ABSTRACT
Segmentation is a fundamental building block in image processing analysis. It is the first stage of analysing an image. It partitions the image into disjoint regions, which correlate strongly with objects or features of interest where pixels of similar attributes are grouped together. Segmentation techniques may be non-contextual or contextual. In the non-contextual technique, features of an image are not taken into account and the pixels are grouped together on the basis of a global attribute, that is, pixel intensities. It does not take into account the relative location of the pixels. The contextual technique takes into account the closeness of the pixels in an object. That is, it exploits the relationship between the image features. Thresholding is a simple and non-contextual technique. It is computationally inexpensive and fast. Thresholding is a segmentation technique that classifies the pixels into two categories: those pixels that fall below the threshold and those that fall above the threshold. It involves analysing the histogram. A threshold may be global or local. Global threshold selects a threshold for the entire image. This method does not work if there is uneven illumination, and in that case, local threshold works well. In local thresholds, thresholds are obtained from each subregion of an image, thereby adapting to local variations.
