An evolutionary computational based approach towards automatic image registration
This work addresses accuracy limitations in automatic image registration for domains like satellite image analysis, though it appears incremental as it builds on existing intelligent methodologies.
The paper tackles the problem of automatic image registration by proposing a framework that combines Vector Machines, Cellular Neural Networks, SIFT, coreset optimization, and Cellular Automata to improve feature shape modeling and adaptive resampling. The approach achieved considerable success in tests on satellite images, with reduced complexity compared to contemporary methods.
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.