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 speciic importance (concerning spectrum information being isolated) as well as signiicant scale levels from the viewpoint of these spectrum bands explicit. In scale-space representation, where the gray-level shape is generally comprised of multiple structures at diierent levels of scale, 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 diierent scale levels may be sought. To overcome this problem, we present an eecient multi-channel scheme which may be employed to automatically describe each gray-level structure at its most suitable level of smoothing. The ability to decompose a complex problem{that of where to look as well as how to concentrate on certain features in the input data{ into simpler subproblems is a major motivation for using the proposed scheme. This representation allows data-driven detection of those spectrum bands, and the evolution of scale levels from the viewpoint of such domains, and it is not an eeect of some externally chosen criteria or tuning parameters. Therefore, a multichannel organization is derived, which is selectively sensitive to spatial frequency and size which is biologically inspired by the behavior of visual cortex neurones as well as retinal cells.