Behavior of Edges in Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiresolution Hierarchical Approach to Image Segmentation Based on Intensity Extrema
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Coping with Discontinuities in Computer Vision: Their Detection, Classification, and Measurement
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Scale-Space for Discrete Signals
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Residual Analysis for Feature Detection
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Adaptive Smoothing: A General Tool for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Widths Yield Reliable Finite Differences (Computer Vision)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reasoning About Edges in Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Determination of Filter Scales for Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Kernel Designs for Efficient Multiresolution Edge Detection and Orientation Estimation
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Edge detection in a gray-scale image at a fine resolution typically yields noise and unnecessary detail, whereas edge detection at a coarse resolution distorts edge contours. We show that ``edge focusing'', i.e., a coarse-to-fine tracking in a continuous manner, combines high positional accuracy with good noise-reduction. This is of vital interest in several applications. Junctions of different kinds are in this way restored with high precision, which is a basic requirement when performing (projective) geometric analysis of an image for the purpose of restoring the three-dimensional scene. Segmentation of a scene using geometric clues like parallelism, etc., is also facilitated by the algorithm, since unnecessary detail has been filtered away. There are indications that an extension of the focusing algorithm can classify edges, to some extent, into the categories diffuse and nondiffuse (for example diffuse illumination edges). The edge focusing algorithm contains two parameters, namely the coarseness of the resolution in the blurred image from where we start the focusing procedure, and a threshold on the gradient magnitude at this coarse level. The latter parameter seems less critical for the behavior of the algorithm and is not present in the focusing part, i.e., at finer resolutions. The step length of the scale parameter in the focusing scheme has been chosen so that edge elements do not move more than one pixel per focusing step.