Lines and curves in an image are detected locally by a template-matching process which determines the "line-ness" value of the image at each point, in a set of orientations. The output of the detection process is the strongest of these values at each point, and the orientation that gave rise to this value. The results of this approach tend to be noisy, but their noisiness can be reduced by examining, for each point, the values at nearby points, in the direction defined by the preferred orientation, and increasing the point's value if the nearby points have high values and similar orientations. Iteration of this reinforcement process leads to further noise reduction. Several variations on this scheme are presented. The preferred orientations can also be "sharpened" by examining the orientation at nearby points (in the preferred direction) and biasing it toward their average. Experimental results using these methods are obtained for LANDSAT and SKYLAB images containing many linear features.
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