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.
Go to article