Current edge detection techniques are often insensitive to some perceptually significant edges in an image. Similarly, segmentation techniques do not always segment an image into perceptually meaningful regions. Edge detection and segmentation algorithms can be improved by incorporating the natural constraints used by humans in the perception of line drawings. To uncover some of these natural constraints, psychophysical experiments were performed. The results show that the perceptibility of individual lines in a drawing depends upon the presence of particular local features. First, short lines appear to have lower contrast than long lines. Second, there is a hierarchy of three types of connections between the ends of line segments, and the perceived contrast of a line increases by different amounts, depending upon which types of end connections are present. The natural constraints uncovered in the psychophysical experiments were incorporated into a computer vision module which selectively enhances and segments line drawings. The in-put to the module is a drawing containing straight and/or curved lines, and the output is an enhanced and segmented version of the line drawing. The computations in the module are local and can be performed in parallel. They are efficient because of the simplicity of the operations involved, and because they are performed just once-no iterative relaxation is required. The use of natural constraints results in lines being enhanced in accordance with their perceptual significance. For example, lines which are part of the outer contours of objects are enhanced more than other lines, while lines that are part of object edges are enhanced more than lines which form part of textures or noise. In addition, when one object occludes another, the object in the foreground is enhanced versus the occluded object. When objects are accidentally aligned, the segmentation computed by the module agrees with the segmentation used by humans. By using the natural constraints, many tasks which previously had been thought to require domain dependent knowledge, can be performed using data driven, bottom-up processing.