Subjective surfaces: A method for completing missing boundaries

A. Sarti, R. Malladi, J. A. Sethian
2000 Proceedings of the National Academy of Sciences of the United States of America  
We present a model and algorithm for segmentation of images with missing boundaries. In many situations, the human visual system fills in missing gaps in edges and boundaries, building and completing information that is not present. This presents a considerable challenge in computer vision, since most algorithms attempt to exploit existing data. Completion models, which postulate how to construct missing data, are popular, but are often trained and specific to particular images. In this paper,
more » ... e take the following perspective: we consider a reference point within an image as given, and then develop an algorithm which tries to build missing information on the basis of the given point of view and the available information as boundary data to the algorithm. We test the algorithm on some standard images, including the classical triangle of Kanizsa and low signal/noise ratio medical images.
doi:10.1073/pnas.110135797 pmid:10823902 pmcid:PMC18589 fatcat:jxlvafvxarerxfjjcohshto2yi