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Titlebook: Genetic Learning for Adaptive Image Segmentation; Bir Bhanu,Sungkee Lee Book 1994 Springer Science+Business Media New York 1994 Navigation

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Segmentation as an Optimization Problem,omplexity of the objective function, and variations in the objective function caused by changes in the imagery as well as the accepted definition of the function itself. In this chapter, we formulate segmentation as a parameter optimization problem and discuss the choice of genetic algorithms for adaptive image segmentation.
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https://doi.org/10.1007/978-1-4615-2774-9Navigation; algorithms; cognition; computer vision; control; genetic algorithms; image segmentation; learni
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Klientelnationen: Der Fall der DDR regions may roughly correspond to objects, parts of objects, or groups of objects in the scene represented by that image. It can also be viewed as the process of identifying edges that correspond to boundaries between objects, and regions that correspond to surfaces of objects in the image. Segment
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https://doi.org/10.1007/978-3-8349-8056-4omplexity of the objective function, and variations in the objective function caused by changes in the imagery as well as the accepted definition of the function itself. In this chapter, we formulate segmentation as a parameter optimization problem and discuss the choice of genetic algorithms for ad
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Wirtschaftende Personen und Institutionen,4. The images in the database are intended to be more complex than the indoor images used in Chapter 5 and the type of variations between the images are more extensive due to the changing position and intensity of the sun. This movement creates varying object highlights, moving shadows, and many sub
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Wirtschaftende Personen und Institutionen,aditional approach commonly used in the Computer Vision field. In this chapter, we carry out further experiments to evaluate the effectiveness of the baseline adaptive image segmentation technique. In particular, we perform experiments, (a) to compare the baseline adaptive image segmentation system
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