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This paper presents a semi-automatic registration algorithm for partially overlapped aerial images which has been successfully tested with images of the Mississippi Delta area. In this algorithm, each individual aerial image is registered by employing a pattern search method. This pattern search method considers an affine transformation without shear as a 5-parameter vector and searches toward the gradient direction that results in higher similarity values between the reference and the sensed images. The registration of the first aerial image requires some initial manual work. After the registration of the first image, based on the overlap of the neighboring images and the existing transformation parameters for the first image, the search starting point of the second image can be automatically obtained for the registration of the second image. This process can be repeated for the remaining aerial images in a sequential order. Experimental results with 12 sample images demonstrate the success of this algorithm.

A merging procedure joining search pattern and variable block size motion estimation for H.264/AVC is proposed in this paper. The principal purpose of the proposed methods is the reduction of the computational complexity for block matching module. In fact, there are numerous contributions in the literature aiming the reduction of the computational cost needed for motion estimation. The best solution from a qualitative point of view is the full search that considers every possible detail. The computational effort required is enormous and this makes motion estimation by far the most important computational bottleneck in video coding systems. Our approach invests and exploits the center-biased characteristics of the real world video sequences, aiming to achieve an acceptable image quality while independently targeting the reduction of the computational complexity. The simulations results demonstrated that the proposal performs well.

Soft computing has attracted many research scientists, decision makers and practicing researchers in recent years as powerful computational intelligent techniques, for solving unlimited number of complex real-world problems particularly related to research area of optimization. Under the uncertain and turbulence environment, classical and traditional approaches are unable to obtain a complete solution with satisfaction for the real-world problems on optimization. Therefore, new global optimization methods are required to handle these issues seriously. One such method is hybrid Genetic algorithms and Pattern search, a generic, flexible, robust, and versatile framework for solving complex problems of global optimization and search in real-world applications.