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Thresholding is a commonly used technique in image segmentation because of its fast and easy application. How to choose an appropriate threshold which preservers most feature information has drawn a lot of research interests. In this book an adaptive logical thresholding method is proposed for the binarization of blueprint images first. The new method exploits the geometric features of blueprint images, implemented by utilizing a robust windows operation which is based on the assumption that the objects have certain shape in a small area. Second, the fuzzy set theory, along with probability partition and maximum entropy theory, is explored to compute the threshold based on the histogram of the image. A novel fuzzy membership function is proposed which gives more flexibility in the search for the optimal fuzzy sets, although it also increases the complication in the search for the fuzzy sets in multi-level thresholding. This complication is solved by a new method called the "Onion-Peeling" method. And a multi-resolution thresholding method based on quadtree scheme and fuzzy partition is also devised for images with dominant area of background or object.