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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Flow Adaptive Video Object Segmentation, Fanqing Lin
Flow Adaptive Video Object Segmentation, Fanqing Lin
Theses and Dissertations
We tackle the task of semi-supervised video object segmentation, i.e, pixel-level object classification of the images in video sequences using very limited ground truth training data of its corresponding video. Recently introduced online adaptation of convolutional neural networks for video object segmentation (OnAVOS) has achieved good results by pretraining the network, fine-tuning on the first frame and training the network at test time using its approximate prediction as newly obtained ground truth. We propose Flow Adaptive Video Object Segmentation (FAVOS) that refines the generated adaptive ground truth for online updates and utilizes temporal consistency between video frames with the help …
Toward Real-Time Flip Fluid Simulation Through Machine Learning Approximations, Javid Kennon Pack
Toward Real-Time Flip Fluid Simulation Through Machine Learning Approximations, Javid Kennon Pack
Theses and Dissertations
Fluids in computer generated imagery can add an impressive amount of realism to a scene, but are particularly time-consuming to simulate. In an attempt to run fluid simulations in real-time, recent efforts have attempted to simulate fluids by using machine learning techniques to approximate the movement of fluids. We explore utilizing machine learning to simulate fluids while also integrating the Fluid-Implicit-Particle (FLIP) simulation method into machine learning fluid simulation approaches.
Using Machine Learning To Accurately Predict Ambient Soundscapes From Limited Data Sets, Katrina Lynn Pedersen
Using Machine Learning To Accurately Predict Ambient Soundscapes From Limited Data Sets, Katrina Lynn Pedersen
Theses and Dissertations
The ability to accurately characterize the soundscape, or combination of sounds, of diverse geographic areas has many practical implications. Interested parties include the United States military and the National Park Service, but applications also exist in areas such as public health, ecology, community and social justice noise analyses, and real estate. I use an ensemble of machine learning models to predict ambient sound levels throughout the contiguous United States. Our data set consists of 607 training sites, where various acoustic metrics, such as overall daytime L50 levels and one-third octave frequency band levels, have been obtained. I have data for …
Using Aviris And Machine Learning To Map And Discriminate Bull Kelp And Giant Kelp Along The Pacific Coast Of The United States, Tanner Thompson, Dr. Ryan Jensen
Using Aviris And Machine Learning To Map And Discriminate Bull Kelp And Giant Kelp Along The Pacific Coast Of The United States, Tanner Thompson, Dr. Ryan Jensen
Journal of Undergraduate Research
Kelp forests provide food and shelter for many organisms, and they are an important part of coastal ecosystems throughout the world. Along the Pacific coast of the United States, kelp forests are made up of two species of kelp: bull kelp (Nereocystis Leutkana) and giant kelp (Macrocystis Pyrifera). While similar, these two species are physiologically and structurally different.