|Title:||GA method for image registration|
|Subject:||Hong Kong Polytechnic University -- Dissertations.|
|Department:||Department of Electronic and Information Engineering|
|Pages:||ix, 101 leaves : ill. (some col.) ; 30 cm.|
|Abstract:||Image registration is one of the elementary steps in image processing. The purpose of image registration is to overlay two images (or more images) of the same scene that are obtained at different times, from different viewpoints, or by different sensors. Typically, image registration can be applied hi medical imaging, remote sensing and computer vision. The images used in the process of registration are called the reference image and the study image. The reference image is the target image while the study image is the transformed image hi the registration process. During the process of image registration, the transformation method is an important component. In brief, the transformation method transforms the study image to the reference image based on the corresponding relationship of the objects. The judgment criteria can be used to calculate the performance after the registration. For the purpose of achieving satisfied performance of the registered image, there are many mathematical optimization methods used hi the process of the transformation method, such as steepest gradient descent optimization, conjugate gradient optimization and Newton methods, etc. However, most of the gradient optimization methods are easily trapped in the local minimum value. Genetic Algorithms (GA) is one of the optimization technologies in global area. Basically, Genetic Algorithms is an adaptive searching algorithm which is inspired by biological evolution. Generally speaking, Genetic Algorithms optimize the fitness function, an objective function, mainly by three GA operations. The optimization results are generated iteratively until the termination conditions are satisfied. In order to overcome the non-optimal solution drawback, Genetic Algorithms is applied in the dissertation as the optimization method. Compared with other gradient based optimization methods, Genetic Algorithms is a derivative free optimization algorithm where only similarity cost function needs to be assessed during the optimization iterations. In my dissertation, the main contributions focus on using Genetic Algorithms to optimize the process of image registration in the platform of MATLAB software. The study image is transformed followed by B-spline transformation method based on the reference image. The performance of the proposed Genetic Algorithms method is compared with those of the other different optimization methods in the dissertation. The benefits and the limitations of GA image registration are analyzed as well.|
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