@conference {10.1007/978-3-030-34869-4_37,
	title = {Image Registration Using Single Swarm PSO with Refined Search Space Exploration},
	booktitle = {Pattern Recognition and Machine Intelligence},
	year = {2019},
	note = {n/a},
	pages = {337{\textendash}346},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {<p>Image registration is an elementary task in Computer Vision, which geometrically aligns multiple images of a scene, captured at different times, from various viewpoints, or by heterogeneous sensors. The optimisation strategy we employ for achieving the optimal set of transformation vectors is a major factor that determines the success and effectiveness of an automatic registration procedure. This paper discusses a scheme to modify the conventional Particle Swarm Optimisation (PSO) algorithm for better search space exploration and for faster convergence. While PSO is running, after half of the total number of iterations, find the particle which is in worst position in space, then reposition that particle by mean value of its current position and the global solution. It is observed that re-positioning the worst particle in space helps that particle from premature convergence to a local optimum solution and motivates the particle to generate unique search directions, which increased the possibility of finding the globally best solution. An image registration algorithm using this modified PSO method is also presented. From the experimental results presented here, it is visible that the proposed algorithm guarantees superior results in terms of registration accuracy and reduced execution time, even in the case of large deformations between the reference and float images.</p>
},
	isbn = {978-3-030-34869-4},
	author = {Maddaiah, P. N. and Pournami, P. N.},
	editor = {Deka, Bhabesh and Maji, Pradipta and Mitra, Sushmita and Bhattacharyya, Dhruba Kumar and Bora, Prabin Kumar and Pal, Sankar Kumar}
}
