It should be noted that the segmentation performance varies significantly depending on certain parameters supplied. Also, the number of orientations should be chosen appropriately. For most practical purposes, a value of 8-16 is widely used. An appropriate threshold value for filtering the noise from the edge-flow intensity matrix also needs to be supplied. Any edge with strength below this threshold value is ignored.
Human eye is the best judge for such results.
Other than the idea and justification of the theory behind the implementation, and the observation based on the results presented above, there is little else we can say about the performance of the algorithm. The Edge Flow algorithm has been successfully and efficiently implemented and tested. It has been found that the algorithm is very general and is well-suited for all kinds of images, right from those with simple structures without any texture to those with very complicated textures. It allows the segmentation of any image as desired, with the specification of only one significant parameter, sigma, which is supplied by the user This parameter too can be fixed a-priori for certain class of images and segmentation type required, thus minimizing user interaction which is a significant advantage in automatic image recognition and segmentation. Among the drawbacks, the algorithm is fairly complex and cumbersome to implement with a lot of minute details missing, and is also computationally very expensive.