Searched for: contributor%3A%22Kayhan%2C+O.S.+%28mentor%29%22
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Mullaj, Dajt (author)
Deep convolutional neural networks (CNNs) have achieved current state-of-the-art in image denoising, but require large datasets for training. Their performance remains limited on smaller real-noise datasets. In this paper, we investigate robust deep learning denoising using transfer learning. We explore the impact of dataset sizes, CNN parameter...
master thesis 2023
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Markhorst, Thomas (author)
In this paper, we combine image denoising and classification, aiming to enhance human perception of noisy images captured by edge devices, like security cameras. Since edge devices have little computational power, we also optimize for efficiency by proposing a novel architecture that integrates the two tasks. Additionally, we alter a Neural...
master thesis 2023
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Huizer, Rick (author)
Automated imaging systems, critical in domains like medical imaging, autonomous driving, and security, experience noise from camera sensors and electronic circuits in bad or dark lighting conditions. This impacts downstream tasks, including object detection. However, an analysis of strategies combining denoising and object detection is lacking....
master thesis 2023
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Pytel, Rafal (author)
Occlusion degrades the performance of human pose estimation. In this paper, we introduce targeted keypoint and body part occlusion attacks. The effects of the attacks are systematically analyzed on the best-performing methods. In addition, we propose occlusion specific data augmentation techniques against keypoint and part attacks. Our extensive...
master thesis 2020
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Poorgholi, Soroosh (author)
Video understanding has received more attention in the past few years due to the availability of several large-scale video datasets and improvement in the computational power of computers. However, annotating large-scale video datasets are cost-intensive due to their complexity. In this work, we propose a time-efficient video annotation method...
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
Searched for: contributor%3A%22Kayhan%2C+O.S.+%28mentor%29%22
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