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• W2014372423 abstract "Abstract Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as contextual information had limited the attainable accuracy. In this paper, we propose a framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using corset optimization The salient features of this work are cellular neural network approach based SIFT feature point optimisation, adaptive resampling and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. System has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling." @default.
• W2014372423 created "2016-06-24" @default.
• W2014372423 creator A5052051562 @default.
• W2014372423 creator A5070707003 @default.
• W2014372423 date "2013-06-01" @default.
• W2014372423 modified "2023-10-18" @default.
• W2014372423 title "A CNN based Hybrid approach towards automatic image registration" @default.
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• W2014372423 doi "https://doi.org/10.2478/geocart-2013-0005" @default.
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