Classifying objects using partitions and machine vision techniques

Abstract

A method for classifying objects according to a predefined classification of sample objects. This is achieved by classifying first a plurality of sample object images and determining an image partition for each and every class. The sample classes and their corresponding partitions are used to build a classifying function. The classifying function is built using the partitions and the geometric features and statistical information retrieved from the sample images. In order to classify a specific object, several image copies of the object image to-be-classified are prepared; each image copy is then partitioned according to a different classified-in-advance partition. The classifying is achieved by applying the classifying function that has been built on the object image copies.

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