In this work, a framework for a system which will intelligently assign an edge detection filter to an image based on features taken from the image is introduced. The framework has four parts: the learning stage, image feature extraction, training filter creation, and filter selection training. Two prototypes systems of this framework are given. The learning stage for these systems is the Berkeley Segmentation Database coupled with the Baddelay Delta Metric. Feature extraction is performed using a GIST methodology which extracts color, intensity, and orientation information. The set of image features are used as the input to a single hidden layer feed forward neural network trained using back propagation. The system trains against a set of linear cellular automata filters which are determined to best solve the edge image according to the Baddelay Delta Metric. One system uses cellular automata augmented with a fuzzy rule. The systems are trained and tested against the images from the Berkeley Segmentation Database. The results from the testing indicate that systems built on this framework can perform better than standard methods of edge detection on average across many types of images.
|Commitee:||Dianat, Sohail A., Hopkins, Mark A., Saber, Eli|
|School:||Rochester Institute of Technology|
|School Location:||United States -- New York|
|Source:||MAI 54/05M(E), Masters Abstracts International|
|Subjects:||Electrical engineering, Robotics, Computer science|
|Keywords:||Cellular automata, Edge detection, Learning algorithms, Scene recognition|
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