Understanding Ocean Surface Temperature Features
Spasic, Nemanja and Jared Tilanus (2005) Understanding Ocean Surface Temperature Features. Technical Report CS05-07-00, Department of Computer Science, University of Cape Town.
The aim of this project was to develop a prediction system that uses Artificial Intelligence, machine learning using training data and Image Processing (AI) to extract training data from Sea Surface temperature (SST) images to predict the ocean surface, temperature features around the coast of the Southern African region.
Region growing and histographic algorithms were used in the image processing section to extract thermal fronts as training data from the available SST images. A Temporal Bayesian Network was developed as the prediction model which used approximate stochastic learning and inference algorithms based on the Maximum Likelihood Algorithm (MLE). User-Centered Design (UCD) and Human-Computer Interaction (HCI) methods were used to develop user-friendly and easy to understand Graphical User Interfaces (GUI).
Results and evaluations of the project revealed that a generally successful prototype implementation of a prediction system that used AI, machine learning and image processing was developed.
|EPrint Type:||Departmental Technical Report|
|Keywords:||Ocean Surface Temperature Feature Prediction,Image Processing, Region Growing Algorithm, Histographic Algorithm, Bayesian Nwetworks, Temporal Bayesian Networks, Approximate Learning and Inference, Poisson Distribution|
|Subjects:||H Information Systems: H.5 INFORMATION INTERFACES AND PRESENTATION|
I Computing Methodologies: I.4 IMAGE PROCESSING AND COMPUTER VISION
I Computing Methodologies: I.5 PATTERN RECOGNITION
G Mathematics of Computing: G.3 PROBABILITY AND STATISTICS
H Information Systems: H.2 DATABASE MANAGEMENT
I Computing Methodologies: I.2 ARTIFICIAL INTELLIGENCE
|Deposited By:||Spasic, Nemanja|
|Deposited On:||21 October 2005|