Abstract
Otsu’s method is often used in image segmentation applications such as defect detections, medical image diagnosis and object shape recognitions. However it is very time-consuming for multilevel segmentation. Lloyd-Max quantizer is a popular and efficient data compressor. Fundamentally, Otsu’s method and Lloyd-Max quantizer are equivalent to maximum a posteriori probability estimate. Applying them on multilevel image segmentation, we can find their segmented results over an image are very approximate, but Otsu’s method running in exhaustive search consumes more processing time than Lloyd-Max quantizer with iterative characteristics does. Thus, Lloyd-Max quantizer is strongly recommended as the fast and first-stage agent for Otsu’s method to find the optimal threshold values for image segmentation.
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