Abstract
In this paper, we propose Evidence-based technique for image registration. In our previous work, we proposed hierarchical model for image registration using Normalized Mutual Information (NMI) as similarity metric. In few cases, we observe atypical behavior of NMI and infer NMI alone is not sufficient to optimize the transformation matrix, to address this problem in this paper we propose evidence-based image registration using Structural Similarity (SSIM) and NMI as evidences. Atypical behavior of NMI is addressed in evidence- based image registration. We also propose evidence-based framework for image fusion and show image fusion is sensitive to the registration of input observations. Multi-temporal image fusion is challenging due to the presence of high mutual information among them. To address this, we formulate an evidence-based fusion framework with weighted combination of observations, considering Confidence Factor (CF) as weights. CFs for fusion are generated using principal components and distance of registered input observations from reference as evidences. Dempster–Shafer Combination Rule (DSCR) is used to combine the evidences to generate CF. We compare the results with state-of-the-art registration techniques.
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Patil, U., Tabib, R.A., Dhanakshirur, R.R., Mudenagudi, U. (2020). Evidence-Based Image Registration and Its Effect on Image Fusion. In: Elçi, A., Sa, P., Modi, C., Olague, G., Sahoo, M., Bakshi, S. (eds) Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing, vol 766. Springer, Singapore. https://doi.org/10.1007/978-981-13-9683-0_4
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DOI: https://doi.org/10.1007/978-981-13-9683-0_4
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