Relevant bibliographies by topics / SPATIAL TEMPORAL DESCRIPTOR

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Academic literature on the topic 'SPATIAL TEMPORAL DESCRIPTOR'

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Published: 11 September 2023

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Journal articles on the topic "SPATIAL TEMPORAL DESCRIPTOR"

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Lin, Bo, and Bin Fang. "A new spatial-temporal histograms of gradients descriptor and HOD-VLAD encoding for human action recognition." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 02 (March 2019): 1940009. http://dx.doi.org/10.1142/s0219691319400095.

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Automatic human action recognition is a core functionality of systems for video surveillance and human object interaction. In the whole recognition system, feature description and encoding represent two crucial key steps. In order to construct a powerful action recognition framework, it is important that the two steps must provide reliable performance. In this paper, we proposed a new human action feature descriptor which is called spatio-temporal histograms of gradients (SPHOG). SPHOG is based on the spatial and temporal derivation signal, which extracts the gradient changes between consecutive frames. Compared to the traditional descriptors histograms of optical flow, our proposed SPHOG costs less computation resource. In order to incorporate the distribution information of local descriptors into Vector of Locally Aggregated Descriptors (VLAD), which is a popular encoding approach for Bag-of-Feature representation, a Gaussian kernel is implanted to compute the weighted distance histograms of local descriptors. By doing this, the encoding schema for bag-of-feature (BOF) representation is more effective. We validated our proposed algorithm for human action recognition on three public available datasets KTH, UCF Sports and HMDB51. The evaluation experiment results indicate that the proposed descriptor and encoding method can improve the efficiency of human action recognition and the recognition accuracy.
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Arun Kumar H. D. and Prabhakar C. J. "Moving Vehicles Detection in Traffic Video Using Modified SXCS-LBP Texture Descriptor." International Journal of Computer Vision and Image Processing 5, no. 2 (July 2015): 14–34. http://dx.doi.org/10.4018/ijcvip.2015070102.

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Background modeling and subtraction based method for moving vehicle's detection in traffic video using a novel texture descriptor called as Modified Spatially eXtended Center Symmetric Local Binary Pattern (Modified SXCS-LBP) descriptor. The XCS-LBP texture descriptor is sensitive to noise because in order to generate binary code, the value of center pixel value is used as the threshold directly, and it does not consider temporal motion information. In order to solve this problem, this paper proposed a novel texture descriptor called as Modified SXCS-LBP descriptor for moving vehicle detection based on background modeling and subtraction. The proposed descriptor is robust against noise, illumination variation, and able to detect slow moving vehicles because it considers both spatial and temporal moving information. The evaluation carried out using precision and recall metric, which are obtained using experiments conducted on two popular datasets such as BMC and CDnet datasets. The experimental result shows that the authors' method outperforms existing texture and non-texture based methods.
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Pan, Xianzhang, Wenping Guo, Xiaoying Guo, Wenshu Li, Junjie Xu, and Jinzhao Wu. "Deep Temporal–Spatial Aggregation for Video-Based Facial Expression Recognition." Symmetry 11, no. 1 (January 5, 2019): 52. http://dx.doi.org/10.3390/sym11010052.

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The proposed method has 30 streams, i.e., 15 spatial streams and 15 temporal streams. Each spatial stream corresponds to each temporal stream. Therefore, this work correlates with the symmetry concept. It is a difficult task to classify video-based facial expression owing to the gap between the visual descriptors and the emotions. In order to bridge the gap, a new video descriptor for facial expression recognition is presented to aggregate spatial and temporal convolutional features across the entire extent of a video. The designed framework integrates a state-of-the-art 30 stream and has a trainable spatial–temporal feature aggregation layer. This framework is end-to-end trainable for video-based facial expression recognition. Thus, this framework can effectively avoid overfitting to the limited emotional video datasets, and the trainable strategy can learn to better represent an entire video. The different schemas for pooling spatial–temporal features are investigated, and the spatial and temporal streams are best aggregated by utilizing the proposed method. The extensive experiments on two public databases, BAUM-1s and eNTERFACE05, show that this framework has promising performance and outperforms the state-of-the-art strategies.
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Uddin, Md Azher, Joolekha Bibi Joolee, Young-Koo Lee, and Kyung-Ah Sohn. "A Novel Multi-Modal Network-Based Dynamic Scene Understanding." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 1 (January 31, 2022): 1–19. http://dx.doi.org/10.1145/3462218.

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In recent years, dynamic scene understanding has gained attention from researchers because of its widespread applications. The main important factor in successfully understanding the dynamic scenes lies in jointly representing the appearance and motion features to obtain an informative description. Numerous methods have been introduced to solve dynamic scene recognition problem, nevertheless, a few concerns still need to be investigated. In this article, we introduce a novel multi-modal network for dynamic scene understanding from video data, which captures both spatial appearance and temporal dynamics effectively. Furthermore, two-level joint tuning layers are proposed to integrate the global and local spatial features as well as spatial and temporal stream deep features. In order to extract the temporal information, we present a novel dynamic descriptor, namely, Volume Symmetric Gradient Local Graph Structure ( VSGLGS ), which generates temporal feature maps similar to optical flow maps. However, this approach overcomes the issues of optical flow maps. Additionally, Volume Local Directional Transition Pattern ( VLDTP ) based handcrafted spatiotemporal feature descriptor is also introduced, which extracts the directional information through exploiting edge responses. Lastly, a stacked Bidirectional Long Short-Term Memory ( Bi-LSTM ) network along with a temporal mixed pooling scheme is designed to achieve the dynamic information without noise interference. The extensive experimental investigation proves that the proposed multi-modal network outperforms most of the state-of-the-art approaches for dynamic scene understanding.
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Hu, Xing, Shiqiang Hu, Xiaoyu Zhang, Huanlong Zhang, and Lingkun Luo. "Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/632575.

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We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance.
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Zheng, Aihua, Foqin Wang, Amir Hussain, Jin Tang, and Bo Jiang. "Spatial-temporal representatives selection and weighted patch descriptor for person re-identification." Neurocomputing 290 (May 2018): 121–29. http://dx.doi.org/10.1016/j.neucom.2018.02.039.

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Islam, Md Anwarul, Md Azher Uddin, and Young-Koo Lee. "A Distributed Automatic Video Annotation Platform." Applied Sciences 10, no. 15 (July 31, 2020): 5319. http://dx.doi.org/10.3390/app10155319.

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In the era of digital devices and the Internet, thousands of videos are taken and share through the Internet. Similarly, CCTV cameras in the digital city produce a large amount of video data that carry essential information. To handle the increased video data and generate knowledge, there is an increasing demand for distributed video annotation. Therefore, in this paper, we propose a novel distributed video annotation platform that explores the spatial information and temporal information. Afterward, we provide higher-level semantic information. The proposed framework is divided into two parts: spatial annotation and spatiotemporal annotation. Therefore, we propose a spatiotemporal descriptor, namely, volume local directional ternary pattern-three orthogonal planes (VLDTP–TOP) in a distributed manner using Spark. Moreover, we developed several state-of-the-art appearance-based and spatiotemporal-based feature descriptors on top of Spark. We also provide the distributed video annotation services for the end-users so that they can easily use the video annotation and APIs for development to produce new video annotation algorithms. Due to the lack of a spatiotemporal video annotation dataset that provides ground truth for both spatial and temporal information, we introduce a video annotation dataset, namely, STAD which provides ground truth for spatial and temporal information. An extensive experimental analysis was performed in order to validate the performance and scalability of the proposed feature descriptors, which proved the excellence of our proposed approach.
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SEURONT, LAURENT, and YVAN LAGADEUC. "VARIABILITY, INHOMOGENEITY AND HETEROGENEITY: TOWARDS A TERMINOLOGICAL CONSENSUS IN ECOLOGY." Journal of Biological Systems 09, no. 02 (June 2001): 81–87. http://dx.doi.org/10.1142/s0218339001000281.

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Current widespread use of ecological terms such as variability, heterogeneity and homogeneity is misleading and prevents ecologists from reaching a terminological consensus on what is meant when discussing these concepts, in particular with regard to the descriptor 'heterogeneous.' We propose the use of 'inhomogeneity' to define patterns or processes exhibiting a scale-dependent structure, whether spatial or temporal. Thus, the concept of 'inhomogeneity' can be regarded as a structural ecological entity. A descriptor exhibiting different kinds of inhomogeneity, either spatially or temporally, will then be qualified as being heterogeneous. The terminological consensus introduced here in the particular frame of ecological sciences is finally discussed and generalized to the actual scientific thought process.
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Inturi, Anitha Rani, Vazhora Malayil Manikandan, Mahamkali Naveen Kumar, Shuihua Wang, and Yudong Zhang. "Synergistic Integration of Skeletal Kinematic Features for Vision-Based Fall Detection." Sensors 23, no. 14 (July 10, 2023): 6283. http://dx.doi.org/10.3390/s23146283.

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According to the World Health Organisation, falling is a major health problem with potentially fatal implications. Each year, thousands of people die as a result of falls, with seniors making up 80% of these fatalities. The automatic detection of falls may reduce the severity of the consequences. Our study focuses on developing a vision-based fall detection system. Our work proposes a new feature descriptor that results in a new fall detection framework. The body geometry of the subject is analyzed and patterns that help to distinguish falls from non-fall activities are identified in our proposed method. An AlphaPose network is employed to identify 17 keypoints on the human skeleton. Thirteen keypoints are used in our study, and we compute two additional keypoints. These 15 keypoints are divided into five segments, each of which consists of a group of three non-collinear points. These five segments represent the left hand, right hand, left leg, right leg and craniocaudal section. A novel feature descriptor is generated by extracting the distances from the segmented parts, angles within the segmented parts and the angle of inclination for every segmented part. As a result, we may extract three features from each segment, giving us 15 features per frame that preserve spatial information. To capture temporal dynamics, the extracted spatial features are arranged in the temporal sequence. As a result, the feature descriptor in the proposed approach preserves the spatio-temporal dynamics. Thus, a feature descriptor of size [m×15] is formed where m is the number of frames. To recognize fall patterns, machine learning approaches such as decision trees, random forests, and gradient boost are applied to the feature descriptor. Our system was evaluated on the UPfall dataset, which is a benchmark dataset. It has shown very good performance compared to the state-of-the-art approaches.
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Yan, Jing Jie, and Ming Han Xin. "Facial Expression Recognition Based on Fused Spatio-Temporal Features." Applied Mechanics and Materials 347-350 (August 2013): 3780–85. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3780.

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Although spatio-temporal features (ST) have recently been developed and shown to be available for facial expression recognition and behavior recognition in videos, it utilizes the method of directly flattening the cuboid into a vector as a feature vector for recognition which causes the obtained vector is likely to potentially sensitive to small cuboid perturbations or noises. To overcome the drawback of spatio-temporal features, we propose a novel method called fused spatio-temporal features (FST) method utilizing the separable linear filters to detect interesting points and fusing two cuboids representation methods including local histogrammed gradient descriptor and flattening the cuboid into a vector for cuboids descriptor. The proposed FST method may robustness to small cuboid perturbations or noises and also preserve both spatial and temporal positional information. The experimental results on two video-based facial expression databases demonstrate the effectiveness of the proposed method.
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Dissertations / Theses on the topic "SPATIAL TEMPORAL DESCRIPTOR"

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Megrhi, Sameh. "Spatio-temporal descriptors for human action recognition." Thesis, Paris 13, 2014. http://www.theses.fr/2014PA131046/document.

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L'analyse et l’interprétation de contenus visuels et plus particulièrement la vidéo est un domaine de recherche de plus en plus attractif en raison du nombre important d'applications telles que la vidéo-surveillance, le résumé de films, l'indexation, les jeux vidéo, la robotique et la domotique. Dans cette thèse nous nous intéressons à la détection et à la reconnaissance d'actions humaines dans des séquences vidéo. Pour la partie détection des actions, nous avons introduit deux approches basées sur les points d'intérêts locaux. La première proposition est une méthode simple et efficace qui vise à détecter les mouvements humains ensuite contribuer à extraire des séquences vidéo décrivant des actions importantes. Afin d'atteindre cet objectif, les premières séquences vidéo sont segmentées en volumes de trames et groupes de points d’intérêts. Dans cette méthode, nous nous basons sur le suivi du mouvement des points d'intérêts. Nous avons utilisé, dans un premier lieu, des vidéos simples puis nous avons progressivement augmenté la complexité des vidéos en optant pour des scènes réalistes. Les jeux de données simples présentent généralement un arrière-plan statique avec un Seul acteur qui effectue une seule action unique ou bien la même action mais d'une manière répétitive. Nous avons ensuite testé la robustesse de la détection d'action proposée dans des jeux de données plus complexes réalistes recueillis à partir des réseaux sociaux. Nous avons introduit une approche de détection d'actions efficace pour résoudre le problème de la reconnaissance d'actions humaines dans les vidéos réalistes contenant des mouvements de caméra. Le mouvement humain est donc segmenté d'une manière spatio-temporelle afin de détecter le nombre optimal de trames suffisant pour effectuer une description vidéo. Les séquences sont décrites au moyen de descripteurs spatio-temporels. Nous avons proposé dans cette thèse deux nouveaux descripteurs spatio-temporels basés sur le suivi de la trajectoire des points d'intérêts. Les suivis et la description vidéo sont effectués sur les patchs vidéo qui contiennent un mouvement ou une partie d'un mouvement détecté par la segmentation réalisée lors de l'étape précédente. Nous nous sommes basés sur le descripteur SURF non seulement pour sa précision et mais surtout pour la rapidité. Le premier descripteur proposé est appelé ST-SURF basé sur une nouvelle combinaison du (SURF) et du flot optique. Le ST-SURF permet le suivi de la trajectoire des points d'intérêts tout en gardant les informations spatiales, pertinentes, provenant du SURF. Le deuxième descripteur proposé dans le cadre de cette thèse est un histogramme du mouvement de la trajectoire (HMTO). HMTO est basé sur la position ainsi que l'échelle relative à un SURF. Ainsi, pour chaque SURF détecté, nous définissons une région du voisinage du point d'intérêt en nous basant sur l'échelle. Pour le patch détecté, nous extrayons le flot optique d'une manière dense. Les trajectoires de mouvement sont ensuite générées pour chaque pixel en exploitant les composantes horizontale et verticale de flot optique (u, v). La précision de la description de la vidéo proposée est testée sur un ensemble de données complexes et un plus grand ensemble de données réalistes. Les descripteurs de vidéo proposés sont testés d'une manière simple puis en les fusionnants avec d'autres descripteurs. Les descripteurs vidéo ont été introduits dans un processus de classification basé sur le sac de mots et ont démontré une amélioration des taux de reconnaissance par rapport aux approches précédemment proposés dans l'état-de-l ‘art
Due to increasing demand for video analysis systems in recent years, human action de-tection/recognition is being targeted by the research community in order to make video description more accurate and faster, especially for big datasets. The ultimate purpose of human action recognition is to discern automatically what is happening in any given video. This thesis aims to achieve this purpose by contributing to both action detection and recognition tasks. We thus have developed new description methods for human action recognition.For the action detection component we introduce two novel approaches for human action detection. The first proposition is a simple yet effective method that aims at detecting human movements. First, video sequences are segmented into Frame Packets (FPs) and Group of Interest Points (GIP). In this method we track the movements of Interest Points in simple controlled video datasets and then in videos of gradually increasing complexity. The controlled datasets generally contain videos with a static background and simple ac-tions performed by one actor. The more complex realistic datasets are collected from social networks.The second approach for action detection attempts to address the problem of human ac-tion recognition in realistic videos captured by moving cameras. This approach works by segmenting human motion, thus investigating the optimal sufficient frame number to per-form action recognition. Using this approach, we detect object edges using the canny edge detector. Next, we apply all the steps of the motion segmentation process to each frame. Densely distributed interest points are detected and extracted based on dense SURF points with a temporal step of N frames. Then, optical flows of the detected key points between two frames are computed by the iterative Lucas and Kanade optical flow technique, using pyramids. Since we are dealing with scenes captured by moving cameras, the motion of objects necessarily involves the background and/or the camera motion. Hence, we propose to compensate for the camera motion. To do so, we must first assume that camera motion exists if most points move in the same direction. Then, we cluster optical flow vectors using a KNN clustering algorithm in order to determine if the camera motion exists. If it does, we compensate for it by applying the affine transformation to each frame in which camera motion is detected, using as input parameters the camera flow magnitude and deviation. Finally, after camera motion compensation, moving objects are segmented using temporal differencing and a bounding box is drawn around each detected moving object. The action recognition framework is applied to moving persons in the bounding box. Our goal is to reduce the amount of data involved in motion analysis while preserving the most important structural features. We believe that we have performed action detection in the spatial and temporal domain in order to obtain better action detection and recognition while at the same time considerably reducing the processing time
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Mercieca, Julian. "Estimation of temporal and spatio-temporal nonlinear descriptor systems." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/19416/.

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As advances in the remote sensing of fluid flows forge ahead at an impressive rate, we face an increasingly compelling question of how best to exploit this progress. Light detection and ranging (LIDAR) measurement equipment still presents the problems of having only radial (line-of-sight) wind speed measurements (Cyclops' dilemma). Substantial expanses of unmeasured flow still remain and range weighting errors have a considerable influence on LIDAR measurements. Clearly, more information needs to be extracted from LIDAR data and an estimation problem naturally arises. A key challenge is that most established estimation techniques, such as Kalman filters, cater for systems that are finite-dimensional and described by ordinary differential equations (ODEs). By contrast, many fluid flows are governed by the Navier-Stokes equations, which are nonlinear partial differential-algebraic equations (PDAEs). With this motivation in mind, this thesis proposes a novel statistical signal processing framework for the model-based estimation of a class of spatio-temporal nonlinear partial differential-algebraic equations (PDAEs). The method employs finite-dimensional reduction that converts this formulation to a nonlinear DAE form for which new unscented transform-based filtering and smoothing algorithms are proposed. Gaussian approximations are derived for differential state variables and more importantly, extended to algebraic state variables. A mean-square error lower bound for the nonlinear descriptor filtering problem is obtained based on the posterior Cramér-Rao inequality. The potential of adopting a descriptor systems approach to spatio-temporal estimation is shown for a wind field estimation problem. A basis function decomposition method is used in conjunction with a pressure Poisson equation (PPE) formulation to yield a spatially-continuous, strangeness-free, reduced-order descriptor flow model which is used to estimate unmeasured wind velocities and pressure over the entire spatial region of interest using sparse measurements from wind turbine-mounted LIDAR instruments. The methodology is validated for both synthetic data generated from large eddy simulations of the atmospheric boundary layer and real-world LIDAR measurement data. Results show that a reconstruction of the flow field is achievable, thus presenting a validated estimation framework for potential applications including wind gust prediction systems, the preview control of wind turbines and other spatio-temporal descriptor systems spanning several disciplines.
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Lättman, Håkan. "Description of spatial and temporal distributions of epiphytic lichens." Licentiate thesis, Linköping University, Linköping University, Department of Physics, Chemistry and Biology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11561.

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Lichens are, in most cases, sensitive to anthropogenic factors such as air pollution, global warming, forestry and fragmentation. Two studies are included in this thesis. The first is an evaluation of the importance of old oak for the rare epiphytic lichen Cliostomum corrugatum (Ach.) Fr. This study analysed whether C. corrugatum was limited by dispersal or restricted to tree stands with an unbroken continuity or the substrate old oaks. The results provide evidence that the investigated five populations in Östergötland, Sweden, of C. corrugatum exhibit substantial gene flow, an effective dispersal and a small genetic variation between the sites. Most of the genetic variation was within the populations. Thus, C. corrugatum is more dependent of the substrate old oaks, rather than limited by dispersal. The second study investigated possible range shift of some common macrolichens, due to global warming, from 64 sites in southern Sweden comparing the two years 1986 and 2003. The centroid of three lichen species had moved a significant distance, all in a north east direction: Hypogymnia physodes (L.) Nyl. and Vulpicida pinastri (Scop.) J.-E. Mattsson and M. J. Lai on the tree species Juniperus communis L. (50 and 151 km, respectively) and H. physodes on Pinus sylvestris L. (41 km). Considering also the non-significant cases, there is strong evidence for a prevailing NE direction of centroid movement.

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Lättman, Håkan. "Description of spatial and temporal distributions of epiphytic lichens /." Linköping : [Department of Physics, Chemistry and Biology], Linköping University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11561.

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Brighi, Marco. "Human Activity Recognition: A Comparative Evaluation of Spatio-Temporal Descriptors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19436/.

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The study presented in this thesis aims to implement and evaluate three different features for the recognition of human activities (HAR). A feature is a piece of information appropriately extracted from raw data that is relevant for solving a particular task. With HAR is meant that discipline able to automatically recognize the activity carried out by an agent through the collection of data. These systems can be realized by exploiting data of different kinds and coming from different sources. Within this study, it was decided to use visual data from properly recorded videos. Each feature is analyzed and evaluated in every detail and finally implemented in a project that uses the most famous and used computer vision and machine learning libraries. The effectiveness of each is evaluated through two different datasets recognized by the scientific community. Each feature was evaluated not only for its performance but also for the computational cost involved in the extraction. The final analysis is therefore the result of a compromise between results obtained and costs to be incurred.
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Whiten, Christopher J. "Probabilistic Shape Parsing and Action Recognition Through Binary Spatio-Temporal Feature Description." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/24006.

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In this thesis, contributions are presented in the areas of shape parsing for view-based object recognition and spatio-temporal feature description for action recognition. A probabilistic model for parsing shapes into several distinguishable parts for accurate shape recognition is presented. This approach is based on robust geometric features that permit high recognition accuracy. As the second contribution in this thesis, a binary spatio-temporal feature descriptor is presented. Recent work shows that binary spatial feature descriptors are effective for increasing the efficiency of object recognition, while retaining comparable performance to state of the art descriptors. An extension of these approaches to action recognition is presented, facilitating huge gains in efficiency due to the computational advantage of computing a bag-of-words representation with the Hamming distance. A scene's motion and appearance is encoded with a short binary string. Exploiting the binary makeup of this descriptor greatly increases the efficiency while retaining competitive recognition performance.
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Mowbray, Stuart David. "Modelling and extracting periodically deforming objects by continuous, spatio-temporal shape description." Thesis, University of Southampton, 2008. https://eprints.soton.ac.uk/266132/.

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This thesis proposes a new model for describing spatio-temporally deforming objects. Through a novel use of Fourier descriptors, it is shown how arbitrary shape description can be extended to include spatio-temporal shape deformation. It is further demonstrated that these new spatio-temporal Fourier descriptors have the ability to be used as the basis for both the recognition and extraction of deforming objects. Application of this new recognition technique to human gait sequences demonstrates recognition rates of over 86% for individual human subjects, signifying that these descriptors possess unique descriptive properties. Based upon the new spatio-temporal Fourier descriptor model, a new technique for the detection and extraction of deforming shapes from an image sequence is presented through a new variant of the Hough transform (the Continuous Deformable Hough Transform) that utilises spatio-temporal shape correlation within an evidence-gathering context. This new technique demonstrates excellent success rates and tolerance to noise, correctly extracting human subjects in image sequences corrupted with noise levels of up to 80%. The technique is also tested extensively using real-world data, thus demonstrating its worth in a modern-day computer vision system. Both the spatio-temporal Fourier descriptor model, the Continuous Deformable Hough Transform, and aspects of their application are fully discussed throughout the thesis, along with ideas and suggestions for future research and development.
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BOUAJJANI, MOHAMMED. "Contribution a l'etude du raisonnement spatio-temporel. Localisation d'un agent et description d'itineraires." Toulouse 3, 1999. http://www.theses.fr/1999TOU30215.

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Cette etude se situe dans le domaine de la representation des connaissances et de la formalisation du raisonnement sur l'action. Notre contribution s'interesse particulierement a la localisation d'un mobile a partir d'une description de son itineraire. Nous considerons une scene constituee d'un ensemble de lieux de reference par rapport auxquels un mobile est localise. Deux relations de localisation sont possibles etre a l'interieur ou etre a l'exterieur d'un lieu. L'itineraire du mobile est decrit a travers sa localisation a differents instants, et a travers des actions de deplacements du type entrer ou sortir d'un lieu. Le probleme qui se pose est : que peut-on deduire sur la localisation du mobile a differents instants? deux axes ont ete developpes. Le premier concerne l'etude des problemes de representation de la localisation du mobile par rapport aux lieux, ainsi que la nature spatio-temporelle des deplacements du mobile. Une theorie spatio-temporelle a ainsi ete proposee pour la description de l'evolution d'un mobile par rapport a un ensemble de lieux. Le deuxieme axe developpe concerne l'etude de la representation logique des actions et de leurs effets et particulierement les problemes du decor, de la ramification, du non determinisme et de la concurrence des actions. Une comparaison des differents formalismes dans ce domaine a ete realisee. Nous avons egalement propose un systeme de raisonnement em (modelisation d'evenements), base sur la notion de dependance entre une action et l'etat d'une propriete, et qui permet de resoudre ces differents problemes. Le systeme em constitue la base formelle permettant de decrire les actions de deplacements (entrer et sortir), les lois sur le comportement du mobile, et les hypotheses concernant l'interpretation d'une description. Nous montrons ensuite que les deductions obtenues sont conformes a nos intuitions et au raisonnement de sens commun (suivant la specification de la classe de scenarios etudiee).
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Gruhier, Elise. "Spatiotemporal description and modeling of mechanical product and its assembly sequence based on mereotopology : theory, model and approach." Thesis, Belfort-Montbéliard, 2015. http://www.theses.fr/2015BELF0276/document.

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L’objectif de cette thèse est de d’écrire l’évolution du produit dans les trois dimensions (spatiale, temporelle et spatio-temporelle). Dans le contexte industriel actuel, les modèles produit sont considérés uniquement du point de vue spatial pendant la phase de conception et du point de vue temporel pendant la phase d’assemblage. Le manque de lien entre le produit et le process mène à des incompréhensions de définition de produit et entraine de mauvaises interprétations en conception. Cependant, le produit ´évolue à travers le temps et subit des changements tout au long des phases de conception et d’assemblage. L’aspect dynamique des activités de conception nécessite de lier ces deux dimensions afin de pouvoir représenter l’évolution du produit et avoir une cohérence des informations. Par conséquent, la dimension spatio-temporelle (i.e. permettant de lier l’espace et le temps) a besoin d’être ajoutée et les relations entre la modélisation du produit et sa séquence d’assemblage ont besoin d’être particulièrement étudiées. Cette thèse en mécanique et conception s’est inspirée de divers domaines comme la gestion des connaissances, les systèmes d’information géographique et la philosophie. Ici le produit est considéré d’un point de vue perdurantiste. Le perdurantisme considère l’objet comme étant compose de tranches temporelles et gardant toujours la même identité quelque soit le changement subi. D’après les précédentes déclarations, cette thèse introduit une nouvelle description du couple produit-process afin d’assurer la compréhension des intentions de conception aux acteurs projet. Dans le but d’atteindre cet objectif, une théorie mereotopologique, permettant de d´écrire le produit comme perçu dans la réalité, et de développée et implémentée dans un modèle ontologique pour être formalisée. La théorie JANUS d´écrit qualitativement l’évolution du produit à travers le temps dans un contexte de conception orientée assemblage, permettant l’intégration de la séquence d’assemblage d`es le début du processus de conception. La théorie permet la description formelle des relations liant les informations et connaissances du couple produit-process. Ces efforts ont pour but d’apporter une base concrète pour la description des changements d’entités spatiales (telles que les composants) et leurs relations `a travers l’espace et le temps. Cette théorie basée sur les régions lie les dimensions spatiale, temporelle et spatio-temporelle et apporte donc une vision perdurantiste en conception de produit. Ensuite, PRONOIA2 – une ontologie formelle basée sur la précédente théorie –développée. De ce fait, les informations liées à l’assemblage sont rendues accessibles et exploitables par des systèmes de gestion d’information et les outils de XAO afin de supporter les activités de l’architecte produit et du concepteur. En effet, les informations et connaissances liées à la conception de produit, ainsi que la séquence d’assemblage associée, ont besoin d’une fondation sémantique et logique afin d’être gérées de manière cohérente et proactive. Suite au développement de la théorie JANUS et de l’ontologie PRONOIA2, l’approche proposée permet d’associer les informations spatiales (gérées par le PDM) et les informations temporelles (gérées par le MPM) à travers des relations mereotopologiques spatio-temporelles. Par conséquent, de nouvelles entités doivent être gérées dans le PLM, en utilisant notamment l’ontologie et un système hub, afin d’assurer un maintien des principes d’ingénierie proactives et améliorer la compréhension de l’architecte produit et du concepteur concernant l’ évolution du produit
The major goal of this research is to describe product evolution in the three dimensions (i.e. spatial, temporal andspatiotemporal). In the current industrial context, product models are only considered from a purely spatial point ofview during the design stage and from a purely temporal point of view during the assembly stage. The lack of linkbetween product and process leads to misunderstanding in engineering definition and causes wrong designinterpretation. However, the product undergoes changes throughout the design and assembly phases. The dynamicaspect of design activities requires linking both dimensions in order to be able to represent product evolution andhave consistent information. As such, spatiotemporal dimension (i.e. linking space and time) needs to be added andrelationships between product modelling and assembly sequences need to be particularly studied.This PhD thesis in mechanical design draws inspiration from several domains such as mathematics, geographicinformation systems and philosophy. Here the product is considered from a perdurantist point of view. Perdurantismregards the object as being composed of temporal slices and always keeping the same identity whatever changesundergone. Based on this statement, this PhD thesis introduces a novel product-process description so as to ensureproduct architect's and designer's understanding of design intents at the early design stages. In order to achieve thisobjective, a mereotopological theory, enabling the product description as it is perceived in the real world, has beendeveloped and implemented in an ontology model to be formalized.The JANUS theory qualitatively describes product evolution over time in the context of AOD, integrating assemblysequence planning in the early product design stages. The theory enables the formal relationships description ofproduct-process design information and knowledge. The proposed efforts aim at providing a concrete basis fordescribing changes of spatial entities (i.e. product parts) and their relationships over time and space. This regionbasedtheory links together spatial, temporal and spatiotemporal dimensions, therefore leading to a perdurantistphilosophy in product design.Then, PRONOIA2 - a formal ontology based on the previous mereotopological theory - is developed. Assemblyinformation is accessible and exploitable by information management systems and computer-aided X tools in orderto support product architects and designer's activities. Indeed product design information and knowledge as well asthe related assembly sequence require a semantic and logical foundation in order to be managed consistently andprocessed proactively.Based on JANUS theory and PRONOIA2 ontology, the MERCURY approach enables associating spatial information(managed by PDM) and temporal information (managed by MPM) through spatiotemporal mereotopologicalrelationships. Therefore, new entities are managed through PLM, using ontology and hub system, so as to ensureproactive engineering and improve product architects' and designers' understanding of product evolution
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Bouchard, Geneviève. "Architecture d'estimateur de canaux pour récepteur à traitement spatio-temporel." Thesis, Université Laval, 2007. http://www.theses.ulaval.ca/2007/24445/24445.pdf.

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Books on the topic "SPATIAL TEMPORAL DESCRIPTOR"

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Baum, Rex I. Kinematics of the Aspen Grove landslide, Ephraim Canyon, central Utah: Description and anlysis of deformational structures and of spatial and temporal patterns of movement of the landslide. Denver, CO: U.S. Geological Survey, 1994.

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Viboud, Cécile, Hélène Broutin, and Gerardo Chowell. Spatial-temporal transmission dynamics and control of infectious diseases: Ebola virus disease (EVD) as a case study. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789833.003.0004.

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Disentangling the spatial-temporal dynamics of infectious disease transmission is important to address issues of disease persistence, epidemic growth and optimal control. In this chapter, we review key concepts relating to the spatial-temporal dynamics of infectious diseases in meta-populations, whereby geographically separate subpopulations are connected by migration or mobility rates. We review the dynamics of colonization, persistence and extinction of emerging and recurrent pathogens in meta-populations; the role of demographic and environmental factors; and geographic heterogeneity in epidemic growth rate. We illustrate theoretical concepts by reviewing the spatial dynamics of childhood diseases and other acute infections in low- and middle-income countries, and provide a detailed description of the spatial-temporal dynamics of the 2014–16 Ebola epidemic in West Africa. We further discuss how increased availability of empirical data and recent methodological developments provide a deeper mechanistic understanding of transmission processes in space and time, and make recommendations for future work.
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Papanicolaou, Andrew C. Overview of Basic Concepts. Edited by Andrew C. Papanicolaou. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199764228.013.002.

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Functional brain imaging is based on a set of assumptions. A clear appreciation of those makes it possible to form reasonable opinions regarding the potential range of application and the limits of the functional neuroimaging procedures. Exposition of these basic facts and assumptions is the primary goal of this chapter. A secondary goal is the juxtaposition of the basic concepts involved in them, such as the concept of “function,” “brain mechanism,” “neuronal network,” “activity,” and “activation”; drawing, in broad outline, a description of the methods of neuroimaging, their relative spatial and temporal resolution, and the type of neurophysiological effects each of them captures and renders in images.
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Eckersley, Andrea. Encountering Surfaces, Encountering Spaces, Encountering Painting. Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474429344.003.0006.

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Encounters are immanent to the event of art, whereby an artwork may be understood by way of the singularities, the differences in intensity, it generates. As an event, art expresses encounters between bodies, surfaces, affects, concepts and percepts. Thinking about artworks in these terms opens up the materiality of art, exposing more of its extensive and intensive properties. It also calls attention to the co-constitution of events of art in diverse spatial, temporal and affective encounters. This argument is developed throughout this chapter by way of a description of the author’s painting practice and intensive encounters with the work of Agnes Martin. Art as an event draws attention to the activity of surfaces and the ways encounters between surfaces, immanent to the work, co-constitute the work of art in space and time.
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Berezin, Mabel. Events as Templates of Possibility: An Analytic Typology of Political Facts. Edited by Jeffrey C. Alexander, Ronald N. Jacobs, and Philip Smith. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780195377767.013.23.

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This article extends the concept of events to bring cultural analysis to bear on political explanation and privileges “thick description” and narrative as methodological tools. Drawing on the views of Emile Durkheim, it argues that events constitute “social facts”—phenomena with sufficient identity and coherence that the social collectivity recognizes them as discrete and important. The article first considers the tension between the political and the cultural using a metaphor from sports and biology that unites agency and nature. It then discusses the intersection of events and experience as an analytic category that incorporates the “counterfactual” turn in historical analysis by drawing on William Sewell’s sociological theory of events. It also argues for the existence of “political facts” and concludes by proposing an analytic typology of political facts based on the classification of events along a temporal or spatial axis.
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Alston, Richard. The Utopian City in Tacitus’ Agricola. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198768098.003.0011.

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This chapter explores Tacitus’ reading of the question of the relationship of the individual to empire in the Agricola. Tacitus constructed an understanding of Rome’s empire as a total system to which there was no spatial or temporal outside. Although it was impossible to imagine Rome without empire, Tacitean ambivalence constructed a third space, neither imperial nor barbarian, in which Tacitus could refuse assimilation into the discourses of empire. Reading the description of the Agricolan city in Agricola 21 alongside the anti-imperial sentiments of Calgacus’ speech (Agricola 30–2), the second section establishes the totalitarian nature of imperial time and space. The concluding section considers the preservation of humanitas in an empire of servitude and argues that Tacitean humanitas is a form of detachment. In comfortable immunitas, the elite could preserve the transcendental values of humanitas, but only through acquiescence in the violent production of imperial spaces and times.
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Schelbert, Heinrich R. Image-Based Measurements of Myocardial Blood Flow. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0024.

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Image-based measurements of myocardial blood flow afford the assessment of coronary circulatory function. They reflect functional consequences of coronary stenoses, diffuse epicardial vessel disease and microvascular dysfunction and structural changes and thus provide a measure of the total ischemic burden. Measured flows contain therefore clinically important predictive information. Fundamental to flow measurements are the tissue tracer kinetics, their description through tracer kinetic models, high spatial and temporal resolution imaging devices and accurate extraction of radiotracer tissue concentrations from dynamically acquired images for estimating true flows from the tissue time activity curves. A large body of literature on measurements of myocardial blood flow exists for defining in humans normal values for flow at baseline and during hyperemic stress as well as for the myocardial flow reserve. The role of PET for flow measurements has been well established; initial results with modern SPECT devices are encouraging. Responses of myocardial blood flow to specific challenges like pharmacologic vasodilation and to sympathetic stimulation can uncover functional consequences of focal epicardial coronary stenoses, of conduit vessel disturbances and disease and impairments of microvascular function. Apart from risk stratification, flow measurements may allow detection of early preclinical disease, influence treatment strategies and identify therapy responses.
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Book chapters on the topic "SPATIAL TEMPORAL DESCRIPTOR"

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Li, Xiang-wei, Gang Zheng, and Kai Zhao. "A Novel Temporal-Spatial Color Descriptor Representation for Video Analysis." In Lecture Notes in Electrical Engineering, 505–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28807-4_70.

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Laptev, Ivan, and Tony Lindeberg. "Local Descriptors for Spatio-temporal Recognition." In Spatial Coherence for Visual Motion Analysis, 91–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11676959_8.

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Tănase, Claudiu, and Bernard Merialdo. "Efficient Spatio-Temporal Edge Descriptor." In Lecture Notes in Computer Science, 210–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27355-1_21.

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Kufer, Stefan, Daniel Blank, and Andreas Henrich. "Using Hybrid Techniques for Resource Description and Selection in the Context of Distributed Geographic Information Retrieval." In Advances in Spatial and Temporal Databases, 330–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40235-7_19.

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Liao, Yihui, Lu Fan, Huiming Ding, and Zhifeng Xie. "Spatial-Temporal Contextual Feature Fusion Network for Movie Description." In Artificial Intelligence, 490–501. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20497-5_40.

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Kwolek, Bogdan, Tomasz Krzeszowski, Agnieszka Michalczuk, and Henryk Josinski. "3D Gait Recognition Using Spatio-Temporal Motion Descriptors." In Intelligent Information and Database Systems, 595–604. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05458-2_61.

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Liu, Yazhou, Shiguang Shan, Xilin Chen, Janne Heikkila, Wen Gao, and Matti Pietikainen. "Spatial-Temporal Granularity-Tunable Gradients Partition (STGGP) Descriptors for Human Detection." In Computer Vision – ECCV 2010, 327–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15549-9_24.

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Mattivi, Riccardo, and Ling Shao. "Spatio-temporal Dynamic Texture Descriptors for Human Motion Recognition." In Intelligent Video Event Analysis and Understanding, 69–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17554-1_4.

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Utasi, Ákos, and Andrea Kovács. "Recognizing Human Actions by Using Spatio-temporal Motion Descriptors." In Advanced Concepts for Intelligent Vision Systems, 366–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17691-3_34.

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Davison, Adrian K., Moi Hoon Yap, Nicholas Costen, Kevin Tan, Cliff Lansley, and Daniel Leightley. "Micro-Facial Movements: An Investigation on Spatio-Temporal Descriptors." In Computer Vision - ECCV 2014 Workshops, 111–23. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16181-5_8.

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Conference papers on the topic "SPATIAL TEMPORAL DESCRIPTOR"

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Yimo Guo, Guoying Zhao, Jie Chen, Matti Pietikainen, and Zhengguang Xu. "Dynamic texture synthesis using a spatial temporal descriptor." In 2009 16th IEEE International Conference on Image Processing (ICIP 2009). IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5414395.

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Yao, Hongxian, Xinghao Jiang, Tanfeng Sun, and Shilin Wang. "3D human action recognition based on the Spatial-Temporal Moving Skeleton Descriptor." In 2017 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2017. http://dx.doi.org/10.1109/icme.2017.8019498.

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Xie, Jiong, and Cunjin Xue. "A top-down hierarchical spatio-temporal process description method and its data organization." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838353.

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Tu, Yunbin, Xishan Zhang, Bingtao Liu, and Chenggang Yan. "Video Description with Spatial-Temporal Attention." In MM '17: ACM Multimedia Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3123266.3123354.

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Wild, Walter J. "Optimal Estimators for Astronomical Adaptive Optics." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/adop.1996.athb.1.

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An adaptive optics system is a closed-loop servo control system that seeks to maximize PSF Strehl ratio performance by minimizing wavefront distortions. The wavefront is sampled over discrete subapertures and the local slopes are used to estimate the instantaneous wavefront shape which is then used to drive a deformable mirror with a discrete array of actuators. The temporal and spatial performance of the system is embodied in a single mathematical descriptor of the form Γ is a covariance matrix of the error between the atmosphere phase φ(ti+1) at time ti+1 and the deformable mirror figure, ϕdm(ti), derived from measurements at the previous time ti. Boldface quantities are vectors and matrices. The phase reflecting off the mirror is φ(ti+1) – ϕ(ti), presently assuming that ϕdm(ti)≈ϕ(ti), where ϕ(ti) is the estimated wavefront phase. Covariance matrices are a powerful mathematical tool because they contain information, in an ensemble average sense, about the sources of error and correlations present in the system. From Γ, the Strehl ratio, the MTF, time-delay, etc., can be computed. The Strehl ratio in the Marechal approximation is S ~ exp(–σ2), where σ2=Tr(Γ) /Na, for Na actuators within the pupil.
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DeMenthon, Daniel, and David Doermann. "Video retrieval using spatio-temporal descriptors." In the eleventh ACM international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/957013.957124.

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Klaeser, A., M. Marszalek, and C. Schmid. "A Spatio-Temporal Descriptor Based on 3D-Gradients." In British Machine Vision Conference 2008. British Machine Vision Association, 2008. http://dx.doi.org/10.5244/c.22.99.

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Lung, Fam Boon, Mohamed Hisham Jaward, and Jussi Parkkinen. "Spatio-temporal descriptor for abnormal human activity detection." In 2015 14th IAPR International Conference on Machine Vision Applications (MVA). IEEE, 2015. http://dx.doi.org/10.1109/mva.2015.7153233.

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Hadjkacem, Bassem, Walid Ayedi, Mohamed Abid, and Hichem Snoussi. "A spatio-temporal covariance descriptor for person re-identification." In 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2015. http://dx.doi.org/10.1109/isda.2015.7489188.

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Gadgil, Neeraj, He Li, and Edward J. Delp. "Spatial subsampling-based multiple description video coding with adaptive temporal-spatial error concealment." In 2015 Picture Coding Symposium (PCS). IEEE, 2015. http://dx.doi.org/10.1109/pcs.2015.7170053.

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Reports on the topic "SPATIAL TEMPORAL DESCRIPTOR"

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Oughstun, Kurt E., and Natalie A. Cartwright. A Research Program on the Asymptotic Description of Electromagnetic Pulse Propagation in Spatially Inhomogeneous, Temporally Dispersive, Attenuative Media. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada474484.

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Robert, Gillian. PR-420-153722-R01 Pipeline Right-of-Way Ground Movement Monitoring from InSAR. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), February 2018. http://dx.doi.org/10.55274/r0011463.

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Longwall mining induces large surface motion that may impact active pipelines. Typical remediation for longwall mining involves shutting down and exposing the pipeline. The use of InSAR has the potential to provide accurate measurements confirming the expected ground movement that will occur with the mining operations. Used correctly, with an appropriate survey design, InSAR can provide extremely high densities of ground movement over time. Exploiting the wide-area capabilities of InSAR could become an important part of integrity management for pipelines where longwall mining is a consideration. InSAR surveys are well suited to the observation of spatially and temporally smooth movements. These movements can be very small (millimetres in months) or larger in areal extent and movement. We have previously shown the ground movement (of 9.8 ft in 12 years) along a pipeline associated with an enhance oil recovery operation. This work examines some of the design considerations necessary to observe fast, large scale deformation with InSAR. This is accomplished through modelling and through the examination of data captured over a pipeline/longwall mine in Pennsylvania. The qualitative description of the passage of the miner is very good. The local ground conditions in Pennsylvania make a more thorough examination of the ground movement available from SAR less accurate than it would be in regions better suited to InSAR measurements (for instance Wyoming).
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