Neural modeling fields: Difference between revisions

Revision as of 00:36, 13 August 2008

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There are several types of models: one uniform model describing noise (it is not shown) and a variable number of blob models and parabolic models; their number, ___location, and curvature are estimated from the data. Until about stage (g) the algorithm used simple blob models, at (g) and beyond, the algorithm decided that it needs more complex parabolic models to describe the data. Iterations stopped at (h), when similarity stopped increasing.

[[Image:ExampleOfApplicationOfDynamicLogicToNoisyImage.JPG‎ |center |frame| Fig.1. Finding ‘smile’ and ‘frown’ patterns in noise, an example of dynamic logic operation: (a) true ‘smile’ and ‘frown’ patterns are shown without noise; (b) actual image available for recognition (signal is below noise, signal-to-noise ratio is between –2dB and –0.7dB); (c) an initial fuzzy blob-model, the fuzziness corresponds to uncertainty of knowledge; (d) through (m) show improved models at various iteration stages (total of 22 iterations). Between stages (d) and (e) the algorithm tried to fit the data with more than one model and decided, that it needs three blob-models to ‘understand’ the content of the data. There are several types of models: one uniform model describing noise (it is not shown) and a variable number of blob-models and parabolic models, which number, ___location, and curvature are estimated from the data. Until about stage (g) the algorithm ‘thought’ in terms of simple blob models, at (g) and beyond, the algorithm decided that it needs more complex parabolic models to describe the data. Iterations stopped at (m), when similarity (2) stopped increasing. This example is discussed in more details in (Linnehan et al 2003).]]

[[Image:ExampleOfApplicationOfDynamicLogicToNoisyImage.JPG‎ | Example of Dynami Logic Application]]

==References==