Using edges to improve Global Thresholding One approach for improving the shape of histograms is to consider only those pixels that lie on or near the edges between objects and the background. Improvement is that the histograms would be less dependent on the relative sizes of objet and background.(peaks would be same sizes). Using gradient value we can find edges, also with Laplacian value bordering pixels on object and background sides are determined.

Multiple Thresholds

Variable Thresholding based on local image properties

Using Moving Averages An special case of the local thresolding method is based on computing a moving average along scan lines of an image. This is useful in document processing where speed is a fundamental requirement. Let denote the intensity of point encountered in the scanning sequence at step k+1 The moving average (mean intensity(at this new point is given by: n is the number of points used in computing moving average. For document processing n is set to 5 times ofaverage strock width.

When image histogram is multimodal the region based methods are most proper approaches for image sementation.