Subjective surfaces: A method for completing missing boundaries

We present a model and algorithm for segmentation of images with missing bo undaries. In many situations. the human visual system fills in missing gaps in edges and boundaries, building and completing information that is not p resent This presents a considerable challenge in computer vision, since mos t algorithms attempt to exploit existing data. Completion models, which pos tulate how to construct missing data, are popular but are often trained and specific to particular images. In this paper, we take the following perspe ctive: We consider a reference point within an image as given and then deve lop an algorithm that tries to build missing information on the basis of th e given point of view and the available information as boundary data to the algorithm. We test the algorithm on some standard images, including the cl assical triangle of Kanizsa and low signal:noise ratio medical images.