The process of identifying the objects from a given set of structured images is an ambiguous one to implement due to its immense architecture of defining the image components and distinguishing it with the proper features. Neural networks play a vital role in the field of image processing through its dense approaches for handling the image components by its existing feature existence. Object extraction and interpretation are the basis for identifying the objects from the structured image. The process of learning interesting information from the images through iterated network training provides the expected results for optimal identification of objects from the structured images when compared with normal object extraction and interpretation approaches. The proposed methodology of this paper deals with the optimal object identification through neural networks based feature extraction, classification, and verification. In future this research article will be extended by implementing the Artificial Intelligence based soft computing approach towards the exact ideal matching in object identification with image processing approach.

imageobject, image feature,object identification, neural networks,optimal identification