Subjective surfaces: A geometric model for boundary completion
We present a geometric model and a computational method 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, Boundary completion presents a considerable challenge in computer vision, since most algorithms attempt to exploit existing data. A large body of work concerns, completion models, which postulate how to construct missing data; these models are often trained and specific to particular images. In this paper, we take the following, alternative perspective: we consider a given reference point within the image, 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. Starting from this point of view, a surface is constructed. It is then evolved with the mean curvature flow in the metric induced by the image until a piecewise constant solution is reached. We test the computational model on modal completion, amodal completion, and texture segmentation. We extend the geometric model and the algorithm to 3D in order to extract shapes from low signal/noise ratio ultrasound image volumes. Results in 3D echocardiography and 3D fetal echography are also presented.
subjective surfaces segmentation perceptual contours level sets differential geometry riemannian geometry surface evolution