1. A method of automated object identification and classification of objects and anomalies comprising the steps of: capturing a pixel map of an image from a location containing a possible object or anomaly; decomposing the pixel map into attributed primitives by tracing around edges of the object or anomaly, the primitives comprising numerical representations for a starting place, ending place, length, left and right texture attributes, angle of deviation from previous primitive, and curvature of the edges of the object; combining adjacent primitives to form segments with width, length, number of vertices in segments and coordinates of vertices and angles between them; storing separately primitive and segment values; forming higher level descriptors with an object class from grouped segments representative of the objects and anomalies by determining a plurality of common characteristics including size, shape, average color, edge sharpness, solidity of texture and regularity of texture of the object or anomaly wherein the characteristics are represented numerically; providing a knowledge base with a class category, each class category comprising a plurality of correctly classified samples of known objects and anomalies stored as sets of high level descriptors, with individual characteristics stored numerically; and numerically comparing the set of higher level descriptors of the object or anomaly to preclassified high level descriptors in a knowledge base by calculating a similarity function to determine the knowledge base class with the closest similarity to the high level descriptor of the object or anomaly.