Memorization and Processing for Image Data Using Associative Memory

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The results of the project are summarized in the following three points:1. A new bilevel representation method for continuous tone images using a probablistic assignment of fixed patterns is presented. Using the method, the size of the memorized image data can be reduced to quarter its original size.2. The problems of how to construct image database using associative memory (Associative Image Database) is considerd.(1) A method to construct an Associative Image Database from incomplete (degraded) memorized image data is proposed. In the method the correct memorized information is obtained from the given incomplete memorized image data using space reduction method. The construction method is useful, because it is very rare to get complete memorized image data in practice.(2) The optimal Associative Image Database for incomplete input key is proposed. It is known that for incomplete input key the Moore-Penrose type associative memory proposed by T.Kohonen (1973) is extremely sensitive (unstable) and its association error becomes unacceptably large. The proposed optimal Associative Image Database (associative memory) realizes both the stable association and the minimum error association. The proposed method is useful, because it is very rare to get complete input key in practice.(3) A practical construction method of Associative Image Database for incomplete input key is also proposed. In the method the stabel association can be easily done, but the association error is not always minimum. The merit of the method is its simplicity.3. An image processing (noise attenuation) method using associative memory is proposed. Since the method is based on the Two-Stepped Association Procedure, the ability of noise attenuation can be controled by changing the dimensions of the mediate vectors. Using the method, both information retrieval and information processing for memorized image data can be done simultaneously in the Associative Image Database.