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For automated data exploration applied to space plasma remote sensing data by.
| Content Provider | CiteSeerX |
|---|---|
| Author | Grinstein, Georges G. Galkin, Ivan Andreevich |
| Abstract | As research instruments of large information capacities become a reality, automated systems for intelligent data analysis become a necessity. Scientific archives containing huge volumes of data preclude manual manipulation or intervention and require automated exploration and mining that can at least pre-classify information in categories. The large dataset from the radio plasma imager (RPI) instrument onboard the IMAGE satellite shows a critical need for such exploration in order to identify and archive features of interest in the volumes of visual information. In this research we have developed such a pre-classifier through a model of pre-attentive vision capable of detecting and extracting traces of echoes from the RPI plasmagrams. The overall design of our model complies with Marr’s paradigm of vision where elements of increasing perceptual strength are built bottom up under the Gestalt constraints of good continuation and smoothness. The specifics of the RPI data, however, demanded extension of this paradigm to achieve greater robustness for signature analysis. Our pre-attentive model now employs a feedback neural network that refines alignment |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Good Continuation Visual Information Large Information Feedback Neural Network Perceptual Strength Pre-attentive Vision Capable Rpi Data Intelligent Data Analysis Research Instrument Model Complies Archive Feature Critical Need Image Satellite Large Dataset Pre-classify Information Pre-attentive Model Huge Volume Signature Analysis Gestalt Constraint Manual Manipulation Overall Design Radio Plasma Imager Rpi Plasmagrams |
| Content Type | Text |
