Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Fuzzy-Neural Systems
Computer Vision and Fuzzy-Neural Systems
Content-Based Image Retrieval Based on a Fuzzy Approach
IEEE Transactions on Knowledge and Data Engineering
A new approach to image retrieval with hierarchical color clustering
IEEE Transactions on Circuits and Systems for Video Technology
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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Model-based automatic target recognition (ATR) systems deal with recognizing three dimensional objects from two dimensional images. In order to recognize and identify objects the ATR system must have one or more stored models. Multiple two dimensional views of each three dimensional object that may appear in the universe it deals with are stored in the database. During recognition, two dimensional view of a target object is used a query image and the search is carried out to identify the corresponding three dimensional object. Stages of a model-based ATR system include preprocessing, segmentation, feature extraction, and searching the database. One of the most important problems in a model-based ATR system is to access the most likely candidate model rapidly from a large database. In this paper we propose new architecture for a model-based ATR system that is based on association-based image retrieval. We try to mimic human memory. The human brain retrieves images by association. We use generalized bi-directional associative memories to retrieve associated images from the database. We use the ATR system to identify military vehicles from their two dimensional views.