Low Level Filtering for Perceptually Interesting Objects

A computational technique is needed which can combine the effects of contrast, symmetry and scale to find the set of the interesting regions in an image. An example of the possible output of such an algorithm would be a collection of points defining circular regions of a certain radius (or scale) which exhibit perceptual importance. A mask or filter is needed which can be quickly applied locally (topographically) over the whole image at multiple scales. The output of the mask would be a perceptual significance map which measures the level of contrast and symmetric enclosure of the image region overlapped by the filter. To detect large perceptually significant objects first, this mask would be applied first at large scales (i.e., with a relatively large mask) and then at progressively smaller ones. Such a filter would provide us with an efficient attentional mechanism for quickly fixating further face-recognition computational resources only on interesting regions.