation, and adaptive machine learning. Region candidates of compact plates or plate characters are extracted and verified by these algorithms performed on effectively calculated features, such as vertical gradients and extended Haar-like features. Moreover, we proposed to exclude repeated patterns with the similar appearances in the same location of consecutive frames, which usually include stopped vehicles or regular backgrounds and could be excluded from repeated classification. For efficiency, repeated patterns were detected only on the plate candidates, named spatiotemporal SSR, based on a block-based mechanism by estimating the tangent distance, which is invariant to the variations in positions, sizes, rotations, or brightness. In our experiments, the search space could be reduced up to 87.9% by the spatiotemporal SSR; the LPR system can recognize plates over 38 frames per second with a resolution of 640 x 480 pixels on a 3-GHz Intel P-IV PC.
Ziaeetabar, Fatemeh. "Spatio-temporal reasoning for semantic scene understanding and its application in recognition and prediction of manipulation actions in image sequences." Thesis, 2019. http://hdl.handle.net/21.11130/00-1735-0000-0005-1381-3.
Повний текст джерелаHuang, Chyi, and 黃琦. "Image Sequence Representation and Retrieval Base on Spatio- Temporal Logic." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/49253641279518216102.
Повний текст джерела