Binarization Based on Maximum and Average Gray Values
Many image processing techniques use binarization for object detection in images, where the objects and background are well distinct by their brightness values, where, the threshold level is globally assigned, on the other hand, if it’s adaptive, the threshold level is locally calculated. In order t...
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| Autor principal: | |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
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IntechOpen
2022
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/78646 |
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| Sumario: | Many image processing techniques use binarization for object detection in images, where the objects and background are well distinct by their brightness values, where, the threshold level is globally assigned, on the other hand, if it’s adaptive, the threshold level is locally calculated. In order to determine the optimal binarization threshold, from an image with the mean gray values and extreme gray values, exchanging the mean gray values relating to automatic analisis for a standard histogram equalization, which can evaluate a wide range of image features, even when the gray values in both the object of interest and background of the image are not uniform. |
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| ISBN: | 978-1-83969-794-4 |
| DOI: | 10.5772/intechopen.99932 |