Using occlusion models to evaluate scale-space processors

There are several systems that are claimed to generate scale-spaces but how do they compare? This paper examines four significant contenders using both synthetic and real models of occlusion in images. We find that anisotropic diffusion, while effective at removing noise, is not suitable for the estimation of position or scale of simple objects. Linear diffusion may be used for scale selection but has poor performance in occlusion and noise. Of the morphological scale-spaces that we discuss, we find that filtering with flat, connected set structuring elements is not only quicker to compute than diffusion-based scale-spaces, but the results are also perturbed less in the presence of common image distortions such as noise or occlusion.