SUBPIXEL-PRECISE EXTRACTION OF LINES AND EDGES

Abstract

Novel approaches to extract curvilinear structures, i.e., lines and edges, from 2D images are proposed. For lines, explicit geometric models for line profiles are used to analyze their behavior in scale-space. From this analysis, algorithms to extract lines and their widths with subpixel resolution are derived, and it is shown that the line positions and widths are necessarily biased by the smoothing used in the extraction. Since the mapping that describes the bias is invertible, it can be removed, leading to unbiased and hence accurate results. To extract edges, they are regarded as bright lines in the gradient image. Furthermore, by a scale-space analysis it is shown why line and edge junctions often cannot be extracted. From this analysis, a new method to extract complete junction information is derived. Finally, the line and edge extraction algorithms are extended to multispectral images.