A pre-attentive neural system for the analysis of nuclear physics experimental data
Monica Alderighi, Paolo Guazzoni, Stefano Russo, Giacomo R. Sechi, Luisa Zetta · 2000
Biological vision processes are at the basis of many studies in the image-processing field. In this context, preattentive neural networks developed by Grossberg (1987, 1994) constitute an interesting approach. Pre-attentive networks model the process in biological vision known as emergent perception. They are able to extract meaningful information from the global structure of data rather than from local relationships, yielding to a coherent and complete visual perception, also in case of noisy and incomplete images. The paper evaluates the application of Grossberg's approach to the analysis of scatter plots from nuclear physics experiments. The design and implementation of a preattentive neural system developed for this purpose are presented. Simulation results prove the effectiveness of the approach.