A multi-channel autofocusing scheme for gray-level shape scale detection

This paper describes the implementation of a multi-channel autofocusing scheme capable of automatically extracting gray-level shape scales. The scale concept in the vision literature stands for the characteristic length over which gray-level variations in the image take place and/or the operator size used for processing the given image. The proposed scheme allows for a data-driven multichannel organization selectively sensitive to spatial frequency and size which is biologically inspired by the behavior of visual cortex neurones as well as retinal cells. We investigate the suitability of several band-pass lter based autofocusing criteria for the scale selection. In scale-space representation where the gray-level shape generally is 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, because if one structure is well-enhanced, the others 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 autofocusing 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. In the absence of further information, the information derived through such a formulation may serve as a guide to subsequent processing requiring knowledge about the scales at which a grey-level structure with particular spectrum components (high, medium or low frequency content) occurs.