Abstract
Image template matching is essential in image analysis and computer vision tasks. Cross-correlation algorithms are often used in practice, but they are sensitive to nonlinear changes in image intensity and random noise, and are computationally expensive. In this paper, we propose a template-matching algorithm based on a modified particle swarm optimization (PSO) procedure with a mutual information (MI) similarity measure. The influence of MI on the performance of template matching, calculated by different histogram bins, is analyzed first. A modified PSO method (CRI-PSO) is then presented. The proposed algorithm is tested with remote sensing imagery from different sensors and for different seasons. Our experimental results indicate that the proposed approach is robust in practical scenarios and outperforms the standard PSO , multi-start PSO , and cross-correlation algorithms in accuracy and efficiency with our test data. The proposed method can be used for position estimation of aircraft, object recognition, and image retrieval.
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