Relevant bibliographies by topics / Deep learning segmentation / Dissertations / Theses
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Dissertations / Theses on the topic 'Deep learning segmentation'

Author: Grafiati
Published: 19 April 2023
Last updated: 25 July 2025

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1

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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Favia, Federico. "Real-time hand segmentation using deep learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292930.

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Hand segmentation is a fundamental part of many computer vision systems aimed at gesture recognition or hand tracking. In particular, augmented reality solutions need a very accurate gesture analysis system in order to satisfy the end consumers in an appropriate manner. Therefore the hand segmentation step is critical. Segmentation is a well-known problem in image processing, being the process to divide a digital image into multiple regions with pixels of similar qualities. Classify what pixels belong to the hand and which ones belong to the background need to be performed within a real-time p
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Sarpangala, Kishan. "Semantic Segmentation Using Deep Learning Neural Architectures." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304.

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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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¿, Ananya. "DEEP LEARNING METHODS FOR CROP AND WEED SEGMENTATION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1528372119706623.

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Tosteberg, Patrik. "Semantic Segmentation of Point Clouds Using Deep Learning." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136793.

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In computer vision, it has in recent years become more popular to use point clouds to represent 3D data. To understand what a point cloud contains, methods like semantic segmentation can be used. Semantic segmentation is the problem of segmenting images or point clouds and understanding what the different segments are. An application for semantic segmentation of point clouds are e.g. autonomous driving, where the car needs information about objects in its surrounding. Our approach to the problem, is to project the point clouds into 2D virtual images using the Katz projection. Then we use pre-t
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Kolhatkar, Dhanvin. "Real-Time Instance and Semantic Segmentation Using Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40616.

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In this thesis, we explore the use of Convolutional Neural Networks for semantic and instance segmentation, with a focus on studying the application of existing methods with cheaper neural networks. We modify a fast object detection architecture for the instance segmentation task, and study the concepts behind these modifications both in the simpler context of semantic segmentation and the more difficult context of instance segmentation. Various instance segmentation branch architectures are implemented in parallel with a box prediction branch, using its results to crop each instance's feature
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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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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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Granli, Petter. "Semantic segmentation of seabed sonar imagery using deep learning." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160561.

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For investigating the large parts of the ocean which have yet to be mapped, there is a need for autonomous underwater vehicles. Current state-of-the-art underwater positioning often relies on external data from other vessels or beacons. Processing seabed image data could potentially improve autonomy for underwater vehicles. In this thesis, image data from a synthetic aperture sonar (SAS) was manually segmented into two classes: sand and gravel. Two different convolutional neural networks (CNN) were trained using different loss functions, and the results were examined. The best performing netwo
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Bou, Albert. "Deep Learning models for semantic segmentation of mammography screenings." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265652.

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This work explores the performance of state-of-the-art semantic segmentation models on mammographic imagery. It does so by comparing several reference semantic segmentation deep learning models on a newly proposed medical dataset of mammograpgy screenings. All models are re-implemented in Tensorflow and validated first on the benchmark dataset Cityscapes. The new medical image corpus was gathered and annotated at the Science for Life Laboratory in Stockholm. In addition, this master thesis shows that it is possible to boost segmentation performance by training the models in an adversarial mann
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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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Simonovsky, Martin. "Deep learning on attributed graphs." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1133/document.

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Le graphe est un concept puissant pour la représentation des relations entre des paires d'entités. Les données ayant une structure de graphes sous-jacente peuvent être trouvées dans de nombreuses disciplines, décrivant des composés chimiques, des surfaces des modèles tridimensionnels, des interactions sociales ou des bases de connaissance, pour n'en nommer que quelques-unes. L'apprentissage profond (DL) a accompli des avancées significatives dans une variété de tâches d'apprentissage automatique au cours des dernières années, particulièrement lorsque les données sont structurées sur une grille
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von, Koch Christian, and William Anzén. "Detecting Slag Formation with Deep Learning Methods : An experimental study of different deep learning image segmentation models." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177269.

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Image segmentation through neural networks and deep learning have, in the recent decade, become a successful tool for automated decision-making. For Luossavaara-Kiirunavaara Aktiebolag (LKAB), this means identifying the amount of slag inside a furnace through computer vision.  There are many prominent convolutional neural network architectures in the literature, and this thesis explores two: a modified U-Net and the PSPNet. The architectures were combined with three loss functions and three class weighting schemes resulting in 18 model configurations that were evaluated and compared. This thes
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He, Haoyu. "Deep learning based human parsing." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/24262.

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Human parsing, or human body part semantic segmentation, has been an active research topic due to its wide potential applications. Although prior works have made significant progress by introducing large-scale datasets and deep learning to solve the problem, there are still two challenges remain unsolved. Firstly, to better exploit the existing parsing annotations, prior methods learn a knowledge-sharing mechanism to improve semantic structures in cross-dataset human parsing. However, the modeling for such mechanism remains inefficient for not considering classes' granularity difference in dif
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Bahl, Gaétan. "Architectures deep learning pour l'analyse d'images satellite embarquée." Thesis, Université Côte d'Azur, 2022. https://tel.archives-ouvertes.fr/tel-03789667.

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Les progrès des satellites d'observation de la Terre à haute résolution et la réduction des temps de revisite introduite par la création de constellations de satellites ont conduit à la création quotidienne de grandes quantités d'images (des centaines de Teraoctets par jour). Simultanément, la popularisation des techniques de Deep Learning a permis le développement d'architectures capables d'extraire le contenu sémantique des images. Bien que ces algorithmes nécessitent généralement l'utilisation de matériel puissant, des accélérateurs d'inférence IA de faible puissance ont récemment été dével
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La, Rosa Francesco. "A deep learning approach to bone segmentation in CT scans." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14561/.

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This thesis proposes a deep learning approach to bone segmentation in abdominal CT scans. Segmentation is a common initial step in medical images analysis, often fundamental for computer-aided detection and diagnosis systems. The extraction of bones in CT scans is a challenging task, which if done manually by experts requires a time consuming process and that has not today a broadly recognized automatic solution. The method presented is based on a convolutional neural network, inspired by the U-Net and trained end-to-end, that performs a semantic segmentation of the data. The training dataset
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Torrents, Barrena Jordina. "Deep learning -based segmentation methods for computer-assisted fetal surgery." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/668188.

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This thesis focuses on the development of deep learning-based image processing techniques for the detection and segmentation of fetal structures in magnetic resonance imaging (MRI) and 3D ultrasound (US) images of singleton and twin pregnancies. Special attention is laid on monochorionic twins affected by the twin-to-twin transfusion syndrome (TTTS). In this context, we propose the first TTTS fetal surgery planning and simulation platform. Different approaches are utilized to automatically segment the mother’s soft tissue, uterus, placenta, its peripheral blood vessels, and umbilical cord from
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Carrizo, Gabriel. "Organ Segmentation Using Deep Multi-task Learning with Anatomical Landmarks." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-241640.

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This master thesis is the study of multi-task learning to train a neural network to segment medical images and predict anatomical landmarks. The paper shows the results from experiments using medical landmarks in order to attempt to help the network learn the important organ structures quicker. The results found in this study are inconclusive and rather than showing the efficiency of the multi-task framework for learning, they tell a story of the importance of choosing the tasks and dataset wisely. The study also reflects and depicts the general difficulties and pitfalls of performing a projec
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Wan, Fengkai. "Deep Learning Method used in Skin Lesions Segmentation and Classification." Thesis, KTH, Medicinsk teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233467.

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Malignant melanoma (MM) is a type of skin cancer that is associated with a very poor prognosis and can often lead to death. Early detection is crucial in order to administer the right treatment successfully but currently requires the expertise of a dermatologist. In the past years, studies have shown that automatic detection of MM is possible through computer vision and machine learning methods. Skin lesion segmentation and classification are the key methods in supporting automatic detection of different skin lesions. Compared with traditional computer vision as well as other machine learning
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Kobold, Jonathan. "Deep Learning for lesion and thrombus segmentation from cerebral MRI." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLE044.

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L'apprentissage profond est le meilleur ensemble de méthodes aumonde pour identifier des objets sur des images. L'accident vascu-laire cérébral est une maladie mortelle dont le traitement nécessitel'identification d'objets par imagerie médicale. Cela semble être unecombinaison évidente, mais il n'est pas anodin de joindre les deux.La segmentation de la lésion de l'IRM cérébrale a retenu l'attentiondes chercheurs, mais la segmentation du thrombus est encore inex-plorée. Ce travail montre que les architectures de réseau de neur-ones convolutionnels contemporaines ne peuvent pas ident
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Holmberg, Joakim. "Targeting the zebrafish eye using deep learning-based image segmentation." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-428325.

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Researchers studying cardiovascular and metabolic disease in humans commonly usecomputer vision techniques to segment internal structures of the zebrafish animalmodel. However, there are no current image segmentation methods to target theeyes of the zebrafish. Segmenting the eyes is essential for accurate measurement ofthe eyes' size and shape following the experimental intervention. Additionally,successful segmentation of the eyes functions as a good starting point for futuresegmentation of other internal organs. To establish an effective segmentation method,the deep learning neural network a
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Westermark, Hanna. "Deep Learning with Importance Sampling for Brain Tumor MR Segmentation." Thesis, KTH, Optimeringslära och systemteori, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289574.

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Segmentation of magnetic resonance images is an important part of planning radiotherapy treat-ments for patients with brain tumours but due to the number of images contained within a scan and the level of detail required, manual segmentation is a time consuming task. Convolutional neural networks have been proposed as tools for automated segmentation and shown promising results. However, the data sets used for training these deep learning models are often imbalanced and contain data that does not contribute to the performance of the model. By carefully selecting which data to train on, there i
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Makaroff, Nicolas. "Segmentation by deep learning with geometric constraints and active contours." Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLD030.

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La segmentation des images médicales est une tâche critique dans la pratique clinique, nécessitant des méthodes précises et fiables pour aider au diagnostic et à la planification du traitement. Cependant, les approches d'apprentissage profond existantes manquent souvent d'interprétabilité et de robustesse, ce qui limite leur application dans des environnements cliniques sensibles. Cette thèse aborde ces défis en proposant deux nouveaux modèles d'apprentissage profond qui intègrent des techniques classiques de traitement d'images pour améliorer la performance et la fiabilité de la segmentation.
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Wu, Xinheng. "A Deep Unsupervised Anomaly Detection Model for Automated Tumor Segmentation." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22502.

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Many researches have been investigated to provide the computer aided diagnosis (CAD) automated tumor segmentation in various medical images, e.g., magnetic resonance (MR), computed tomography (CT) and positron-emission tomography (PET). The recent advances in automated tumor segmentation have been achieved by supervised deep learning (DL) methods trained on large labelled data to cover tumor variations. However, there is a scarcity in such training data due to the cost of labeling process. Thus, with insufficient training data, supervised DL methods have difficulty in generating effective feat
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Gammulle, Pranali Harshala. "Deep learning for human action understanding." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/135199/1/Pranali_Gammulle_Thesis.pdf.

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This thesis addresses the problem of understanding human behaviour in videos in multiple problem settings including, recognition, segmentation, and prediction. Considering the complex nature of human behaviour, we propose to capture both short-term and long-term context in the given videos and propose novel multitask learning-based approaches to solve the action prediction task, as well as an adversarially-trained approach to action recognition. We demonstrate the efficacy of these techniques by applying them to multiple real-world human behaviour understanding settings including, security sur
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Gujar, Sanket. "Pointwise and Instance Segmentation for 3D Point Cloud." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1290.

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The camera is the cheapest and computationally real-time option for detecting or segmenting the environment for an autonomous vehicle, but it does not provide the depth information and is undoubtedly not reliable during the night, bad weather, and tunnel flash outs. The risk of an accident gets higher for autonomous cars when driven by a camera in such situations. The industry has been relying on LiDAR for the past decade to solve this problem and focus on depth information of the environment, but LiDAR also has its shortcoming. The industry methods commonly use projections methods to create
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Kim, Max. "Improving Knee Cartilage Segmentation using Deep Learning-based Super-Resolution Methods." Thesis, KTH, Medicinteknik och hälsosystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297900.

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Segmentation of the knee cartilage is an important step for surgery planning and manufacturing patient-specific prostheses. What has been a promising technology in recent years is deep learning-based super-resolution methods that are composed of feed-forward models which have been successfully applied on natural and medical images. This thesis aims to test the feasibility to super-resolve thick slice 2D sequence acquisitions and acquire sufficient segmentation accuracy of the articular cartilage in the knee. The investigated approaches are single- and multi-contrast super-resolution, where the
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Kamann, Christoph [Verfasser], and Carsten [Akademischer Betreuer] Rother. "Robust Semantic Segmentation with Deep Learning / Christoph Kamann ; Betreuer: Carsten Rother." Heidelberg : Universitätsbibliothek Heidelberg, 2021. http://d-nb.info/123647483X/34.

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Dickens, James. "Depth-Aware Deep Learning Networks for Object Detection and Image Segmentation." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42619.

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The rise of convolutional neural networks (CNNs) in the context of computer vision has occurred in tandem with the advancement of depth sensing technology. Depth cameras are capable of yielding two-dimensional arrays storing at each pixel the distance from objects and surfaces in a scene from a given sensor, aligned with a regular color image, obtaining so-called RGBD images. Inspired by prior models in the literature, this work develops a suite of RGBD CNN models to tackle the challenging tasks of object detection, instance segmentation, and semantic segmentation. Prominent architectur
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Shah, Abhay. "Multiple surface segmentation using novel deep learning and graph based methods." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5630.

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The task of automatically segmenting 3-D surfaces representing object boundaries is important in quantitative analysis of volumetric images, which plays a vital role in numerous biomedical applications. For the diagnosis and management of disease, segmentation of images of organs and tissues is a crucial step for the quantification of medical images. Segmentation finds the boundaries or, limited to the 3-D case, the surfaces, that separate regions, tissues or areas of an image, and it is essential that these boundaries approximate the true boundary, typically by human experts, as closely as po
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Ciocarlan, Alina. "Small target detection using deep learning." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG102.

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La détection de petits objets dans les images infrarouges (IR) est une tâche complexe mais cruciale en défense, surtout lorsqu'il s'agit de distinguer ces cibles d'un fond texturé. Les méthodes de détection d'objets classiques peinent à trouver un équilibre entre un taux de détection élevé et un faible taux de fausses alarmes. Bien que certaines approches aient amélioré les réponses des cartes de caractéristiques pour les petits objets, elles restent tout de même sensibles aux fausses alarmes induites par les éléments du fond. Pour résoudre ce problème, la première partie de cette thèse introd
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BINDER, THOMAS. "Gland Segmentation with Convolutional Neural Networks : Validity of Stroma Segmentation as a General Approach." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-246134.

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The analysis of glandular morphology within histopathology images is a crucial step in determining the stage of cancer. Manual annotation is a very laborious task. It is time consuming and suffers from the subjectivity of the specialists that label the glands. One of the aims of computational pathology is developing tools to automate gland segmentation. Such an algorithm would improve the efficiency of cancer diag- nosis. This is a complex task as there is a large variability in glandular morphologies and staining techniques. So far, specialised models have given promising results focusing on
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Janurberg, Norman, and Christian Luksitch. "Exploring Deep Learning Frameworks for Multiclass Segmentation of 4D Cardiac Computed Tomography." Thesis, Linköpings universitet, Institutionen för hälsa, medicin och vård, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178648.

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By combining computed tomography data with computational fluid dynamics, the cardiac hemodynamics of a patient can be assessed for diagnosis and treatment of cardiac disease. The advantage of computed tomography over other medical imaging modalities is its capability of producing detailed high resolution images containing geometric measurements relevant to the simulation of cardiac blood flow. To extract these geometries from computed tomography data, segmentation of 4D cardiac computed tomography (CT) data has been performed using two deep learning frameworks that combine methods which have p
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MATRONE, FRANCESCA. "Deep Semantic Segmentation of Built Heritage Point Clouds." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2924998.

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Estgren, Martin. "Bone Fragment Segmentation Using Deep Interactive Object Selection." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157668.

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In recent years semantic segmentation models utilizing Convolutional Neural Networks (CNN) have seen significant success for multiple different segmentation problems. Models such as U-Net have produced promising results within the medical field for both regular 2D and volumetric imaging, rivalling some of the best classical segmentation methods. In this thesis we examined the possibility of using a convolutional neural network-based model to perform segmentation of discrete bone fragments in CT-volumes with segmentation-hints provided by a user. We additionally examined different classical seg
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Zhewei, Wang. "Fully Convolutional Networks (FCNs) for Medical Image Segmentation." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1605199701509179.

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Suzani, Amin. "Automatic vertebrae localization, identification, and segmentation using deep learning and statistical models." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50722.

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Automatic localization and identification of vertebrae in medical images of the spine are core requirements for building computer-aided systems for spine diagnosis. Automated algorithms for segmentation of vertebral structures can also benefit these systems for diagnosis of a range of spine pathologies. The fundamental challenges associated with the above-stated tasks arise from the repetitive nature of vertebral structures, restrictions in field of view, presence of spine pathologies or surgical implants, and poor contrast of the target structures in some imaging modalities. This thesis pres
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Agerskov, Niels. "Adaptable Semi-Automated 3D Segmentation Using Deep Learning with Spatial Slice Propagation." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-241542.

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Even with the recent advances of deep learning pushing the field of medical image analysis further than ever before, progress is still slow due to limited availability of annotated data. There are multiple reasons for this, but perhaps the most prominent one is the amount of time manual annotation of medical images takes. In this project a semi-automated algorithm is proposed, approaching the segmentation problem in a slice by slice manner utilising the prediction of a previous slice as a prior for the next. This both allows the algorithm to segment entirely new cases and gives the user the ab
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Bodin, Emanuel. "Furniture swap : Segmentation and 3D rotation of natural images using deep learning." Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-435503.

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Learning to perceive scenes and objects from 2D images as 3D models is atrivial task for a human but very challenging for a computer. Being ableto retrieve a 3D model from a scene just by taking a picture of it canbe of great use in many fields, for example when making 3D blueprintsfor buildings or working with animations in the game or film industry.Novel view synthesis is a field within deep learning where generativemodels are trained to construct 3D models of scenes or objects from 2Dimages. In this work, the generative model HoloGAN is combined together with aU-net segmentation network. Th
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ASLANI, SHAHAB. "Deep learning approaches for segmentation of multiple sclerosis lesions on brain MRI." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/997626.

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Multiple Sclerosis (MS) is a demyelinating disease of the central nervous system which causes lesions in brain tissues, especially visible in white matter with magnetic resonance imaging (MRI). The diagnosis of MS lesions, which is often performed visually with MRI, is an important task as it can help characterizing the progression of the disease and monitoring the efficacy of a candidate treatment. automatic detection and segmentation of MS lesions from MRI images offer the potential for a faster and more cost-effective performance which could also be immune to expert bias segmentation. In t
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Sarkaar, Ajit Bhikamsingh. "Addressing Occlusion in Panoptic Segmentation." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/101988.

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Visual recognition tasks have witnessed vast improvements in performance since the advent of deep learning. Despite the gains in performance, image understanding algorithms are still not completely robust to partial occlusion. In this work, we propose a novel object classification method based on compositional modeling and explore its effect in the context of the newly introduced panoptic segmentation task. The panoptic segmentation task combines both semantic and instance segmentation to perform labelling of the entire image. The novel classification method replaces the object detection pipel
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Wang, Zhewei. "Laplacian Pyramid FCN for Robust Follicle Segmentation." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1565620740447982.

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Mali, Shruti Atul. "Multi-Modal Learning for Abdominal Organ Segmentation." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285866.

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Deep Learning techniques are widely used across various medical imaging applications. However, they are often fine-tuned for a specific modality and are not generalizable when it comes to new modalities or datasets. One of the main reasons for this is large data variations for e.g., the dynamic range of intensity values is large across multi-modal images. The goal of the project is to develop a method to address multi-modal learning that aims at segmenting liver from Computed Tomography (CT) images and abdominal organs from Magnetic Resonance (MR) images using deep learning techniques. In this
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Havaei, Seyed Mohammad. "Machine learning methods for brain tumor segmentation." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10260.

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Abstract : Malignant brain tumors are the second leading cause of cancer related deaths in children under 20. There are nearly 700,000 people in the U.S. living with a brain tumor and 17,000 people are likely to loose their lives due to primary malignant and central nervous system brain tumor every year. To identify whether a patient is diagnosed with brain tumor in a non-invasive way, an MRI scan of the brain is acquired followed by a manual examination of the scan by an expert who looks for lesions (i.e. cluster of cells which deviate from healthy tissue). For treatment purposes, the tumor
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Kushibar, Kaisar. "Automatic segmentation of brain structures in magnetic resonance images using deep learning techniques." Doctoral thesis, Universitat de Girona, 2020. http://hdl.handle.net/10803/670766.

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This PhD thesis focuses on the development of deep learning based methods for accurate segmentation of the sub-cortical brain structures from MRI. First, we have proposed a 2.5D CNN architecture that combines convolutional and 2/2 spatial features. Second, we proposed a supervised domain adaptation technique to improve the robustness and consistency of deep learning model. Third, an unsupervised domain adaptation method was proposed to eliminate the requirement of manual intervention to train a deep learning model that is robust to differences in the MRI images from multi-centre and mu
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Serra, Sabina. "Deep Learning for Semantic Segmentation of 3D Point Clouds from an Airborne LiDAR." Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-168367.

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Light Detection and Ranging (LiDAR) sensors have many different application areas, from revealing archaeological structures to aiding navigation of vehicles. However, it is challenging to interpret and fully use the vast amount of unstructured data that LiDARs collect. Automatic classification of LiDAR data would ease the utilization, whether it is for examining structures or aiding vehicles. In recent years, there have been many advances in deep learning for semantic segmentation of automotive LiDAR data, but there is less research on aerial LiDAR data. This thesis investigates the current st
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Rönnberg, Axel. "Semi-Supervised Deep Learning using Consistency-Based Methods for Segmentation of Medical Images." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279579.

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In radiation therapy, a form of cancer treatment, accurately locating the anatomical structures is required in order to limit the impact on healthy cells. The automatic task of delineating these structures and organs is called segmentation, where each pixel in an image is classified and assigned a label. Recently, deep neural networks have proven to be efficient at automatic medical segmentation. However, deep learning requires large amounts of training data. This is a restricting feature, especially in the medical field due to factors such as patient confidentiality. Nonetheless, the main cha
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