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Rozprawy doktorskie na temat „Visual learning”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Zhu, Fan. "Visual feature learning." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/8218/.

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Categorization is a fundamental problem of many computer vision applications, e.g., image classification, pedestrian detection and face recognition. The robustness of a categorization system heavily relies on the quality of features, by which data are represented. The prior arts of feature extraction can be concluded in different levels, which, in a bottom up order, are low level features (e.g., pixels and gradients) and middle/high-level features (e.g., the BoW model and sparse coding). Low level features can be directly extracted from images or videos, while middle/high-level features are co

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2

Goh, Hanlin. "Learning deep visual representations." Paris 6, 2013. http://www.theses.fr/2013PA066356.

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Les avancées récentes en apprentissage profond et en traitement d'image présentent l'opportunité d'unifier ces deux champs de recherche complémentaires pour une meilleure résolution du problème de classification d'images dans des catégories sémantiques. L'apprentissage profond apporte au traitement d'image le pouvoir de représentation nécessaire à l'amélioration des performances des méthodes de classification d'images. Cette thèse propose de nouvelles méthodes d'apprentissage de représentations visuelles profondes pour la résolution de cette tache. L'apprentissage profond a été abordé sous deu

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3

Walker, Catherine Livesay. "Visual learning through Hypermedia." CSUSB ScholarWorks, 1996. https://scholarworks.lib.csusb.edu/etd-project/1148.

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4

Owens, Andrew (Andrew Hale). "Learning visual models from paired audio-visual examples." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107352.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 93-104).<br>From the clink of a mug placed onto a saucer to the bustle of a busy café, our days are filled with visual experiences that are accompanied by distinctive sounds. In this thesis, we show that these sounds can provide a rich training signal for learning visual models. First, we propose the task of predicting the sound that an object makes when struck as a way of studying physical

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5

Peyre, Julia. "Learning to detect visual relations." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE016.

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Nous étudions le problème de détection de relations visuelles de la forme (sujet, prédicat, objet) dans les images, qui sont des entités intermédiaires entre les objets et les scènes visuelles complexes. Cette thèse s’attaque à deux défis majeurs : (1) le problème d’annotations coûteuses pour l’entrainement de modèles fortement supervisés, (2) la variation d’apparence visuelle des relations. Nous proposons un premier modèle de détection de relations visuelles faiblement supervisé, n’utilisant que des annotations au niveau de l’image, qui, étant donné des détecteurs d’objets pré-entrainés, atte

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6

Wang, Zhaoqing. "Self-supervised Visual Representation Learning." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29595.

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In general, large-scale annotated data are essential to training deep neural networks in order to achieve better performance in visual feature learning for various computer vision applications. Unfortunately, the amount of annotations is challenging to obtain, requiring a high cost of money and human resources. The dependence on large-scale annotated data has become a crucial bottleneck in developing an advanced intelligence perception system. Self-supervised visual representation learning, a subset of unsupervised learning, has gained popularity because of its ability to avoid the high cost

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7

Tang-Wright, Kimmy. "Visual topography and perceptual learning in the primate visual system." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:388b9658-dceb-443a-a19b-c960af162819.

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The primate visual system is organised and wired in a topological manner. From the eye well into extrastriate visual cortex, a preserved spatial representation of the vi- sual world is maintained across many levels of processing. Diffusion-weighted imaging (DWI), together with probabilistic tractography, is a non-invasive technique for map- ping connectivity within the brain. In this thesis I probed the sensitivity and accuracy of DWI and probabilistic tractography by quantifying its capacity to detect topolog- ical connectivity in the post mortem macaque brain, between the lateral geniculate

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8

Shi, Xiaojin. "Visual learning from small training datasets /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.

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9

Liu, Jingen. "Learning Semantic Features for Visual Recognition." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3358.

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Visual recognition (e.g., object, scene and action recognition) is an active area of research in computer vision due to its increasing number of real-world applications such as video (image) indexing and search, intelligent surveillance, human-machine interaction, robot navigation, etc. Effective modeling of the objects, scenes and actions is critical for visual recognition. Recently, bag of visual words (BoVW) representation, in which the image patches or video cuboids are quantized into visual words (i.e., mid-level features) based on their appearance similarity using clustering, has been wi

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10

Beale, Dan. "Autonomous visual learning for robotic systems." Thesis, University of Bath, 2012. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558886.

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This thesis investigates the problem of visual learning using a robotic platform. Given a set of objects the robots task is to autonomously manipulate, observe, and learn. This allows the robot to recognise objects in a novel scene and pose, or separate them into distinct visual categories. The main focus of the work is in autonomously acquiring object models using robotic manipulation. Autonomous learning is important for robotic systems. In the context of vision, it allows a robot to adapt to new and uncertain environments, updating its internal model of the world. It also reduces the amount

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11

Lakshmi, Ratan Aparna. "Learning visual concepts for image classification." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80092.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.<br>Includes bibliographical references (leaves 166-174).<br>by Aparna Lakshmi Ratan.<br>Ph.D.

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12

Moghaddam, Baback 1963. "Probabilistic visual learning for object detection." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10242.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.<br>Includes bibliographical references (leaves 78-82).<br>by Baback Moghaddam.<br>Ph.D.

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13

Wilson, Andrew David. "Learning visual behavior for gesture analysis." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/62924.

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14

Zhou, Bolei. "Interpretable representation learning for visual intelligence." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117837.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 131-140).<br>Recent progress of deep neural networks in computer vision and machine learning has enabled transformative applications across robotics, healthcare, and security. However, despite the superior performance of the

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15

Pillai, Sudeep. "Learning articulated motions from visual demonstration." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89861.

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Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>35<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 94-98).<br>Robots operating autonomously in household environments must be capable of interacting with articulated objects on a daily basis. They should be able to infer each object's u

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16

Williams, Oliver Michael Christian. "Bayesian learning for efficient visual inference." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613979.

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17

North, Ben. "Learning dynamical models for visual tracking." Thesis, University of Oxford, 1998. http://ora.ox.ac.uk/objects/uuid:6ed12552-4c30-4d80-88ef-7245be2d8fb8.

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Using some form of dynamical model in a visual tracking system is a well-known method for increasing robustness and indeed performance in general. Often, quite simple models are used and can be effective, but prior knowledge of the likely motion of the tracking target can often be exploited by using a specially-tailored model. Specifying such a model by hand, while possible, is a time-consuming and error-prone process. Much more desirable is for an automated system to learn a model from training data. A dynamical model learnt in this manner can also be a source of useful information in its own

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18

Florence, Peter R. (Peter Raymond). "Dense visual learning for robot manipulation." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128398.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2020<br>Cataloged from student-submitted PDF of thesis.<br>Includes bibliographical references (pages 115-127).<br>We would like to have highly useful robots which can richly perceive their world, semantically distinguish its fine details, and physically interact with it sufficiently for useful robotic manipulation. This is hard to ach

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19

Chen, Zhenghao. "Deep Learning for Visual Data Compression." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29729.

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With the tremendous success of neural networks, a few learning-based image codecs were proposed and outperformed those traditional image codecs. However, the field of learning-based compression research for other categories of visual data has remained much less explored. This thesis will investigate the effectiveness of deep learning for visual data compression and propose three end-to-end learning-based compression methods for respectively compressing standard videos, 3D volumetric images and stereo videos. First, we improve the existing learning-based video codecs by using a newly proposed

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20

Dey, Priya. "Visual speech in technology-enhanced learning." Thesis, University of Sheffield, 2012. http://etheses.whiterose.ac.uk/3329/.

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This thesis investigates the use of synthetic talking heads, with lip, tongue and face movements synchronized with synthesized or natural speech, in technology-enhanced learning. This work applies talking heads in a speech tutoring application for teaching English as a second language. Previous studies have shown that speech perception is aided by visual information, but more research is needed to determine the effectiveness of visualization of articulators in pronunciation training. This thesis explores whether or not visual speech technology can give an improvement in learning pronunciation.

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Nguyen, Duc Minh Chau. "Affordance learning for visual-semantic perception." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2443.

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Affordance Learning is linked to the study of interactions between robots and objects, including how robots perceive objects by scene understanding. This area has been popular in the Psychology, which has recently come to influence Computer Vision. In this way, Computer Vision has borrowed the concept of affordance from Psychology in order to develop Visual-Semantic recognition systems, and to develop the capabilities of robots to interact with objects, in particular. However, existing systems of Affordance Learning are still limited to detecting and segmenting object affordances, which is cal

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22

SANGUINETI, VALENTINA. "Audio-Visual Learning for Scene Understanding." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1068960.

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Multimodal deep learning aims at combining the complementary information of different modalities. Among all modalities, audio and video are the predominant ones that humans use to explore the world. In this thesis, we decided to focus our study on audio-visual deep learning to mimic with our networks how humans perceive the world. Our research includes images, audio signals and acoustic images. The latter provide spatial audio information and are obtained from a planar array of microphones combining their raw audios with the beamforming algorithm. They better mimic human auditory systems, whi

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23

Santolin, Chiara. "Learning Regularities from the Visual World." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424417.

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Patterns of visual objects, streams of sounds, and spatiotemporal events are just a few examples of the structures present in a variety of sensory inputs. Amid such variety, numerous regularities can be found. In order to handle the sensory processing, individuals of each species have to be able to rapidly track these regularities. Statistical learning is one of the principal mechanisms that enable to track patterns from the flow of sensory information, by detecting coherent relations between elements (e.g., A predicts B). Once relevant structures are detected, learners are sometimes required

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24

Durand, Thibaut. "Weakly supervised learning for visual recognition." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066142/document.

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Cette thèse s'intéresse au problème de la classification d'images, où l'objectif est de prédire si une catégorie sémantique est présente dans l'image, à partir de son contenu visuel. Pour analyser des images de scènes complexes, il est important d'apprendre des représentations localisées. Pour limiter le coût d'annotation pendant l'apprentissage, nous nous sommes intéressé aux modèles d'apprentissage faiblement supervisé. Dans cette thèse, nous proposons des modèles qui simultanément classifient et localisent les objets, en utilisant uniquement des labels globaux pendant l'apprentissage. L'app

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25

Dancette, Corentin. "Shortcut Learning in Visual Question Answering." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS073.

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Cette thèse se concentre sur la tâche de VQA, c'est à dire les systèmes questions-réponses visuelles. Nous étudions l'apprentissage des biais dans cette tâche. Les modèles ont tendance à apprendre des corrélations superficielles les conduisant à des réponses correctes dans la plupart des cas, mais qui peuvent échouer lorsqu'ils rencontrent des données d'entrée inhabituelles. Nous proposons deux méthodes pour réduire l'apprentissage par raccourci sur le VQA. La première, RUBi, consiste à encourager le modèle à apprendre à partir des exemples les plus difficiles et les moins biaisés grâce à une

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26

Chen, Yifu. "Deep learning for visual semantic segmentation." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS200.

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Dans cette thèse, nous nous intéressons à la segmentation sémantique visuelle, une des tâches de haut niveau qui ouvre la voie à une compréhension complète des scènes. Plus précisément, elle requiert une compréhension sémantique au niveau du pixel. Avec le succès de l’apprentissage approfondi de ces dernières années, les problèmes de segmentation sémantique sont abordés en utilisant des architectures profondes. Dans la première partie, nous nous concentrons sur la construction d’une fonction de coût plus appropriée pour la segmentation sémantique. En particulier, nous définissons une nouvelle

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27

Durand, Thibaut. "Weakly supervised learning for visual recognition." Electronic Thesis or Diss., Paris 6, 2017. http://www.theses.fr/2017PA066142.

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Cette thèse s'intéresse au problème de la classification d'images, où l'objectif est de prédire si une catégorie sémantique est présente dans l'image, à partir de son contenu visuel. Pour analyser des images de scènes complexes, il est important d'apprendre des représentations localisées. Pour limiter le coût d'annotation pendant l'apprentissage, nous nous sommes intéressé aux modèles d'apprentissage faiblement supervisé. Dans cette thèse, nous proposons des modèles qui simultanément classifient et localisent les objets, en utilisant uniquement des labels globaux pendant l'apprentissage. L'app

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28

De, Pasquale Roberto. "Visual discrimination learning and LTP-like changes in primary visual cortex." Doctoral thesis, Scuola Normale Superiore, 2009. http://hdl.handle.net/11384/85939.

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Doyon, Julien. "Right temporal-lobe contribution to global visual processing and visual-cue learning." Thesis, McGill University, 1988. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=75696.

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This thesis explores the visual functions of the right anterior temporal cortex of the human brain. In Part 1, 92 patients with unilateral temporal- or frontal-lobe excisions and 35 normal control subjects were tested under two experimental conditions (global, local) of a reaction-time task, employing hierarchically structured letters or designs as stimuli. In both versions, the right temporal-lobe group was less affected than other groups by interference from the global aspect of the stimulus. These findings support the hypothesis that the right temporal lobe contributes to global visual proc

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30

Gepperth, Alexander Rainer Tassilo. "Neural learning methods for visual object detection." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=981053998.

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31

Qin, Lei. "Online machine learning methods for visual tracking." Thesis, Troyes, 2014. http://www.theses.fr/2014TROY0017/document.

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Nous étudions le problème de suivi de cible dans une séquence vidéo sans aucune connaissance préalable autre qu'une référence annotée dans la première image. Pour résoudre ce problème, nous proposons une nouvelle méthode de suivi temps-réel se basant sur à la fois une représentation originale de l’objet à suivre (descripteur) et sur un algorithme adaptatif capable de suivre la cible même dans les conditions les plus difficiles comme le cas où la cible disparaît et réapparait dans le scène (ré-identification). Tout d'abord, pour la représentation d’une région de l’image à suivre dans le temps,

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Pralle, Mandi Jo. "Visual design in the online learning environment." [Ames, Iowa : Iowa State University], 2007.

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Hussain, Sibt Ul. "Machine Learning Methods for Visual Object Detection." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00680048.

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The goal of this thesis is to develop better practical methods for detecting common object classes in real world images. We present a family of object detectors that combine Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) features with efficient Latent SVM classifiers and effective dimensionality reduction and sparsification schemes to give state-of-the-art performance on several important datasets including PASCAL VOC2006 and VOC2007, INRIA Person and ETHZ. The three main contributions are as follows. Firstly, we pioneer the use of Local Ternar

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34

Cabral, Ricardo da Silveira. "Unifying Low-Rank Models for Visual Learning." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/506.

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Many problems in signal processing, machine learning and computer vision can be solved by learning low rank models from data. In computer vision, problems such as rigid structure from motion have been formulated as an optimization over subspaces with fixed rank. These hard-rank constraints have traditionally been imposed by a factorization that parameterizes subspaces as a product of two matrices of fixed rank. Whilst factorization approaches lead to efficient and kernelizable optimization algorithms, they have been shown to be NP-Hard in presence of missing data. Inspired by recent work in co

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Xu, Yang. "Cortical spatiotemporal plasticity in visual category learning." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/272.

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Central to human intelligence, visual categorization is a skill that is both remarkably fast and accurate. Although there have been numerous studies in primates regarding how information flows in inferiortemporal (ITC) and prefrontal (PFC) cortices during online discrimination of visual categories, there has been little comparable research on the human cortex. To bridge this gap, this thesis explores how visual categories emerge in prefrontal cortex and the ventral stream, which is the human homologue of ITC. In particular, cortical spatiotemporal plasticity in visual category learning was inv

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Ramachandran, Suchitra. "Visual Statistical Learning in Monkey Inferotemporal Cortex." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/463.

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Despite living in noisy sensory environments, humans and non-human primates have the ability to learn regularities and patterns in the environment solely on the basis of passive exposure. This ability to learn what is statistically likely and predictable in the environment is called statistical learning. Visual statistical learning of image sequences has been demonstrated at the level of single neurons in the rhesus macaque (monkey) inferotemporal cortex (IT). Upon subjecting monkeys to extensive exposure to pairs of images presented sequentially such that the display of one image always predi

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37

Frier, Helen Jane. "Compass orientation during visual learning by honeybees." Thesis, University of Sussex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321446.

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Kodirov, Elyor. "Cross-class transfer learning for visual data." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/31852.

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Automatic analysis of visual data is a key objective of computer vision research; and performing visual recognition of objects from images is one of the most important steps towards understanding and gaining insights into the visual data. Most existing approaches in the literature for the visual recognition are based on a supervised learning paradigm. Unfortunately, they require a large amount of labelled training data which severely limits their scalability. On the other hand, recognition is instantaneous and effortless for humans. They can recognise a new object without seeing any visual sam

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Crowley, Elliott Joseph. "Visual recognition in art using machine learning." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:d917f38e-64cb-4b09-9ccf-b081fe68b187.

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This thesis is concerned with the problem of visual recognition in art - such as finding the objects (e.g. cars, cows and cathedrals) present in a painting, or identifying the subject of an oil portrait. Solving this problem is extremely beneficial to art historians, who are often interested in determining when an object first appeared in a painting or how the portrayal of an object has evolved over time. It allows them to avoid the unenviable task of finding paintings for study manually. However, visual recognition of art is a challenging problem, in part due to the lack of annotation in art.

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Kashyap, Karan. "Learning digits via joint audio-visual representations." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113143.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 59-60).<br>Our goal is to explore models for language learning in the manner that humans learn languages as children. Namely, children do not have intermediary text transcriptions in correlating visual and audio inputs from

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41

Gilja, Vikash. "Learning and applying model-based visual context." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/33139.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (p. 53).<br>I believe that context's ability to reduce the ambiguity of an input signal makes it a vital constraint for understanding the real world. I specifically examine the role of context in vision and how a model-based approach can aid visual search and recognition. Through the implementation of a system capable of learning visual context models from an image database, I demonstrate the utility of the model-based approach. The system

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42

Woodley, Thomas Edward. "Visual tracking using offline and online learning." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608814.

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Naha, Shujon. "Zero-shot Learning for Visual Recognition Problems." IEEE, 2015. http://hdl.handle.net/1993/31806.

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In this thesis we discuss different aspects of zero-shot learning and propose solutions for three challenging visual recognition problems: 1) unknown object recognition from images 2) novel action recognition from videos and 3) unseen object segmentation. In all of these three problems, we have two different sets of classes, the “known classes”, which are used in the training phase and the “unknown classes” for which there is no training instance. Our proposed approach exploits the available semantic relationships between known and unknown object classes and use them to transfer the appearance

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Rao, Anantha N. "Learning-based Visual Odometry - A Transformer Approach." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627658636420617.

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Horn, Robert R. "Visual attention and information in observational learning." Thesis, Liverpool John Moores University, 2003. http://researchonline.ljmu.ac.uk/5624/.

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White, Alan Daniel. "Visual-motor learning in minimally invasive surgery." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/17321/.

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The purpose of this thesis was to develop an in-depth understanding of motor control in surgery. This was achieved by applying current theories of sensorimotor learning and developing a novel experimental approach. A survey of expert opinion and a review of the existing literature identified several issues related to human performance and MIS. The approach of this thesis combined existing surgical training tools with state-of-the-art technology and adapted rigorous experimental psychology techniques (grounded in the principles of sensorimotor learning) within a controlled laboratory environmen

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Hanwell, David. "Weakly supervised learning of visual semantic attributes." Thesis, University of Bristol, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.687063.

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There are at present many billions of images on the internet, only a fraction of which are labelled according to their semantic content. To automatically provide labels for the rest, models of visual semantic concepts must be created. Such models are traditionally trained using images which have been manually acquired, segmented, and labelled. In this thesis, we submit that such models can be learned automatically using those few images which have already been labelled, either directly by their creators, or indirectly by their associated text. Such imagery can be acquired easily, cheaply, and

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Hussain, Sabit ul. "Machine Learning Methods for Visual Object Detection." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENM070/document.

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Le but de cette thèse est de développer des méthodes pratiques plus performantes pour la détection d'instances de classes d'objets de la vie quotidienne dans les images. Nous présentons une famille de détecteurs qui incorporent trois types d'indices visuelles performantes – histogrammes de gradients orientés (Histograms of Oriented Gradients, HOG), motifs locaux binaires (Local Binary Patterns, LBP) et motifs locaux ternaires (Local Ternary Patterns, LTP) – dans des méthodes de discrimination efficaces de type machine à vecteur de support latent (Latent SVM), sous deux régimes de réduction de

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Campanholo, Guizilini Vitor. "Non-Parametric Learning for Monocular Visual Odometry." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9903.

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This thesis addresses the problem of incremental localization from visual information, a scenario commonly known as visual odometry. Current visual odometry algorithms are heavily dependent on camera calibration, using a pre-established geometric model to provide the transformation between input (optical flow estimates) and output (vehicle motion estimates) information. A novel approach to visual odometry is proposed in this thesis where the need for camera calibration, or even for a geometric model, is circumvented by the use of machine learning principles and techniques. A non-parametric Bay

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Liu, Li. "Learning discriminative feature representations for visual categorization." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/8239/.

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Learning discriminative feature representations has attracted a great deal of attention due to its potential value and wide usage in a variety of areas, such as image/video recognition and retrieval, human activities analysis, intelligent surveillance and human-computer interaction. In this thesis we first introduce a new boosted key-frame selection scheme for action recognition. Specifically, we propose to select a subset of key poses for the representation of each action via AdaBoost and a new classifier, namely WLNBNN, is then developed for final classification. The experimental results of

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