Searched for: subject:"Convolutional%5C+Neural%5C+Network"
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Ooms, Eline (author)
Lacunes of presumed vascular origin (lacunes) are small lesions in the brain and are an important indicator of cerebral small vessel disease (cSVD). To gain more insight in this disease, obtaining more information about the shape, size and location of lacunes is essential. However, manual segmentation (the voxel-wise labeling of lacunes in a...
master thesis 2021
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Pereboom, Leonie (author)
The aim of this research is to build a machine learning model in order to predict the success of invasive surgical treatment on a degenerated cervical spine, based on the baseline X-ray images. The purpose of the results of this research is an application in computer-aided diagnostics and treatment planning in the field of neurosurgery. Spinal...
master thesis 2021
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Klaoudatos, Dimitrios (author)
This MSc thesis presents the development of a viewpoint optimization framework to face the problem of detecting occluded fruits in autonomous harvesting. A Deep Reinforcement Learning (DRL) algorithm is developed in order to train a robotic manipulator to navigate to occlusion-free viewpoints of the tomato-target. Two Convolutional Neural...
master thesis 2021
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Schuit, Berend (author)
master thesis 2021
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van Leeuwen, Bodine (author)
When building a convolutional neural network, many design choices have to be made. In the case of Deepfake detection, there is no readily implementable recipe that guides these choices. This research aims to work towards understanding the effects of design choices in the case of Deepfake detection, using the Python library Keras and publicly...
master thesis 2020
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Rijsdijk, Jorai (author)
Side-channel attacks (SCA), which use unintended leakage to retrieve a secret cryptographic key, have become more sophisticated over time. With the recent successes of machine learning (ML) and especially deep learning (DL) techniques against cryptographic implementations even in the presence of dedicated countermeasures, various methods have...
master thesis 2020
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den Ottelander, Tom (author)
Computer vision tasks, like supervised image classification, are effectively tackled by convolutional neural networks, provided that the architecture, which defines the structure of the network, is set correctly. Neural Architecture Search (NAS) is a relatively young and increasingly popular field that is concerned with automatically optimizing...
master thesis 2020
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Lacoa Arends, Eric (author)
The increasing penetration of weather-dependent energy sources brings additional challenges to the operation of the power system. Wind power forecasting is a valuable resource for these power operators: a tool that aids the decision-making process and facilitates risk management. On the other hand, the progress of machine learning and their...
master thesis 2020
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Barad, K.R. (author)
Autonomous vision-based navigation is a crucial element for space applications involving a potentially uncooperative target, such as proximity operations for on-orbit servicing or active debris removal. Due to low mass and power characteristics, monocular vision sensors are an attractive choice for onboard vision-based navigation systems. This...
master thesis 2020
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Snel, Koen (author)
The ongoing demand for bigger and more efficient ships pushes their designs towards the strength limits. Sometimes, ship structures are pushed beyond their limits with the possibility of significant negative economic and environmental impact or, in the worst case, impact on human life. This makes it explicitly clear why the development of...
master thesis 2020
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Pocchiari, M. (author)
Recommender Systems assist the user by suggesting items to be consumed based on the user's history. The topic of diversity in recommendation gained momentum in recent years as additional criterion besides recommendation accuracy, to improve user satisfaction. Accuracy and diversity in recommender systems coexist in a delicate trade-off due to...
master thesis 2020
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Simion-Constantinescu, Andrei (author)
This thesis presents a novel self-supervised approach of learning visual representations from videos containing human actions. Our approach tackles the complex problem of learning without the need of labeled data by exploring to what extent the ideas successfully used for images can be transferred, adapted and extended to videos for action...
master thesis 2020
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Datta, Leonid (author)
Training Convolutional Neural Network (CNN) models is difficult when there is a lack of labeled training data and no unlabeled data is available. A popular method for this is domain adaptation where the weights of a pre-trained CNN model are transferred to the problem setup. The model is pre-trained on the same task but in a different domain...
master thesis 2020
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van Driel, R.A. (author)
Solving propositional satisfiability (SAT) and constraint programming (CP) instances has been a fundamental part of a wide range of modern applications. For this reason a lot of research went into improving the efficiency of modern SAT and CP solvers. Recently much of this research has gone into exploring the possibilities of integrating machine...
master thesis 2020
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Mazzola, G. (author)
Time-varying network data are essential in several real-world applications, such as temperature forecasting and earthquake classification. Spatial and temporal dependencies characterize these data and, therefore, conventional machine learning tools often fail to learn these joint correlations from data. On the one hand, hybrid models to learn...
master thesis 2020
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Garg, Chirag (author)
3D indoor reconstruction has been an important research area in the field of computer vision and photogrammetry. While the initial techniques developed for this purpose use sensor devices and multiple images for data acquisition and extracting 3D information and representation of the scene, with the advent of deep learning techniques, there has...
master thesis 2020
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Kaniouras, Pantelis (author)
Road network maps facilitate a great number of applications in our everyday life. However, their automatic creation is a difficult task, and so far, published methodologies cannot provide reliable solutions. The common and most recent approach is to design a road detection algorithm from remote sensing imagery based on a Convolutional Neural...
master thesis 2020
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Galjaard, Jeroen (author)
The execution of multi-inference tasks on low-powered edge devices has become increasingly popular in recent years for adding value to data on-device. The focus of the optimization of such jobs has been on hardware, neural network architectures, and frameworks to reduce execution speed. However, it is yet not known how different scheduling...
bachelor thesis 2020
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Mody, Prerak (author)
The onset of delirium, a disturbance in the mental activities of a patient, can be potentially detected by understanding activities within an Intensive Care Unit (ICU) room. Such activities can be extracted by estimating human pose via a visual capture of the scene. This work uses a top-view depth camera in an ICU room to estimate pose of the...
master thesis 2020
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de Jong, David (author)
End-to-end trained Convolutional Neural Networks have led to a breakthrough in optical flow estimation. The most recent advances focus on improving the optical flow estimation by improving the architecture and setting a new benchmark on the publicly available MPI-Sintel dataset. Instead, in this article, we investigate how deep neural networks...
master thesis 2020
Searched for: subject:"Convolutional%5C+Neural%5C+Network"
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