Open AccessA CNN BASED HYBRID APPROACH TOWARDS AUTOMATIC IMAGE REGISTRATION
Author(s) -
P. Arun
Publication year - 2013
Publication title -
geodesy and cartography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.33
H-Index - 11
eISSN - 2029-6991
pISSN - 2029-7009
DOI - 10.3846/20296991.2013.840409
Subject(s) - computer science , artificial intelligence , feature (linguistics) , resampling , domain (mathematical analysis) , matching (statistics) , key (lock) , component (thermodynamics) , salient , object (grammar) , pattern recognition (psychology) , machine learning , computer vision , data mining , mathematical analysis , philosophy , linguistics , statistics , physics , mathematics , computer security , thermodynamics
Image registration is a key component of spatial analyses that involve different data sets of the same area. Automatic approaches in this domain have witnessed the application of several intelligent methodologies over the past decade; however accuracy of these approaches have been limited due to the inability to properly model shape as well as contextual information. In this paper, we investigate the possibility of an evolutionary computing based framework towards automatic image registration. Cellular Neural Network 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 corset optimization. CNN-prolog based approach has been adopted to dynamically use spectral and spatial information for representing contextual knowledge. The salient features of this work are feature point optimisation, adaptive resampling and intelligent object modelling. Investigations over various satellite images revealed that considerable success has been achieved with the procedure. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling.