A Bayesian Network Framework for Real-Time Object Selection

Abstract

Image segmentation is essential in many computer vision and image understanding applications. We present a Bayesian network for object boundary detection in which the MPE (most probable explanation) before any evidence can produce multiple non-overlapping, non-self-intersecting closed contours and the MPE with evidence-where one or more connected boundary points are provided-produces a single non-self-intersecting, closed contour that accurately defines an object's boundary. We also present a near-linear-time algorithm that determines the MPE by computing the minimum-path spanning tree of a weighted, planar graph and finding the excluded edge that forms the most probable loop. This allows for real-time feedback within an interactive environment in which every mouse movement produces a recomputation of the MPE based on the new evidence and displays the corresponding closed loop. We call this interface "object highlighting" since object boundaries appear and disappear as the mouse cursor moves.