Searched for: subject%3A%22Image%255C%2BClassification%22
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Dong, Y. (author), Lu, Xingmin (author), Li, Ruohan (author), Song, Wei (author), van Arem, B. (author), Farah, H. (author)
The burgeoning navigation services using digital maps provide great convenience to drivers. However, there are sometimes anomalies in the lane rendering map images, which might mislead human drivers and result in unsafe driving. To accurately and effectively detect the anomalies, this paper transforms lane rendering image anomaly detection into...
poster 2024
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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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Sharma, Anirvin (author)
Image data augmentation has been regarded as a reliable and effective way to increase the data available for training. With the advent and rise of Generative AI, generative data augmentation has been shown to realize even better gains in performance for downstream tasks. However, these performance gains are often the cause of "extra information"...
master thesis 2023
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Parankusam, Taneshwar Pranav (author)
Federated Continual Learning (FCL) is a emerging field with strong roots in Image classification. However, limited research has been done on its potential in Natural Language Processing and Tabular datasets. With recent developments in A.I. with language models and the widespread use of mobile devices, it becomes relevant to consider FCL’s...
bachelor thesis 2023
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Wang, Heqi (author)
Accurate and trustworthy short-term traffic prediction is crucial in the modern world for the comfort of drivers and decision-makers as it is used to improve the performance of traffic management systems, lessen congestion, increase safety, and shorten journey times. It is possible to discover useful information for network transportation...
master thesis 2023
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Sharifi Noorian, S. (author), Qiu, S. (author), Sayin, Burcu (author), Balayn, A.M.A. (author), Gadiraju, Ujwal (author), Yang, J. (author), Bozzon, A. (author)
High-quality data plays a vital role in developing reliable image classification models. Despite that, what makes an image difficult to classify remains an unstudied topic. This paper provides a first-of-its-kind, model-agnostic characterization of image atypicality based on human understanding. We consider the setting of image classification...
conference paper 2023
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Zhou, Y. (author), Liang, M. (author), Yue, X. (author)
Large errors can be introduced in traditional acoustic emission (AE) source localization methods using extracted signal features such as arrival time difference. This issue is obvious in the case of irregular structural geometries, complex composite structure types or presence of cracks in wave travel paths. In this study, based on a novel...
journal article 2023
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Jia, T. (author), Vallendar, A.J. (author), de Vries, Rinze (author), Kapelan, Z. (author), Taormina, R. (author)
Supervised Deep Learning (DL) methods have shown promise in monitoring the floating litter in rivers and urban canals but further advancements are hard to obtain due to the limited availability of relevant labeled data. To address this challenge, researchers often utilize techniques such as transfer learning (TL) and data augmentation (DA)....
journal article 2023
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Kidiyur Sathish, Akarsh (author)
With the advent of offshore wind farms, the research into the various phenomenon that affects their performance is vast and detailed. But the effect of a particular phenomenon, atmospheric gravity waves (AGWs), on wind farm performance is limited. AGWs are oscillations of the airflow due to an imbalance in the buoyancy and gravity forces,...
master thesis 2022
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Yarally, Tim (author)
In this work, we look at the intersection of Sustainable Software Engineering and AI engineering known as Green AI. AI computing is rapidly becoming more expensive, calling for a change in design philosophy. We consider both training and inference of neural networks used for image vision; to reveal energy-efficient practices in an exploratory...
master thesis 2022
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Achilleos, Andreas (author)
With the COVID-19 pandemic testing humanity worldwide in unforeseen ways, wellbeing assessment has stepped to the foreground of individual health status. Conversational User Interfaces (CUIs) prove promising as an assessment tool, but lacking the means to retain users engaged during the process. This research aims to explore a solution...
bachelor thesis 2022
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Chen, Cong (author), Batselier, K. (author), Yu, Wenjian (author), Wong, Ngai (author)
Tensor, a multi-dimensional data structure, has been exploited recently in the machine learning community. Traditional machine learning approaches are vector- or matrix-based, and cannot handle tensorial data directly. In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it to the conventional...
journal article 2022
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Schulze Balhorn, L. (author), Gao, Q. (author), Goldstein, Dominik (author), Schweidtmann, A.M. (author)
Flowsheets are the most important building blocks to define and communicate the structure of chemical processes. Gaining access to large data sets of machine-readable chemical flowsheets could significantly enhance process synthesis through artificial intelligence. A large number of these flowsheets are publicly available in the scientific...
book chapter 2022
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Vallendar, André (author)
Plastic pollution is one of the most challenging global environmental problems. Currently, more than 1000 rivers transport approximately 80% of the plastic influx into the oceans. Naturally, more and more companies are interested in tackling this problem. One of them is Noria Sustainable Innovators, a company based in Delft (Netherlands). It is...
master thesis 2021
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Apra, Irène (author), Bachert, Carolin (author), Caceres Tocora, Camilo (author), TUFAN, ÖZGE (author), Veselý, Ondrej (author)
Led in cooperation with the company Brink, who provides management and consultation services for construction and real estate sectors, this Synthesis Project aims at automatically deriving meaningful information about buildings. More precisely, the focus is to automatically detect roof obstacles - such as dormers, chimneys, and solar panels - to...
student report 2021
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Koffas, Stefanos (author)
Deep learning has made tremendous success in the past decade. As a result, it is becoming widely deployed in various safety and security-critical applications like autonomous driving, malware detection, fingerprint identification, and financial fraud detection. It was recently shown that deep neural networks are susceptible to multiple attacks...
master thesis 2021
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Deen, Mitchell (author)
Recent years have shown a tremendous increase in the application of Artificial Intelligence to the field of radiology, often through the extraction and analysis of large numbers of quantitative features from medical images. These applications increase the demand for machine learning models to extract information from these images. To provide...
master thesis 2021
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Balayn, A.M.A. (author), SOILIS, P. (author), Lofi, C. (author), Yang, J. (author), Bozzon, A. (author)
Global interpretability is a vital requirement for image classification applications. Existing interpretability methods mainly explain a model behavior by identifying salient image patches, which require manual efforts from users to make sense of, and also do not typically support model validation with questions that investigate multiple...
conference paper 2021
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Bonhof, Stefan (author)
Performing tasks in dynamic environments is still an open challenge in robotics. To be able to perform a task reliably in such scenarios, the state of the world has to be continuously monitored. In this context, most state-of-the-art perception methods focus on the recognition and classification of individual objects. However, these methods...
master thesis 2020
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SOILIS, Panagiotis (author)
Deep learning models have achieved state-of-the-art performance on several image classification tasks over the past years. Several studies claim to approach or even surpass human-levels of performance when using such models to classify images. However, these architectures are notoriously complex, thus making their interpretation a challenge....
master thesis 2020
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