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Gökçe, Tolga (author)An algal bloom is defined as a rapid increase in common algae (phytoplankton) abundance in water bodies and it can occur when a group of certain environmental factors is combined. If the algae populations grow out of control, such algal blooms become problematic and cause damage to the ecosystem, such phenomena are called harmful algal blooms....bachelor thesis 2023
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Bayraktar, Kerem (author)The term ”Algal Bloom” refers to the accumulation of algae in a confined geological space. They may harm human health and negatively affect ecological systems around the area. Thus, forecasting algal blooms could mitigate the environmental and socio-economical damages. Particularly, the use of deep learning methods could distinguish underlying...bachelor thesis 2023
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Alvarez Lucendo, Rodrigo (author)Forecasting algal blooms using remote sensing data is less labour-intensive and has better cover- age in time and space than direct water sampling. The paper implements a deep learning technique, the UNet Architecture, to predict the chlorophyll concentration, which is a good indicator for al- gal bloom in the Rio Negro water reservoirs of...bachelor thesis 2023
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Lubbers, Rob (author)The aim of this paper is to find out which Machine Learning (ML) model predicts the concentration of Chlorophyll-a, in the Palmar lake in Uruguay best. Currently there are no such models to predict the growth in this lake. The algorithms which will be compared in this paper are a Linear Regression model and the U-Net model. We will compare the...bachelor thesis 2023
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de Gruyl, Einar (author)This research presents a method for forecasting algal blooms using remote sensing with spatially and temporally sparse satellite data. The method involves the use of multiple interpolation methods to interpolate the sparse input data. The approach is shown to be effective in predicting algal blooms in areas where data is sparse, and the results...bachelor thesis 2023
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Tahur, Nishad (author)Color information has been shown to provide useful information during image classification. Yet current popular deep convolutional neural networks use 2-dimensional convolutional layers. The first 2-dimensional convolutional layer in the network combines the color channels of the input images, which produces feature maps per channel with only...master thesis 2022
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Streefkerk, Thomas (author)CycleGANs [1] and CIConv [2] are both relatively new approaches to their respective applications. For CycleGANs this application is unpaired image-to-image domain adaptation and for CIConv this application is making images more<br/>robust to illumination changes. We investigate whether CycleGANs in combination with CIConv can be used to improve...bachelor thesis 2022
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Gioia, Gianpaolo (author)The possibility to improve an existing method by making (part of) it learnable is explored in this research. The work that this research extends added prior knowledge to a Convolutional Neural Network (CNN) to improve its performance when dealing with an illumination shift. The method used for the preprocessing, is the color invariant. The...bachelor thesis 2022
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Brouwer, Gees (author)While deep neural networks show great potential for being part of safety-critical applications such as autonomous driving, covering their sensitivity to illumination shifts by adding training data is of- ten non-trivial. The undesired illumination shift between train and test data can be addressed by domain adaptation methods. Recent work [9]...bachelor thesis 2022
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Ju, Nicky (author)Color Invariant Convolution (CIConv) is a learnable Convolutional Neural Network (CNN) layer that reduces the distribution shift between the source and target set in the CNN under an illumination-based domain shift. We explore the semantic segmentation performance for daynight domain adaptation when using CIConv. We will test this on two...bachelor thesis 2022
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Dimitrov, Yordan (author)In this paper we analyze the performance of a novel clustering objective that optimizes a neural network to predict segmentation. We challenge the reported results by replicating the original experiments and conducting additional tests to gain an insight into the algorithm. We analyzed the efficiency of the clustering objective on a different...master thesis 2021
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Das, Tuhin (author)To alleviate lower classification performance on rare classes in imbalanced datasets, a possible solution is to augment the underrepresented classes with synthetic samples. Domain adaptation can be incorporated in a classifier to decrease the domain discrepancy between real and synthetic samples. While domain adaptation is...bachelor thesis 2021
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Chen, Ivo (author)Wheat is a widely used ingredient for food products. To increase the productionand quality of wheat, the density of ’wheat heads’ in a farm can be studied. Accuratelylocating wheat heads in images can be challenging. A lot of work has taken place insupervised semantic segmentation, but these networks typically require large pixel-wisehuman...bachelor thesis 2021
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Ulev, Petar (author)This research paper analyses the effect that using frequency information can have on object detectors. The latter are complex networks that learn information about objects from images and are then able to predict the location of these objects in new, unseen images. There are, however, certain datasets that are hard to learn on, partly because...bachelor thesis 2021
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Dorrestijn, Eljo (author)In the field of ecology, camera traps are important tools to collect information on the wildlife of certain areas. The problem that arises with many camera traps is that they can collect more images than a human can realistically go trough all by themselves. To help classify these images computer vision is proposed as an alternative to manual...bachelor thesis 2021
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Lanzini, Edoardo (author)The natural world is long-tailed: rare classes are observed orders of magnitudes less frequently than common ones, leading to highly-imbalanced data where rare classes can have only handfuls of examples. Learning from few examples is a known challenge for deep learning based classification algorithms, and is the focus of the field of low-shot...bachelor thesis 2021
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Oosterbaan, Justin (author)Camera traps are used around the world to provide data on species, population sizes and how species are interacting. However this creates a lot of work in identifying which animal was actually spotted near the camera. Attempts have been made to use deep-learning to identify animals and work correctly for animals which are not rare but the lack...bachelor thesis 2021
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Cian, David (author)In this paper, we run two methods of explanation, namely LIME and Grad-CAM, on a convolutional neural network trained to label images with the LEGO bricks that are visible in them. We evaluate them on two criteria, the improvement of the network's core performance and the trust they are able to generate for users of the system. We nd that in...bachelor thesis 2020
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Abderrazik, Hiba (author)The benchmarks for the accuracy of the best performing object detectors to date are usually based on homogeneous datasets, including objects such as vehicles, people, animals and foods. This excludes a whole set of scenarios containing small, cluttered and rotated objects. This paper selects a state-of-the-art object detection model, Faster R...bachelor thesis 2020
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Kam, Berend (author)Data collection and annotation have proven to be a bottleneck for computer vision applications. When faced with the task of data creation, alternative methods to traditional data collection should be considered, as time and cost may be reduced signif- icantly. We introduce three novel datasets for multi- label classification purposes on LEGO...bachelor thesis 2020