finally comparing the values of the gradient magnitude in the estimated gradient direction. A commonly used approach to handle the problem of appropriate thresholds for thresholding is by using thresholding with hysteresis. This method uses multiple thresholds to find edges. We begin by using the upper threshold to find the start of an edge. Once we have a start point, we then trace the path of the edge through the image pixel by pixel, marking an edge whenever we are above the lower threshold. We stop marking our edge only when the value falls below our lower threshold. This approach makes the assumption that edges are likely to be in continuous curves, and allows us to follow a faint section of an edge we have previously seen, without meaning that every noisy pixel in the image is marked down as an edge. Still, however, we have the problem of choosing appropriate thresholding parameters, and suitable thresholding values may vary over the image. Edge thinning Edge thinning is a technique used to remove the unwanted spurious points on the edges in an image. This technique is employed after the image has been filtered for noise (using median, Gaussian filter etc.), the edge operator has been applied (like the ones described above, Canny or Sobel) to detect the edges and after the edges have been smoothed using an appropriate threshold value. This removes all the unwanted points and if applied carefully, results in one pixel thick edge elements. Advantages: Sharp and thin edges lead to greater efficiency in object recognition. If Hough transforms are used to detect lines and ellipses, then thinning could give much better results. If the edge happens to be the boundary of a region, then thinning could easily give the image parameters like perimeter without much algebra. There are many popular algorithms used to do this, one such is described below: Choose a type of connectivity, like 8, 6 or 4. 8 connectivity is preferred, where all the immediate pixels surrounding a particular pixel are considered. Remove points from North, south, east and west. Do this in multiple passes, i.e. after the north pass, use the same semi processed image in the other passes and so on. Remove a point if:The point has no neighbors in the North (if you are in the north pass, and respective directions for other passes).The point is not the end of a