Some Applications of Hierarchical Image Processing Algorithms

Within the past few years considerations concerning the structure of the human retina and the perceptual system have led to increasing interest into hierarchically organized image description schemes (1). Such a hierarchical organization allows for controlling the simultaneous processing of information, on low levels of resolution, by information that itself was acquired on a high level of resolution. For example, the investigation of a certain object in an image can be established in an efficient way by determining the spatial location of that object on low resolution levels, followed by detailed analysis on higher resolution levels. In these and related techniques the resolution decreases with increasing height in the hierarchy and vice versa. As a side-effect the amount of noise may be reduced, or superfluous details suppressed. A degree of similarity of spatial distributions can be obtained from a comparison of their descriptions throughout a range of resolution levels. The greater the resolution range in which spatial distributions match, the greater is the degree of similarity between these distributions.