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Full-Text Articles in Engineering
High-Fidelity 3d Reconstruction Of Space Bodies Using Machine Learning And Neural Radiance Fields, Timothy Jacob Huber
High-Fidelity 3d Reconstruction Of Space Bodies Using Machine Learning And Neural Radiance Fields, Timothy Jacob Huber
Theses and Dissertations
In the era of burgeoning space exploration, the growing population of spacecraft heightens the inevitability of collisions. While traditional imagery remains effective for damage assessment, its lack of 3D representation of the object necessitates more advanced approaches. This research delves into cutting-edge methodologies, with a primary focus on leveraging machine learning and computer vision technologies to enhance the precision and efficiency of damage assessment by taking imagery of a space body and creating a 3D model. The study extensively investigates TransMVSNet, a neural network code employing classical computer vision techniques such as multi-vision stereo (MVS) and depth maps. This approach …
Insights Into Cellular Evolution: Temporal Deep Learning Models And Analysis For Cell Image Classification, Xinran Zhao
Insights Into Cellular Evolution: Temporal Deep Learning Models And Analysis For Cell Image Classification, Xinran Zhao
Master's Theses
Understanding the temporal evolution of cells poses a significant challenge in developmental biology. This study embarks on a comparative analysis of various machine-learning techniques to classify cell colony images across different timestamps, thereby aiming to capture dynamic transitions of cellular states. By performing Transfer Learning with state-of-the-art classification networks, we achieve high accuracy in categorizing single-timestamp images. Furthermore, this research introduces the integration of temporal models, notably LSTM (Long Short Term Memory Network), R-Transformer (Recurrent Neural Network enhanced Transformer) and ViViT (Video Vision Transformer), to undertake this classification task to verify the effectiveness of incorporating temporal features into the classification …