A multi-channel-based approach for extracting significant scales on gray-level images

This paper presents the construction of a novel representation of gray-level shape called the scale-spectrum space, which makes both spatial frequency channels of specific importance as well as significant scale levels from the view-point of these spectrum bands explicit. In scale-space representation it is often not possible to obtain an image in which all the structures are described at their best scale levels, since if one structure is well-enhanced, the other ones appear blurred. At best, some forms of compromise among the structures at different scale levels may be sought. To overcome this problem, we present an efficient multichannel scheme which may be employed to automatically describe each gray-level structure at its most suitable level of smoothing. Such multichannel organization is selectively sensitive to spatial frequency and size which is biologically inspired by the behavior of visual cortex neurones as well as retinal cells.