179.art
Charles Roberson & Max Domeika
Image Recognition/Neural networks
The Adaptive Resonance Theory 2 (ART 2) neural network is used to recognize objects in a thermal image. The objects are a helicopter and an airplane. The neural network is first trained on the objects. After training is complete, the learned images are found in the scanfield image. A window corresponding to the size of the learned objects is scanned across the scanfield image and serves as input for the neural network. The neural network attempts to match the windowed image with one of the images it has learned.
The ART 2 neural network models several characteristics of organic neural processing that is not modelled in more traditional Feed Forward Neural Networks(FFNN). In brief, ART 2 neural networks offer the following advantages over traditional FFNN:
The training files consist of a thermal image of a helicopter and an airplane. The scanfile is a field of view containing other thermal views of the helicopter and airplane.
The output data consists of the confidence of a match between the learned image and the windowed field of view. In addition, each F2 neuron's output is printed. After the entire field of view is scanned the field of view with the highest confidence of being a match is output.
ANSI C
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C. W. Roberson, "Design Extensions To Adaptive Resonance Theory Neural Networks," Master's Project, Clemson University(1994).
M.J. Domeika, C.W. Roberson, E.W. Page, and G.A. Tagliarini, "Adaptive Resonance Theory 2 Neural Network Approach To Star Field Recognition," in Applications and Science of Artificial Neural Networks II, Steven K. Rogers, Dennis W. Ruck, Editors, Proc. SPIE 2760, pp. 589-596(1996).
G.A. Carpenter and S. Grossberg, "ART 2: Stable self-organization of pattern recognition codes for analog input patterns," Applied Optics, 26, pp. 4919-4930(1987).