Patent ID: 6622135
Filing Date: 2003-09-16
Classification: G06K,G06T

Abstract:
A method of detecting anomalies and classifying the anomalies in categories using an artificial neural network (ANN) comprising the steps of:performing a preliminary learning phase comprising: creating a blank block, the blank block having a shape that is designed to view each set of data in its totality in a determined number of steps; stepping each blank block through each image; generating an input block derived from said blank block for each step where each input block is a binary photograph of the image at each step, each input block having a central point; storing the representative input blocks for at least a number of steps, creating a first database using the stored representative input blocks, the first database defining prototypes of a first ANN, providing a recognition phase comprising: calculating the probability of each of said prototypes belonging to defined categories; repeating at least once said blank block stepping through each set of data; using said prototypes and their associated probabilities to characterize new subsets of data, wherein each subset of data is characterized by subset probabilities, the subset probabilities selected from the group consisting of the K nearest neighbor (KNN) algorithm and a function of KNN; replacing the central point of the input blocks with the subset probabilities.