Estimation of Similarity of Image Data Using Associative Memory and Its Applications
Estimation of Similarity of Image Data Using Associative Memory and Its Applications
批准号:
63580029
负责人:
MURAKAMI Kenji
金额:
$1.15万
依托单位:
依托单位国家:
日本
项目类别:
Grant-in-Aid for General Scientific Research (C)
财政年份:
1988
资助国家:
日本
项目状态:
已结题
起止时间:
1988 至 1989
中文摘要
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英文摘要
The results of the project are summarized in the following five points:(1) A new associative memory model which realizes both the stable and minimum error association for degraded input image data is proposed. The model is constructed based on the image restoration model. The performance of the model is also analyzed theoretically.(2) In the Moore-Penrose generalized inverse associative memory, image data are memorized distributively in a matrix form. When a large scale image database is constructed by combining small scale databases, it is necessary to consolidate the memorized matrices. Using the property of orthonormal projection, a method to consolidate the matrices is introduced. From a theoretical analysis, we show the relation between the estimation ability of similarity and the number of memorized prototype image data.(3) An orthogonal transformation method of energy function in Hopfield network is proposed. In programming a network, the energy functions are defined by a linear … More combination of an objective function and constraints. Since the combination causes interference, the solution would lack correctness or validity. The orthogonal transformation method improves this kind of badness by redefining the function in another basis. We verify the method can work for a Fuzzy Clustering of image data.(4) In conventional image database, in order to estimate the similarity of two images, some features are extracted from them. Typical features which are often used in conventional image database are selected, and some properties of the features are given.(5) A simple method of full color image data compression technique that significantly economizes on the number of bits required for an image is proposed. The proposed method is based on the multi-valued color dithering method and the conventional Popularity Algorithm. In the method, due to the effect of dither and the ensuing adequate color selection, both color reproduction and spatial resolution is easily obtained, and strong contouring is also suppressed. Less
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村上研二: "Construction of Associative Memory from Incomplete Memorized Information Using Space Reduction Method" Systems and Computers in Japan. (1990)
Kenji Murakami:“使用空间缩减方法从不完整的记忆信息构建联想记忆”系统和计算机在日本(1990)。
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村上研二: 愛媛大学工学部紀要. 11. 135-142 (1989)
村上健二:爱媛大学工学部通报。11. 135-142 (1989)。
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Kenji Murakami: "Wiener Type Associative Memory" Memoirs of the Faculty of Engineering, Ehime University. Vol.XI, No.4. 135-142 (1989)
村上健二:爱媛大学工学部《维纳型联想记忆》回忆录。
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Kenji Murakami: "Least Squares Associative Memory and a Theoretical Comparison of its Performance" IEEE Trans. on System Man and Cybernetics, Vol.SMC-19, No.5, pp.1230-1234, 1989.
Kenji Murakami:“最小二乘联想记忆及其性能的理论比较”IEEE Trans。
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村上研二: "Least Squares Associative Memory and a Theoretical Comparison of its Performance" LEEE Trans. on Systems,Man,and Cybernetics. SMC-19. 1230-1234 (1989)
Kenji Murakami:“最小二乘联想记忆及其性能的理论比较”,《系统、人类和控制论》上的 LEEE Trans,1230-1234(1989 年)。
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