Illusory Contour Detection Using MRF Models

This paper presents a computational model for obtaining relative depth information from image contours. Local occlusion properties such as T-junctions and concavity are used to arrive at a global percept of distinct surfaces at various relative depths. A multi-layer representation is used to classify each image pixel into the appropriate depth plane based on the local information from the occluding contours. A Bayesian framework is used to incorporate the constraints defined by the contours and the prior constraints. A solution corresponding to the maximum posteriori probability is then determined, resulting in a depth assignment and surface assignment for each image site or pixel. The algorithm was tested on various contour images, including two classes of illusory surfaces: the Kanizsa and the the line termination illusory contours.