Conference Papers

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    Multi-objective Genetic Algorithm for efficient point matching in multi-sensor satellite image
    (2012) Senthilnath, J.; Omkar, S.N.; Mani, V.; Kalro, N.P.; Diwakar, P.G.
    This paper investigates a new approach for point matching in multi-sensor satellite images. The feature points are matched using multi-objective optimization (angle criterion and distance condition) based on Genetic Algorithm (GA). This optimization process is more efficient as it considers both the angle criterion and distance condition to incorporate multi-objective switching in the fitness function. This optimization process helps in matching three corresponding corner points detected in the reference and sensed image and thereby using the affine transformation, the sensed image is aligned with the reference image. From the results obtained, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient. © 2012 IEEE.
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    A new SIFT matching criteria in a genetic algorithm framework for registering multisensory satellite imagery
    (Association for Computing Machinery acmhelp@acm.org, 2014) Senthilnath, J.; Prasad, R.
    Synthetic Aperture Radar (SAR) images are efficient and reliable source of information in extraction of damaged regions in case of floods. In assessment of damage accurately due to floods, image registration of optical (before-flood) and SAR images (after-flood) has to be carried out efficiently. In this paper, we discuss a robust multi-sensor image registration algorithm using scale invariant feature points for keypoint extraction. For matching the keypoints, a multi-objective genetic algorithm is developed with angle, distance and vicinity criterion as the fitness functions. This optimization process helps in matching the scale invariant feature points. From the obtained results, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient. © 2014 ACM.