Edge Detection and Ridge Detection with Automatic Scale SelectionTony LindebergTechnical report ISRN KTH NA/P96/06SE. Department of Numerical Analysis and Computing Science, Royal Institute of Technology, S100 44 Stockholm, Sweden, Jan 1996.International Journal of Computer Vision, vol 30, number 2, pp 117154, 1998. Shortened version in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR'96, San Francisco, California, june 1996, pages 465470. Shortened version in Linde, Sparr (Eds.): Proc. Swedish Symposium on Image Analysis, SSAB'96, Lund, Sweden, pages 2428, march 1996. AbstractWhen computing descriptors of image data, the type of information that can be extracted may be strongly dependent on the scales at which the image operators are applied. This article presents a systematic methodology for addressing this problem. A mechanism is presented for automatic selection of scale levels when detecting onedimensional image features, such as edges and ridges.A concept of a scalespace edge is introduced, defined as a connected set of points in scalespace 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 consequence of this definition is that it allows the scale levels to vary along the edge. Two specific measures of edge strength are analysed in detail, the gradient magnitude and a differential expression derived from the thirdorder derivative in the gradient direction. For a certain way of normalizing these differential descriptors, by expressing them in terms of socalled gammanormalized derivatives, an immediate consequence of this definition is that the edge detector will adapt its scale levels to the local image structure. Specifically, sharp edges will be detected at fine scales so as to reduce the shape distortions due to scalespace smoothing, whereas sufficiently coarse scales will be selected at diffuse edges, such that an edge model is a valid abstraction of the intensity profile across the edge. Since the scalespace edge is defined from the intersection of two zerocrossing surfaces in scalespace, the edges will by definition form closed curves. This simplifies selection of salient edges, and a novel significance measure is proposed, by integrating the edge strength along the edge. Moreover, the scale information associated with each edge provides useful clues to the physical nature of the edge. With just slight modifications, similar ideas can be used for formulating ridge detectors with automatic selection, having the characteristic property that the selected scales on a scalespace ridge instead reflect the width of the ridge. It is shown how the methodology can be implemented in terms of straightforward visual frontend operations, and the validity of the approach is supported by theoretical analysis as well as experiments on realworld and synthetic data. Keywords: edge detection, ridge detection, scale selection, diffuseness, normalized derivative, Gaussian derivative, scalespace, multiscale representation, feature detection, computer vision Background: (General scale selection principle) (First reference to general scale selection principle) (Earlier and closely related scale selection methodology for blob detection) (Application of scale selection principle to junction detection) (Scale selection for flow estimation) (Review paper on principles for automatic scale selection) (Monograph on scalespace theory) (Other publications on scalespace theory)
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