Edge Detection and Ridge Detection with Automatic Scale SelectionTony

When extracting features from image data, the type of information that can be extracted may be strongly dependent on the scales at which the feature detectors are applied. This article presents a systematic methodology for addressing this problem. A mechanism is presented for automatic selection of scale levels when detecting one-dimensional features, such as edges and ridges. A novel concept of a scale-space edge is introduced, de-ned as a connected set of points in scale-space at which: (i) the gradient magnitude assumes a local maximum in the gradient direction, and (ii) a normalized measure of the strength of the edge response is locally maximal over scales. An important property of this deenition is that it allows the scale levels to vary along the edge. Two speciic measures of edge strength are analysed in detail. It is shown that by expressing these in terms of-normalized derivatives, an immediate consequence of this deenition is that ne scales are selected for sharp edges (so as to reduce the shape distortions due to scale-space smoothing), whereas coarse scales are selected for diiuse edges, such that an edge model constitutes a valid abstraction of the intensity proole across the edge. With slight modiications, this idea can be used for formulating a ridge detector with automatic scale selection, having the characteristic property that the selected scales on a scale-space ridge instead reeect the width of the ridge.