ICTACT Journal on Image and Video Processing

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Retrieval of Deformation Fields by Using Stochastic Mutual Information Based Optimization in Automatic Registration of Satellite Images


Affiliations
1 ODPD/SIPG, Space Applications Centre, Indian Space Research Organisation, Gujarat, India
2 Department of Civil Engineering, SRM Institute of Science and Technology, India
     

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Modeling and retrieving the transform parameters that characterize the underlying deformation field is the main crux of the problem in automatic image registration domain which involves employing a similarity measure in an image pair and a robust model estimator. Model estimators can be either a least square fit or an optimization method which finds minimum of a cost function. In this work, a stochastic mutual information based adaptive gradient descent optimizer is proposed in which transforms such as translation, affine and free form deformations are accurately retrieved in the process of image registration and only a percentage of population of intensities is used to estimate mutual information without losing accuracy in a stochastic way. Better than one tenth of a pixel accuracy is achieved in image registration by retrieving different geometric transformations accurately.

Keywords

Mutual Information, Image Registration, Optimization, Deformation, Transforms.
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  • Retrieval of Deformation Fields by Using Stochastic Mutual Information Based Optimization in Automatic Registration of Satellite Images

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Authors

Subbiah Manthira Moorthi
ODPD/SIPG, Space Applications Centre, Indian Space Research Organisation, Gujarat, India
Ramamoorthy Sivakumar
Department of Civil Engineering, SRM Institute of Science and Technology, India

Abstract


Modeling and retrieving the transform parameters that characterize the underlying deformation field is the main crux of the problem in automatic image registration domain which involves employing a similarity measure in an image pair and a robust model estimator. Model estimators can be either a least square fit or an optimization method which finds minimum of a cost function. In this work, a stochastic mutual information based adaptive gradient descent optimizer is proposed in which transforms such as translation, affine and free form deformations are accurately retrieved in the process of image registration and only a percentage of population of intensities is used to estimate mutual information without losing accuracy in a stochastic way. Better than one tenth of a pixel accuracy is achieved in image registration by retrieving different geometric transformations accurately.

Keywords


Mutual Information, Image Registration, Optimization, Deformation, Transforms.

References