| abstract |
An expert system and software method for image recognition optimized for the repeating patterns characteristic of organic material. The method is performed by computing parameters across a two dimensional grid of pixels (rather than a one dimensional scan) with intensity values for each pixel having precision of eight significant bits. The parameters are fed to multiple neural networks, one for each parameter, which were each trained with images showing the tissue, structure, or nucleus to be recognized and trained with images likely to be presented that do not include the material to be recognized. Each neural network then outputs a measure of similarity of the unknown material to the known material on which the network was trained. The outputs of the multiple neural networks are aggregated by an associative voting matrix. A sub-neural network is used for each identified mode of data degradation in the input data. |