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Social and Behavioral Sciences Commons™
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- ARTIFICIAL INTELLIGENCE (1)
- Alignment (1)
- BRAIN (1)
- COMPUTER SCIENCE (1)
- Conflation (1)
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- DATA (1)
- DEEP LEARNING (1)
- ELECTROENCEPHALOGRAM CLASSIFICATION (1)
- Feature extraction (1)
- GMTI (1)
- Genetic Algorithm (1)
- Guidance (1)
- Image (1)
- MACHINE LEARNING (1)
- Multi-objective optimization (1)
- NEURO SCIENCE (1)
- ROBOTICS (1)
- Registration (1)
- Road extraction (1)
- SOCIOLOGY (1)
- Trail extraction (1)
- Vector data (1)
Articles 1 - 3 of 3
Full-Text Articles in Social and Behavioral Sciences
Electroencephalogram Classification Of Brain States Using Deep Learning Approach, Hrishitva Patel
Electroencephalogram Classification Of Brain States Using Deep Learning Approach, Hrishitva Patel
Computer Science Faculty Scholarship
The oldest diagnostic method in the field of neurology is electroencephalography (EEG). To grasp the information contained in EEG signals, numerous deep machine learning architectures have been developed recently. In brain computer interface (BCI) systems, classification is crucial. Many recent studies have effectively employed deep learning algorithms to learn features and classify various sorts of data. A systematic review of EEG classification using deep learning was conducted in this research, resulting in 90 studies being discovered from the Web of Science and PubMed databases. Researchers looked at a variety of factors in these studies, including the task type, EEG pre-processing …
Guidance In Feature Extraction To Resolve Uncertainty, Boris Kovalerchuk, Michael Kovalerchuk, Simon Streltsov, Matthew Best
Guidance In Feature Extraction To Resolve Uncertainty, Boris Kovalerchuk, Michael Kovalerchuk, Simon Streltsov, Matthew Best
Computer Science Faculty Scholarship
Automated Feature Extraction (AFE) plays a critical role in image understanding. Often the imagery analysts extract features better than AFE algorithms do, because analysts use additional information. The extraction and processing of this information can be more complex than the original AFE task, and that leads to the “complexity trap”. This can happen when the shadow from the buildings guides the extraction of buildings and roads. This work proposes an AFE algorithm to extract roads and trails by using the GMTI/GPS tracking information and older inaccurate maps of roads and trails as AFE guides.
Automated Vector-To-Raster Image Registration, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Robert Brigantic, Michael Kovalerchuk, Brian Graff
Automated Vector-To-Raster Image Registration, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Robert Brigantic, Michael Kovalerchuk, Brian Graff
Computer Science Faculty Scholarship
The variability of panchromatic and multispectral images, vector data (maps) and DEM models is growing. Accordingly, the requests and challenges are growing to correlate, match, co-register, and fuse them. Data to be integrated may have inaccurate and contradictory geo-references or not have them at all. Alignment of vector (feature) and raster (image) geospatial data is a difficult and time-consuming process when transformational relationships between the two are nonlinear. The robust solutions and commercial software products that address current challenges do not yet exist. In the proposed approach for Vector-to-Raster Registration (VRR) the candidate features are auto-extracted from imagery, vectorized, and …