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Relevant bibliographies by topics / Image retrieval deep learning / Dissertations / Theses

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Dissertations / Theses on the topic 'Image retrieval deep learning'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Zhao, Yang. "Person Retrieval with Deep Learning." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/411526.

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Person retrieval aims at matching person images across multiple non-overlapping camera views. It has facilitated a wide range of important applications in intelligent video analysis. The task of person retrieval remains challenging due to dramatic changes on visual appearance that are caused by large intra-class variations from human pose and camera viewpoint, misaligned person detection and occlusion. How to learn discriminative features under these challenging conditions becomes the core issue for the task of person retrieval. According to the input modality, person retrieval could be catego

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Gajić, Bojana. "Training strategies for efficient deep image retrieval." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/673961.

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En aquesta tesi ens centrem en la recuperació i re-identificació d’imatges. L’entrenament de xarxes neuronals profundes usant funcions de pèrdua basades en rànquing ha esdevingut un estàndard de facto per a les tasques de recuperació i re-identificació. Hi analitzem i aportem propostes de respostes a tres qüestions principals: 1) Quines són les estratègies més rellevants dels mètodes de l’estat de l’art i com es poden combinar per obtenir un millor rendiment? 2) Es pot realitzar un mostreig de mostres negatives restrictiu de manera eficient (O(1)) mentre es proporciona un rendiment millorat re

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Morère, Olivier André Luc. "Deep learning compact and invariant image representations for instance retrieval." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066406.

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Nous avons précédemment mené une étude comparative entre les descripteurs FV et CNN dans le cadre de la recherche par similarité d’instance. Cette étude montre notamment que les descripteurs issus de CNN manquent d’invariance aux transformations comme les rotations ou changements d’échelle. Nous montrons dans un premier temps comment des réductions de dimension (“pooling”) appliquées sur la base de données d’images permettent de réduire fortement l’impact de ces problèmes. Certaines variantes préservent la dimensionnalité des descripteurs associés à une image, alors que d’autres l’augmentent,

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Morère, Olivier André Luc. "Deep learning compact and invariant image representations for instance retrieval." Electronic Thesis or Diss., Paris 6, 2016. http://www.theses.fr/2016PA066406.

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Nous avons précédemment mené une étude comparative entre les descripteurs FV et CNN dans le cadre de la recherche par similarité d’instance. Cette étude montre notamment que les descripteurs issus de CNN manquent d’invariance aux transformations comme les rotations ou changements d’échelle. Nous montrons dans un premier temps comment des réductions de dimension (“pooling”) appliquées sur la base de données d’images permettent de réduire fortement l’impact de ces problèmes. Certaines variantes préservent la dimensionnalité des descripteurs associés à une image, alors que d’autres l’augmentent,

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Alzu’bi, Ahmad Gazi Suleiman. "Semantic content-based image retrieval using compact multifeatures and deep learning." Thesis, University of the West of Scotland, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.738480.

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Tursun, Osman. "Missing ingredients in optimising large-scale image retrieval with deep features." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/227803/1/Osman_Tursun_Thesis.pdf.

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This thesis applies advanced image processing and deep machine learning techniques to solve the challenges of large-scale image retrieval. Solutions are provided to overcome key obstacles in real-world large-scale image retrieval applications by introducing unique methods for making deep learning systems more reliable and efficient. The outcome of the research is useful for several image retrieval applications including patent search, and trademark and logo infringement analysis.

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Fuschino, Andrea. "Deep Meta Metric Learning via Learnable Distance." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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In this thesis work, we propose a Deep Metric Learning method via learnable distance to solve image retrieval problems. Unlike the conventional approaches where a certain predefined distance is used (such as the most commonly Euclidean distance) to compute the dissimilarity between embeddings, what we propose is to learn this distance with a neural network. This learned distance has the objective of performing better than a certain target distance (such as the Euclidean distance), trying to put the examples belonging to the same class closer and the examples belonging to different classes fart

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Mom, Kannara. "Deep learning based phase retrieval for X-ray phase contrast imaging." Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0087.

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Le développement de sources de rayons X hautement cohérentes, telles que les installations de rayonnement synchrotron de troisième génération, a contribué de manière significative à l'avancement de l'imagerie à contraste de phase. Le haut degré de cohérence de ces sources permet une mise en œuvre efficace des techniques de contraste de phase et peut augmenter la sensibilité de plusieurs ordres de grandeur. Cette nouvelle technique d'imagerie a trouvé des applications dans un large éventail de domaines, notamment la science des matériaux, la paléontologie, la recherche sur les os, la médecine e

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Moreux, Jean-Philippe, and Guillaume Chiron. "Image Retrieval in Digital Libraries: A Large Scale Multicollection Experimentation of Machine Learning techniques." Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden, 2017. https://slub.qucosa.de/id/qucosa%3A16444.

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While historically digital heritage libraries were first powered in image mode, they quickly took advantage of OCR technology to index printed collections and consequently improve the scope and performance of the information retrieval services offered to users. But the access to iconographic resources has not progressed in the same way, and the latter remain in the shadows: manual incomplete and heterogeneous indexation, data silos by iconographic genre. Today, however, it would be possible to make better use of these resources, especially by exploiting the enormous volumes of OCR produced dur

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Garg, Sourav. "Robust visual place recognition under simultaneous variations in viewpoint and appearance." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/134410/1/Sourav%20Garg%20Thesis.pdf.

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This thesis explores the problem of visual place recognition and localization for a mobile robot, particularly dealing with the challenges of simultaneous variations in scene appearance and camera viewpoint. The proposed methods draw inspiration from humans and make use of semantic cues to represent places. This approach enables effective place recognition from similar or opposing viewpoints, despite variations in scene appearance caused by different times of day or seasons. The research contributions presented in the thesis advance visual place recognition techniques, making them more useful

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Chen, Jianan. "Deep Learning Based Multimodal Retrieval." Electronic Thesis or Diss., Rennes, INSA, 2023. http://www.theses.fr/2023ISAR0019.

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Les tâches multimodales jouent un rôle crucial dans la progression vers l'atteinte de l'intelligence artificielle (IA) générale. L'objectif principal de la recherche multimodale est d'exploiter des algorithmes d'apprentissage automatique pour extraire des informations sémantiques pertinentes, en comblant le fossé entre différentes modalités telles que les images visuelles, le texte linguistique et d'autres sources de données. Il convient de noter que l'entropie de l'information associée à des données hétérogènes pour des sémantiques de haut niveau identiques varie considérablement, ce qui pose

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Li, Yuanxi. "Semantic image similarity based on deep knowledge for effective image retrieval." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/99.

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A flourishing World Wide Web dramatically increases the amount of images uploaded and shared, and exploring them is an interesting and challenging task. While content-based image retrieval, which is based on the low level features extracted from images, has grown relatively mature, human users are more interested in the semantic concepts behind or inside the images. Search that is based solely on the low level features would not be able to satisfy users requirements and not e.ective enough. In order to measure the semantic similarity among images and increase the accuracy of Web image retrie

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Mensink, Thomas. "Learning Image Classification and Retrieval Models." Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENM113/document.

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Nous assistons actuellement à une explosion de la quantité des données visuelles. Par exemple, plusieurs millions de photos sont partagées quotidiennement sur les réseaux sociaux. Les méthodes d'interprétation d'images vise à faciliter l'accès à ces données visuelles, d'une manière sémantiquement compréhensible. Dans ce manuscrit, nous définissons certains buts détaillés qui sont intéressants pour les taches d'interprétation d'images, telles que la classification ou la recherche d'images, que nous considérons dans les trois chapitres principaux. Tout d'abord, nous visons l'exploitation de la n

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Ma, Sihan. "Image Matting via Deep Learning." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22426.

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Image matting aims to extract the accurate opacity of the foreground from the input RGB image, which is beneficial and essential for the subsequent applications, such as image editing, compositing, and film production. Unfortunately, this task is challenging because of its ill-posed nature. Specifically, with the corresponding foreground and background unknown, it is hard to predict the opacity of the foreground from the single RGB image. Recently, deep learning is introduced to image matting to deal with this problem. However, there are still some issues to be addressed. First, the choice of

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Dumas, Thierry. "Deep learning for image compression." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S029/document.

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Ces vingt dernières années, la quantité d’images et de vidéos transmises a augmenté significativement, ce qui est principalement lié à l’essor de Facebook et Netflix. Même si les capacités de transmission s’améliorent, ce nombre croissant d’images et de vidéos transmises exige des méthodes de compression plus efficaces. Cette thèse a pour but d’améliorer par l’apprentissage deux composants clés des standards modernes de compression d’image, à savoir la transformée et la prédiction intra. Plus précisément, des réseaux de neurones profonds sont employés car ils ont un grand pouvoir d’approximati

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Siarohin, Aliaksandr. "Image Animation Using Deep Learning." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/310291.

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Recently media content generation, particularly image and video, using deep learning gained a lot of attention in the research community. One of the main reasons for that is the surge of the interactions in the social networks, that draw a lot of people without specialized backgrounds into the media industry. This raises the interest in the tolls for simplifying the production of the media content, such as images and videos. Another potential avenue for deep learning methods is a simplification of the content generation for the traditional media, especially creation of movies, visual effects f

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Siarohin, Aliaksandr. "Image Animation Using Deep Learning." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/310291.

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Recently media content generation, particularly image and video, using deep learning gained a lot of attention in the research community. One of the main reasons for that is the surge of the interactions in the social networks, that draw a lot of people without specialized backgrounds into the media industry. This raises the interest in the tolls for simplifying the production of the media content, such as images and videos. Another potential avenue for deep learning methods is a simplification of the content generation for the traditional media, especially creation of movies, visual effects f

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Jing, Yushi. "Learning an integrated hybrid image retrieval system." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43746.

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Current Web image search engines, such as Google or Bing Images, adopt a hybrid search approach in which a text-based query (e.g. "apple") is used to retrieve a set of relevant images, which are then refined by the user (e.g. by re-ranking the retrieved images based on similarity to a selected example). This approach makes it possible to use both text information (e.g. the initial query) and image features (e.g. as part of the refinement stage) to identify images which are relevant to the user. One limitation of these current systems is that text and image features are treated as independent c

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Zeledon, Lostalo Emilia Maria. "FMRI IMAGE REGISTRATION USING DEEP LEARNING." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2641.

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fMRI imaging is considered key on the understanding of the brain and the mind, for this reason has been the subject of tremendous research connecting different disciplines. The intrinsic complexity of this 4-D type of data processing and analysis has been approached with every single computational perspective, lately increasing the trend to include artificial intelligence. One step critical on the fMRI pipeline is image registration. A model of Deep Networks based on Fully Convolutional Neural Networks, spatial transformation neural networks with a self-learning strategy was proposed for the i

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Hossain, Md Zakir. "Deep learning techniques for image captioning." Thesis, Hossain, Md. Zakir (2020) Deep learning techniques for image captioning. PhD thesis, Murdoch University, 2020. https://researchrepository.murdoch.edu.au/id/eprint/60782/.

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Generating a description of an image is called image captioning. Image captioning is a challenging task because it involves the understanding of the main objects, their attributes, and their relationships in an image. It also involves the generation of syntactically and semantically meaningful descriptions of the images in natural language. A typical image captioning pipeline comprises an image encoder and a language decoder. Convolutional Neural Networks (CNNs) are widely used as the encoder while Long short-term memory (LSTM) networks are used as the decoder. A variety of LSTMs and CNNs incl

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Tensmeyer, Christopher Alan. "Deep Learning for Document Image Analysis." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7389.

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Automatic machine understanding of documents from image inputs enables many applications in modern document workflows, digital archives of historical documents, and general machine intelligence, among others. Together, the techniques for understanding document images comprise the field of Document Image Analysis (DIA). Within DIA, the research community has identified several sub-problems, such as page segmentation and Optical Character Recognition (OCR). As the field has matured, there has been a trend of moving away from heuristic-based methods, designed for particular tasks and domains of

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Moran, Sean James. "Learning to hash for large scale image retrieval." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20390.

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This thesis is concerned with improving the effectiveness of nearest neighbour search. Nearest neighbour search is the problem of finding the most similar data-points to a query in a database, and is a fundamental operation that has found wide applicability in many fields. In this thesis the focus is placed on hashing-based approximate nearest neighbour search methods that generate similar binary hashcodes for similar data-points. These hashcodes can be used as the indices into the buckets of hashtables for fast search. This work explores how the quality of search can be improved by learning t

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Yang, Cheng 1974. "Image database retrieval with multiple-instance learning techniques." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50505.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.<br>Includes bibliographical references (p. 81-82).<br>In this thesis, we develop and test an approach to retrieving images from an image database based on content similarity. First, each picture is divided into many overlapping regions. For each region, the sub-picture is filtered and converted into a feature vector. In this way, each picture is represented by a number of different feature vectors. The user selects positive and negative image examples to train the system. During th

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Shen, Yuming. "Deep binary representation learning for single/cross-modal data retrieval." Thesis, University of East Anglia, 2018. https://ueaeprints.uea.ac.uk/67635/.

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Data similarity search is widely regarded as a classic topic in the realms of computer vision, machine learning and data mining. Providing a certain query, the retrieval model sorts out the related candidates in the database according to their similarities, where representation learning methods and nearest-neighbour search apply. As matching data features in Hamming space is computationally cheaper than in Euclidean space, learning to hash and binary representations are generally appreciated in modern retrieval models. Recent research seeks solutions in deep learning to formulate the hash func

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Zarrinkoub, Sahand. "Transfer Learning in Deep Structured Semantic Models for Information Retrieval." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286310.

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Recent approaches to IR include neural networks that generate query and document vector representations. The representations are used as the basis for document retrieval and are able to encode semantic features if trained on large datasets, an ability that sets them apart from classical IR approaches such as TF-IDF. However, the datasets necessary to train these networks are not available to the owners of most search services used today, since they are not used by enough users. Thus, methods for enabling the use of neural IR models in data-poor environments are of interest. In this work, a bag

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Southard, Spencer. "Designing 2D Interfaces For 3D Gesture Retrieval Utilizing Deep Learning." UNF Digital Commons, 2017. https://digitalcommons.unf.edu/etd/774.

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Gesture retrieval can be defined as the process of retrieving the correct meaning of the hand movement from a pre-assembled gesture dataset. The purpose of the research discussed here is to design and implement a gesture interface system that facilitates retrieval for an American Sign Language gesture set using a mobile device. The principal challenge discussed here will be the normalization of 2D gestures generated from the mobile device interface and the 3D gestures captured from video samples into a common data structure that can be utilized by deep learning networks. This thesis covers con

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Al-Bander, B. Q. "Retinal image analysis based on deep learning." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3022573/.

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Chen, He. "Microscopic Hyperspectral Image Analysis via Deep Learning." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/396188.

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Hyperspectral imaging (HSI) is a technique that can obtain more spectral information than that in normal color images. Due to this property and strength in material classification, it is widely used in remote sensing, agriculture, and environmental monitoring. In recent years, with the rapid developments of hardware, hyperspectral cameras have become more portable and a ordable. An increasing number of studies are being conducted on HSI systems, and research focuses have expanded from remote sensing to close-range objects. With a proper microscopic kit, a hyperspectral camera can capture image

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Shermin, Tasfia. "Enhancing deep transfer learning for image classification." Thesis, Federation University Australia, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/179551.

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Though deep learning models require a large amount of labelled training data for yielding high performance, they are applied to accomplish many computer vision tasks such as image classification. Current models also do not perform well across different domain settings such as illumination, camera angle and real-to-synthetic. Thus the models are more likely to misclassify unknown classes as known classes. These issues challenge the supervised learning paradigm of the models and encourage the study of transfer learning approaches. Transfer learning allows us to utilise the knowledge acquired fro

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Nugroho, Bayu Adhi. "Advanced Deep Learning for Medical Image Analysis." Thesis, Curtin University, 2022. http://hdl.handle.net/20.500.11937/87912.

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The application of deep learning is evolving, including in expert systems for healthcare, such as disease classification. Several challenges in the use of deep-learning algorithms in application to disease classification. The study aims to improve classification to address the problem. The thesis proposes a cost-sensitive imbalance training algorithm to address an unequal number of training examples, a two-stage Bayesian optimisation training algorithm and a dual-branch network to train a one-class classification scheme, further improving classification performance.

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Altaf, Fouzia. "Deep learning augmentation for medical image analysis." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2603.

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Deep learning is at the center of the current rise of computer aided diagnosis in medical imaging. This technology has the ability to mimic extremely complex mathematical functions for predictive tasks. These functions are encoded as computational models that are learned directly from data. Deep learning models are known to achieve human-level accuracy for predictive tasks. However, such a performance requires that the model is trained on a huge amount of training data. For computer aided diagnosis tasks, the relevant training data needs to be carefully annotated by medical experts. This proce

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Rodés-Guirao, Lucas. "Deep Learning for Digital Typhoon : Exploring a typhoon satellite image dataset using deep learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-249514.

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Efficient early warning systems can help in the management of natural disaster events, by allowing for adequate evacuations and resources administration. Several approaches have been used to implement proper early warning systems, such as simulations or statistical models, which rely on the collection of meteorological data. Data-driven techniques have been proven to be effective to build statistical models, being able to generalise to unseen data. Motivated by this, in this work, we explore deep learning techniques applied to the typhoon meteorological satellite image dataset "Digital Typhoon

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Govindarajan, Hariprasath. "Self-Supervised Representation Learning for Content Based Image Retrieval." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166223.

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Automotive technologies and fully autonomous driving have seen a tremendous growth in recent times and have benefitted from extensive deep learning research. State-of-the-art deep learning methods are largely supervised and require labelled data for training. However, the annotation process for image data is time-consuming and costly in terms of human efforts. It is of interest to find informative samples for labelling by Content Based Image Retrieval (CBIR). Generally, a CBIR method takes a query image as input and returns a set of images that are semantically similar to the query image. The

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Kang, Le. "Document and natural image applications of deep learning." Thesis, University of Maryland, College Park, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3726222.

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<p> A tremendous amount of digital visual data is being collected every day, and we need efficient and effective algorithms to extract useful information from that data. Considering the complexity of visual data and the expense of human labor, we expect algorithms to have enhanced generalization capability and depend less on domain knowledge. While many topics in computer vision have benefited from machine learning, some document analysis and image quality assessment problems still have not found the best way to utilize it. In the context of document images, a compelling need exists for reliab

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Ahn, Euijoon. "Unsupervised Deep Feature Learning for Medical Image Analysis." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23002.

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The availability of annotated image datasets and recent advances in supervised deep learning methods are enabling the end-to-end derivation of representative image features that can impact a variety of image analysis problems. These supervised methods use prior knowledge derived from labelled training data and approaches, for example, convolutional neural networks (CNNs) have produced impressive results in natural (photographic) image classification. CNNs learn image features in a hierarchical fashion. Each deeper layer of the network learns a representation of the image data that is higher le

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Chai, Zhizhi. "Deep learning training optimization for medical image analysis." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24928.

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A computer-aided diagnosis (CAD) system can automatically analyze, quantify, and categorize medical images. Therefore, CAD systems are widely used in patient care. However, current CAD systems are expert-designed with limited capabilities. Recently, deep convolutional neural networks (DCNNs) have achieved great success in automating many medical image interpretation such as lesion segmentation and medical disease classification. These successes are attributed to DCNN’s abilities to automatically learn useful image feature from a large number of annotated images. However, due to complicated dat

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Halling, Leonard. "Feature Extraction for ContentBased Image Retrieval Using a PreTrained Deep Convolutional Neural Network." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-274340.

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This thesis examines the performance of features, extracted from a pre-trained deep convolutional neural network, for content-based image retrieval in images of news articles. The industry constantly awaits improved methods for image retrieval, including the company hosting this research project, who are looking to improve their existing image description-based method for image retrieval. It has been shown that in a neural network, the invoked activations from an image can be used as a high-level representation (feature) of the image. This study explores the efficiency of these features in an

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Xiao, Yao. "Vehicle Detection in Deep Learning." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/91375.

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Computer vision techniques are becoming increasingly popular. For example, face recognition is used to help police find criminals, vehicle detection is used to prevent drivers from serious traffic accidents, and written word recognition is used to convert written words into printed words. With the rapid development of vehicle detection given the use of deep learning techniques, there are still concerns about the performance of state-of-the-art vehicle detection techniques. For example, state-of-the-art vehicle detectors are restricted by the large variation of scales. People working on vehicle

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Wen, Shuangyue. "Automatic Tongue Contour Segmentation using Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38343.

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Ultrasound is one of the primary technologies used for clinical purposes. Ultrasound systems have favorable real-time capabilities, are fast and relatively inexpensive, portable and non-invasive. Recent interest in using ultrasound imaging for tongue motion has various applications in linguistic study, speech therapy as well as in foreign language education, where visual-feedback of tongue motion complements conventional audio feedback. Ultrasound images are known to be difficult to recognize. The anatomical structure in them, the rapidity of tongue movements, also missing segments in some f

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Singh, Amarjot. "ScatterNet hybrid frameworks for deep learning." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/285997.

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Image understanding is the task of interpreting images by effectively solving the individual tasks of object recognition and semantic image segmentation. An image understanding system must have the capacity to distinguish between similar looking image regions while being invariant in its response to regions that have been altered by the appearance-altering transformation. The fundamental challenge for any such system lies within this simultaneous requirement for both invariance and specificity. Many image understanding systems have been proposed that capture geometric properties such as shapes

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Lokegaonkar, Sanket Avinash. "Continual Learning for Deep Dense Prediction." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83513.

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Transferring a deep learning model from old tasks to a new one is known to suffer from the catastrophic forgetting effects. Such forgetting mechanism is problematic as it does not allow us to accumulate knowledge sequentially and requires retaining and retraining on all the training data. Existing techniques for mitigating the abrupt performance degradation on previously trained tasks are mainly studied in the context of image classification. In this work, we present a simple method to alleviate catastrophic forgetting for pixel-wise dense labeling problems. We build upon the regularization te

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Rontauroli, Matteo. "Soluzioni di Deep Learning in ambito di Image Steganography." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20485/.

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La steganografia è la pratica del nascondere un messaggio segreto all’interno di una comunicazione ordinaria. Le sfide in tal senso sono legate ad un’immissione del messaggio che permetta al destinatario di decodificarlo e che allo stesso tempo non sia sospettabile da una parte terza. Nel caso di questo lavoro viene considerata l’incorporazione del messaggio all’interno di immagini, in particolare per due differenti casi, attraverso tecniche di Deep Learning. Nel primo il messaggio segreto è un’immagine delle stesse dimensioni di quella nella quale deve essere incorporata e la trasmissione e

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Alpi, Davide. "Metodi di Deep Learning per Blind Image Quality Assessment." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23084/.

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Le tecniche di Deep Learning per Blind Image Quality Assessment consistono nella progettazione e sviluppo di reti neurali profonde per l'assegnazione di punteggi di qualità alle immagini. In questa tesi viene per prima cosa svolta un'indagine sugli approcci al problema presentati in letteratura. Viene presentato brevemente il funzionamento delle reti neurali convoluzionali e elencati dei modelli di successo, evidenziando per ciascuno le novità architetturali introdotte e i traguardi ottenuti in termini di prestazioni. Si procede poi con la progettazione di diversi modelli single-task e multi-t

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Ishaq, Omer. "Image Analysis and Deep Learning for Applications in Microscopy." Doctoral thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-283846.

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Quantitative microscopy deals with the extraction of quantitative measurements from samples observed under a microscope. Recent developments in microscopy systems, sample preparation and handling techniques have enabled high throughput biological experiments resulting in large amounts of image data, at biological scales ranging from subcellular structures such as fluorescently tagged nucleic acid sequences to whole organisms such as zebrafish embryos. Consequently, methods and algorithms for automated quantitative analysis of these images have become increasingly important. These methods range

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Hou, Xianxu. "An investigation of deep learning for image processing applications." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/52056/.

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Significant strides have been made in computer vision over the past few years due to the recent development in deep learning, especially deep convolutional neural networks (CNNs). Based on the advances in GPU computing, innovative model architectures and large-scale dataset, CNNs have become the workhorse behind the state of the art performance for most computer vision tasks. For instance, the most advanced deep CNNs are able to achieve and even surpass human-level performance in image classification tasks. Deep CNNs have demonstrated the ability to learn very powerful image features or repres

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Wang, Wei. "Image Segmentation Using Deep Learning Regulated by Shape Context." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-227261.

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In recent years, image segmentation by using deep neural networks has made great progress. However, reaching a good result by training with a small amount of data remains to be a challenge. To find a good way to improve the accuracy of segmentation with limited datasets, we implemented a new automatic chest radiographs segmentation experiment based on preliminary works by Chunliang using deep learning neural network combined with shape context information. When the process was conducted, the datasets were put into origin U-net at first. After the preliminary process, the segmented images were

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Cabrera, Gil Blanca. "Deep Learning Based Deformable Image Registration of Pelvic Images." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279155.

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Deformable image registration is usually performed manually by clinicians,which is time-consuming and costly, or using optimization-based algorithms, which are not always optimal for registering images of different modalities. In this work, a deep learning-based method for MR-CT deformable image registration is presented. In the first place, a neural network is optimized to register CT pelvic image pairs. Later, the model is trained on MR-CT image pairs to register CT images to match its MR counterpart. To solve the unavailability of ground truth data problem, two approaches were used. For the

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Pourebadi, Maryam. "A DEEP LEARNING APPROACH FOR BLIND IMAGE QUALITY ASSESSMENT." Kent State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=kent1499797575099571.

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Chen, Yani. "Deep Learning based 3D Image Segmentation Methods and Applications." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1547066297047003.

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Liu, Dongnan. "Supervised and Unsupervised Deep Learning-based Biomedical Image Segmentation." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24744.

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Biomedical image analysis plays a crucial role in the development of healthcare, with a wide scope of applications including the disease diagnosis, clinical treatment, and future prognosis. Among various biomedical image analysis techniques, segmentation is an essential step, which aims at assigning each pixel with labels of interest on the category and instance. At the early stage, the segmentation results were obtained via manual annotation, which is time-consuming and error-prone. Over the past few decades, hand-craft feature based methods have been proposed to segment the biomedical images

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