Cellular neural network–based hybrid approach toward automatic image registration

Pattathal VijayaKumar Arun; Sunil Kumar Katiyar

doi:10.1117/1.JRS.7.073533

17 July 2013 Cellular neural network–based hybrid approach toward automatic image registration

Pattathal VijayaKumar Arun, Sunil Kumar Katiyar

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 7, Issue 1, 073533 (July 2013). https://doi.org/10.1117/1.JRS.7.073533

Abstract

Image registration is a key component of various image processing operations that 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 has limited the attainable accuracy. A framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as vector machines, cellular neural network (CNN), scale invariant feature transform (SIFT), coreset, and cellular automata is proposed. CNN has been 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 coreset optimization. The salient features of this work are cellular neural network approach–based SIFT feature point optimization, 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. This system has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. This methodology is also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

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Pattathal VijayaKumar Arun and Sunil Kumar Katiyar "Cellular neural network–based hybrid approach toward automatic image registration," Journal of Applied Remote Sensing 7(1), 073533 (17 July 2013). https://doi.org/10.1117/1.JRS.7.073533

Published: 17 July 2013

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