Literatura académica sobre el tema "Segmentation d'action"
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Tesis sobre el tema "Segmentation d'action"
Illanes, Manriquez Alfredo. "Segmentation de l’électrocardiogramme pour la modélisation de la dynamique du QT lors de l’exercice du handgrip". Rennes 1, 2008. http://www.theses.fr/2008REN1S002.
Texto completoThe objective of this thesis is the study of the dynamics of the cardiac electric activity by the analysis of standard electrocardiogram (ECG) signals. It has been shown that, under certain conditions for recording ECG signals, there exists a relationship between the QT and TQ intervals which is similar to a relationship already established in the field of cellular cardiac electric modeling. This latter, known under the name of restitution curve, relates the action potential duration (APD) to the diastolic interval (DI) of a cardiac cell. In order to achieve this objective, it has been necessary to develop segmentation algorithms measuring the QT and TQ intervals with a sufficient accuracy, and more importantly, with a great robustness to process non stationary ECG signals. Studies have also been conducted on the similarity between the two relationships at the ECG level and at the cellular level, through a mathematical model initially established at the cellular level
Illanes, Manriquez Alfredo Zhang Qinghua. "Segmentation de l'électrocardiogramme pour la modélisation de la dynamique du QT lors de l'exercice du handgrip". [S.l.] : [s.n.], 2008. ftp://ftp.irisa.fr/techreports/theses/2008/illanes.pdf.
Texto completoRadouane, Karim. "Mécanisme d’attention pour le sous-titrage du mouvement humain : Vers une segmentation sémantique et analyse du mouvement interprétables". Electronic Thesis or Diss., IMT Mines Alès, 2024. http://www.theses.fr/2024EMAL0002.
Texto completoCaptioning tasks mainly focus on images or videos, and seldom on human poses. Yet, poses concisely describe human activities. Beyond text generation quality, we consider the motion caption task as an intermediate step to solve other derived tasks. In this holistic approach, our experiments are centered on the unsupervised learning of semantic motion segmentation and interpretability. We first conduct an extensive literature review of recent methods for human pose estimation, as a central prerequisite for pose-based captioning. Then, we take an interest in pose-representation learning, with an emphasis on the use of spatiotemporal graph-based learning, which we apply and evaluate on a real-world application (protective behavior detection). As a result, we win the AffectMove challenge. Next, we delve into the core of our contributions in motion captioning, where: (i) We design local recurrent attention for synchronous text generation with motion. Each motion and its caption are decomposed into primitives and corresponding sub-captions. We also propose specific metrics to evaluate the synchronous mapping between motion and language segments. (ii) We initiate the construction of a motion-language dataset to enable supervised segmentation. (iii) We design an interpretable architecture with a transparent reasoning process through spatiotemporal attention, showing state-of-the-art results on the two reference datasets, KIT-ML and HumanML3D. Effective tools are proposed for interpretability evaluation and illustration. Finally, we conduct a thorough analysis of potential applications: unsupervised action segmentation, sign language translation, and impact in other scenarios
Baillie, Jean-Christophe. "Apprentissage et reconnaissance qualitative d'actions dans des séquences vidéo". Paris 6, 2001. http://www.theses.fr/2001PA066533.
Texto completoChan-Hon-Tong, Adrien. "Segmentation supervisée d'actions à partir de primitives haut niveau dans des flux vidéos". Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066226/document.
Texto completoThis thesis focuses on the supervised segmentation of video streams within the application context of daily action recognition.A segmentation algorithm is obtained from Implicit Shape Model by optimising the votes existing in this polling method.We prove that this optimisation can be linked to the sliding windows plus SVM framework and more precisely is equivalent with a standard training by adding temporal constraint, or, by encoding the data through a dense pyramidal decomposition. This algorithm is evaluated on a public database of segmentation where it outperforms other Implicit Shape Model like methods and the standard linear SVM.This algorithm is then integrated into a action segmentation system.Specific features are extracted from skeleton obtained from the video by standard software.These features are then clustered and given to the polling method.This system, combining our feature and our algorithm, obtains the best published performance on a human daily action segmentation dataset
Wang, Zhen. "Extraction en langue chinoise d'actions spatiotemporalisées réalisées par des personnes ou des organismes". Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016INAL0006.
Texto completoWe have developed an automatic analyser and an extraction module for Chinese langage processing. The analyser performs automatic Chinese word segmentation based on linguistic rules and dictionaries, part-of-speech tagging based on n-gram statistics and dependency grammar parsing. The module allows to extract information around named entities and activities. In order to achieve these goals, we have tackled the following main issues: segmentation and part-of-speech ambiguity; unknown word identification in Chinese text; attachment ambiguity in parsing. Chinese texts are analysed sentence by sentence. Given a sentence, the analyzer begins with typographic processing to identify sequences of Latin characters and numbers. Then, dictionaries are used for preliminary segmentation into words. Linguistic-based rules are used to create proper noun hypotheses and change the weight of some word categories. These rules take into account word context. An n-gram language model is created from a training corpus and selects the best word segmentation and parts-of-speech. Dependency grammar parsing is used to annotate relations between words. A first step of named entity recognition is performed after parsing. Its goal is to identify single-word named entities and noun-phrase-based named entities and to determine their semantic type. These named entities are then used in knowledge extraction. Knowledge extraction rules are used to validate named entities or to change their types. Knowledge extraction consists of two steps: automatic content extraction and tagging from analysed text; extracted contents control and ontology-based co-reference resolution
Jiu, Mingyuan. "Spatial information and end-to-end learning for visual recognition". Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0038/document.
Texto completoIn this thesis, we present our research on visual recognition and machine learning. Two types of visual recognition problems are investigated: action recognition and human body part segmentation problem. Our objective is to combine spatial information such as label configuration in feature space, or spatial layout of labels into an end-to-end framework to improve recognition performance. For human action recognition, we apply the bag-of-words model and reformulate it as a neural network for end-to-end learning. We propose two algorithms to make use of label configuration in feature space to optimize the codebook. One is based on classical error backpropagation. The codewords are adjusted by using gradient descent algorithm. The other is based on cluster reassignments, where the cluster labels are reassigned for all the feature vectors in a Voronoi diagram. As a result, the codebook is learned in a supervised way. We demonstrate the effectiveness of the proposed algorithms on the standard KTH human action dataset. For human body part segmentation, we treat the segmentation problem as classification problem, where a classifier acts on each pixel. Two machine learning frameworks are adopted: randomized decision forests and convolutional neural networks. We integrate a priori information on the spatial part layout in terms of pairs of labels or pairs of pixels into both frameworks in the training procedure to make the classifier more discriminative, but pixelwise classification is still performed in the testing stage. Three algorithms are proposed: (i) Spatial part layout is integrated into randomized decision forest training procedure; (ii) Spatial pre-training is proposed for the feature learning in the ConvNets; (iii) Spatial learning is proposed in the logistical regression (LR) or multilayer perceptron (MLP) for classification
Libros sobre el tema "Segmentation d'action"
Gresse, Carole. Fragmentation des marchés d'actions et concurrence entre systèmes d'échange. Paris: Economica, 2001.
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