<title>Medial description of gray-scale image structure by gradient-limited diffusion</title>

Abstract

The representation of object shape in grayscale images is an important precursor to many common image interpretation needs, including recognition, registration, and measurement. Typically, such computer vision tasks have required the preliminary step of image segmentation, often via the detection of object edges. This paper presents a new means of describing grayscale object shape that obviates the need for edge-finding, i.e., classifying pixels as being inside or outside of an object boundary. Instead, this technique operates directly on the image intensity distribution to produce a set of `medialness' measurements at every pixel and across multiple spatial scales that capture more global properties of object shape. The application of an orientation-sensitive, gradient-limited diffusion model provides many of the benefits of global, multiscale structural analysis while preserving the local region- insulating effects of pixels having edge-like properties. The result of this procedure is a multiscale medial axis (MMA), which represents an object at multiple and simultaneous levels of scale, and which provides several desirable properties for describing the shape of grayscale forms.