Threshold procedures and image segmentation

Abstract

Several fundamental image segmentation methods exist for the calculation of threshold values. The simple solutions provide typically inaccurate results, while the complex procedures require huge computation resources and/or long processing time (for example, OTSU's method for image thresholding in case of determining several levels). One of the easiest procedures is to divide the colors into two domains by defining a threshold level (between the trivial thresholds) in the intensity signal, or, in case of several thresholds (L count including the trivial ones) distinguishing L1 number of regions. The optimal threshold levels are always dependent on the image content, therefore the adaptive methods always provide much better results. Such a procedure is the adaptive threshold (ATH) algorithm (Andreas E. Savakis, 1998) (J. Sauvola et al., 1997), which is easy to implement for grayscale images, and with slight modifications it is also suitable for color pictures