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 Apparatus and method for automatic knowlege-based object identification

Details
Inventors: Hennessey, Audrey Kathleen; Lin, YouLing; Khaja, Veera V. S.; Pattikonda, Ramakrishna; Reddy, Rajasekar; Lu, Huitian; Katragadda, Ramachandra;
Assignee: Texas Instruments Incorporated (Dallas, TX)
Primary Examiner: Johns; Andrew W.
Assistant Examiner:
Attorney, Agent or Firm: Troike; Robert L., Donaldson; Richard L.

An apparatus and method for automatic knowledge-based object or anomaly classification is provided by capturing a pixel map of an image and from that generating high level descriptors of the object or anomaly such as size, shape, color and sharpness. These descriptors are compared with sets of descriptors in a knowledge-base to classify the object or anomaly.

DETAILED DESCRIPTION What is claimed is: 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.
2.
The method of claim 1 further including the step of detecting repeatable and non-repeatable images.
3.
The method of claim 2 wherein said non-repeatable image includes a sudden change in a straight line.
4.
The method of claim 2 wherein said non-repeatable image includes an irregular angle.
5.
The method of claim 2 wherein said non-repeatable image includes a vague edge



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