Our inferences are not always infallible, although most often when we do fail to interpret a line drawing correctly we usually arrive at an ambiguous interpretation rather than an incorrect one. The understanding of line drawings brings together ideas from computer vision and artificial intelligence, and has become a classical field of study in both areas. The problem is usually divided into two steps, namely labeling and realization . Labeling is meant to provide a qualitative description of the scene, by classifying the segments of a line drawing as the projection of concave, convex or contour edges. Realization involves the physical legitimacy of the interpreted scene, and tries to recover the underlying 3D structure.
Image features resulting from different scene events.
Such edges are known as limbs or apparent contours . In this lecture we will assume that the line drawing contains no surface reflectance discontinuities and no illumination discontinuities. Thus, we will only deal with depth and orientation, concentrating on the geometric aspects of the image. What we would like to do is interpret the surfaces making up the objects in the scene. Clearly, we can't expect to determine functions that describe these surfaces when we only have the boundary information.