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Basting, Mark (author)
In real-life scenarios, there are many variations in sizes of objects of the same category and the objects are not always placed at a fixed distance from the camera. This results in objects taking up an arbitrary size of pixels in the image. Vanilla CNNs are by design only translation equivariant and thus have to learn separate filters for...
master thesis 2023
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Haarman, Luuk (author)
Convolutional Neural Networks (CNNs) benefit from fine-grained details in high-resolution images, but these images are not always easily available as data collection can be expensive or time-consuming. Transfer learning pre-trains models on data from a related domain before fine-tuning on the main domain, and is a common strategy to deal with...
master thesis 2023
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Murgoci, Vlad (author)
This study investigates the relationship between deep learning models and the human brain, specifically focusing on the prediction of brain activity in response to static visual stimuli using functional magnetic resonance imaging (fMRI). By leveraging intermediate outputs of pre-trained convolutional neural networks (CNNs) with feature-weighted...
bachelor thesis 2023
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Yang, Wei-Tse (author)
We present the first deep learning approach to estimate the human skeletal system of the musculoskeletal model from monocular video. The current practice of musculoskeletal modeling relies on a motion capture system and OpenSim. The data is recorded in a restricted environment, and OpenSim workflow for musculoskeletal modeling is costly. Our...
master thesis 2021
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Saldanha, Nikhil (author)
A structured CNN filter basis allows incorporating priors about natural image statistics and thus require less training examples to learn, saving valuable annotation time. Here, we build on the Gaussian derivative CNN filter basis that learn both the orientation and scale of the filters. However, this Gaussian filter basis definition depends on...
master thesis 2021
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Biesheuvel, Julian (author)
Yes, convolutional neural networks are domain-invariant, albeit to some limited extent. We explored the performance impact of domain shift for convolutional neural networks. We did this by designing new synthetic tasks, for which the network’s task was to map images to their mean, median, standard deviation, and variance pixel intensities. We...
bachelor thesis 2021
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Cuperman Coifman, Rafael (author)
been given to Human Activity Recognition (HAR) based on signals obtained by IMUs placed on different body parts. This thesis studies the usage of Deep Learning-based models to recognize different football activities in an accurate, robust, and fast manner. Several deep architectures were trained with data captured with IMU sensors placed on...
master thesis 2021
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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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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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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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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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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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Kroes, Mairin (author)
Convolutional Neural Network (CNN) inference has gained a significant amount of traction for performing tasks like speech recognition and image classification. To improve the accuracy with which these tasks can be performed, CNNs are typically designed to be deep, encompassing a large number of neural network layers. As a result, the...
master thesis 2020
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Lelekas, Ioannis (author)
Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and preferential processing given relevant stimuli. On the contrary, CNNs employ a fine-to-coarse processing, moving from local, edge-detecting filters to more global ones...
master thesis 2020
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Tubbing, Rico (author)
A side-channel attack (SCA) recovers secret data from a device by exploiting unintended physical leakages such as power consumption. In a profiled SCA, we assume an adversary has control over a target and copy device. Using the copy device the adversary learns a profile of the device. With the profile, the adversary exploits the measurements...
master thesis 2019
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Wiersma, Ruben (author)
We present a new approach for deep learning on surfaces, combining geometric convolutional networks with rotationally equivariant networks. Existing work either learns rotationally invariant filters, or learns filters in the tangent plane without correctly relating orientations between different tangent planes (orientation ambiguity). We propose...
master thesis 2019
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Riegger, Franzi (author)
Quantitative analysis of material microstructure is a well-known method to derive chemical and physical properties of a sample. This includes the segmentation of e.g. Light Optical Microscopy or Scanning Electron Microscopy images where each pixel is assigned to a material. Since some phases such as the γ-γ’ structure in nickelbased superalloys...
master thesis 2019
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Chen, Joe (author)
For the real-world face recognition, factors such as occlusion and pose-variant (cross face) would affect the identification/verification performance. In addition, large number of classes also increase the complexity, which makes verification/identification even harder. In order to deal with these issues, how to extract discriminative embeddings...
master thesis 2019
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Bormans, Robbert (author)
Robot Care Systems (RCS) is involved in the development of the WEpod, an autonomous shuttle which can transfer up to six people. Based on a predefined map of the environment, the shuttle is able to navigate through mixed traffic its perception sensors such as camera, radar and lidar sensors. This study is acquired in collaboration with RCS and...
master thesis 2018
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Dhar, Aniket (author)
Convolutional neural networks are showing incredible performance in image classification, segmentation, object detection and other computer vision applications in recent years. But they lack understanding of affine transformations to input data. In this work, we introduce rotational invariant<br/>convolutional neural networks that learn...
master thesis 2018
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