The interpretations people attach to line drawings reflect shape-related processes in human vision. Their divergences from expectations embodied in related machine vision traditions are summarized, and used to suggest how human vision decomposes the task of interpretation. A model called IO implements this idea. It first identifies geometrically regular, local fragments. Initial decisions fix edge orientations, and this information constrains decisions about other properties. Relations between fragments are explored, beginning with weak consistency checks and moving to fuller ones. IO's output captures multiple distinctive characteristics of human performance, and it suggests steady progress towards understanding shape-related visual processes is possible.
|Number of pages||11|
|Journal||Image and Vision Computing|
|Publication status||Published - 1993|