This paper examines why the growing gap-year phenomenon is important for university geography departments in the context of education and employment. The research examines the scale and types of gap years, and their effects on students. The study uses a multi-actor approach comprising information from national statistical sources, university departments, students who have taken gap years and commercial gap-year providers. The paper draws some lessons for geography departments such as the need to systematically record the effects of a gap year. It highlights some paradoxes of any expansion of the gap-year market; for example, that expansion may reduce the benefits of the gap-year experience and may narrow the types of gap year taken. Issues of social exclusion also arise.
Image registration is a technique for precisely aligning the content of two or more images. It is often used as a preprocessing stage for further analysis, such as automatic target recognition, change detection, and environmental remote sensing. However, there are many different registration algorithms available to the image analyst, and it's difficult to know which one is the best one to use for a particular pair of images. These various algorithms also have a multitude of settings and parameters that must be given proper values for best results. Consequently, it is often difficult to know which algorithm will perform the best in a given situation, under constraints of time or accuracy. We propose constructing an expert system, with rules based on experimental results, that will automatically select the appropriate registration algorithm and perform appropriate preprocessing steps to prepare the images for registration.
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