Abstract
Image registration helps in aligning corresponding points in images acquired under different conditions. The different contributory conditions can be sensor modality, time, and viewpoint. One critical aspect of image registration is matching corresponding positions in different images to be registered. Image matching signifies the difference between a successful registration or otherwise. With the increasing applications of image processing in solving real-world problem, there is a need to identify and implement effective image matching protocols. In this work, Scale-invariant Feature Transform (SIFT) and Affine—Scale-invariant Feature Transform (ASIFT) have been implemented and analyzed for performance. The performance analysis is done for different images with different attributes like change in tilt and illumination. Apart from calculating the number of matches, the accuracy of the correct matches has been calculated through manual visual inspection. The results demonstrate the efficiency of ASIFT over SIFT in delivering an enhanced performance.
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D., R., T., J., K., S. (2018). Implementation and Performance Analysis of SIFT and ASIFT Image Matching Algorithms. In: Dash, S., Naidu, P., Bayindir, R., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-7868-2_43
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DOI: https://doi.org/10.1007/978-981-10-7868-2_43
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