Document Type
Thesis
Date of Award
5-31-1991
Degree Name
Master of Science in Computer and Information Science - (M.S.)
Department
Computer and Information Science
First Advisor
Frank Y. Shih
Abstract
Neural network can be applied on the image enhancement after adding another two layers into the Adaptive Resonance Theory architectures (ART 1). The analysis for selecting a nice training pattern set associate the appropriate vigilance values is the main concerns in this thesis. For a single training pattern ,the network can act as a mathematical morphology operators such as erosion , dilation, opening and closing. With more than one training patterns in the network, 16 experiments are tested and are compared to each other in order to find the best selection for doing the image enhancement work. With both the training pattern set and vigilance values fixed, the first iteration always show the best performance than the other iterations. The comparison between 16 experiments explores the criteria for selecting the better training pattern set. Increasing the local features in the network training and a little bit "flexibility" of the vigilance parameter can increase the performance of the image enhancement. With the vigilance changed in different iterations also can be the good way to choose the training pattern set.
Recommended Citation
Chang, Fu-Chun, "The performance of training pattern sets in a new ART-based neural architecture for image enhancement" (1991). Theses. 2425.
https://digitalcommons.njit.edu/theses/2425
