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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https://doi.org/10.1007/978-3-531-91870-9ich is a combined segmentation quality measure derived from equally weighted sum of the five quality measures. There are times, however, when it is not practical or not acceptable to simply combine global segmentation quality measures (which evaluate segmentation quality of the whole image) with loc

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Book 1994ristics caused by variable environmental conditions,such as time of day, time of year, weather, etc. Image segmentationperformance is evaluated using multiple measures of segmentationquality. These quality measures include global characteristics of theentire image as well as local features of indivi

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https://doi.org/10.1007/978-3-663-05863-2tually make changes in the executable code of a program. In most of these cases, the adaptation involves changing the condition/action statements of the rules in a production system. Since almost every image segmentation algorithm has parameters to control the segmentation results, we have adopted t

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Introduction, 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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